Classification models categorize objects into given classes, guided by training samples with input features and labels. In practice, however, labels can be corrupted by human error or mistakes, known as label noise, which degrades classification accuracy. To address this issue, recently, various works propose the algorithms to clean datasets with label noise. We categorize the algorithms in granular ways, and review the algorithms, such as sample selection, label correction, and select-and-correct algorithms, based on the categorization. In addition, we provide future research directions for cleaning datasets, considering practical challenges, such as class imbalance, class incremental learning, and corrupted input features.

Recent advancements in an unmanned aerial vehicle (UAV)-enabled network have demonstrated potential of a directional antenna to enhance network performance by utilizing limited resources more efficiently. In the UAV-enabled network where a directional antenna is utilized, controlling both its beam direction and beamwidth appropriately is an important issue in order to maximize its efficiency. Existing studies on the UAV-enabled network with a directional antenna, however, have primarily concentrated on adjusting antenna’s beamwidth with a fixed beam direction for simplicity. In this paper, we explore joint optimization of both beam direction and beamwidth of a UAV equipped with controllable directional antennas. To this end, we consider a UAV-enabled network where the UAV functions as an aerial base station (ABS), relaying data from a ground base station (GBS) to multiple ground users (GUs), aiming at maximizing the sum rate for all GUs by controlling the location, beam direction, and beamwidth of the UAV and resource allocation. To address this complex problem, we develop an algorithm called Joint optimization of location, beam direction, beamwidth, and resource allocation (Joint-LDWR). Through comprehensive simulations, we show the outstanding performance of Joint-LDWR, focusing on its efficiency for enhancing network performance. The results highlight a significant benefit of simultaneously controlling beam direction and beamwidth of the ABS together with its location in the UAV-enabled network.

In this paper, we study an unmanned aerial vehicle (UAV) relaying orthogonal frequency division multiple access (OFDMA) network with multiple user equipment (UE) pairs, each having explicitly given quality-of-service (QoS) requirements. Our goal is to maximize the system throughput while satisfying the QoS requirements for UE pairs, despite the air-to-ground (A2G) channel randomness. To this end, we develop a hybrid offline-online algorithm that designs the UAV’s 3D trajectory in an offline manner and then allocates sub-channels during the UAV flight in an online manner. Under the proposed algorithm, the UAV’s 3D trajectory is elaborately designed with statistical channel state information (CSI), and sub-channels are opportunistically allocated based on instantaneous CSI. In practice, the QoS requirements might not be guaranteed since perfect CSI cannot be obtained a priori. Nevertheless, the proposed algorithm significantly improves the QoS satisfaction of UE pairs to the fullest extent possible by leveraging available CSI. Through simulation, we validate the performance of the proposed algorithm in enhancing the QoS satisfaction of UE pairs and the system throughput.

Unmanned aerial vehicles (UAVs) have been utilized extensively in communication networks due to their versatility and maneuverability. It is also necessary to optimize their locations or trajectories. Moreover, it is important to adopt an appropriate air-to-ground (A2G) channel model that can reflect the practical environment well. In this paper, we first introduce the representative A2G channel models widely adopted in the literature and then review recent works on UAV placement and trajectory optimization from the perspective of A2G channel models. Finally, we present future research directions based on trends and lessons derived from our review of the existing studies.

Predictably, the upcoming six generation (6G) networks demand ultra-massive interconnectivity comprising densely congested sustainable small-to-tiny networks. The conventional radio access network (RAN) will be redesigned to provide the necessary intelligence in all areas to meet required network flexibility, full coverage, and massive access. In this respect, this paper focuses on intelligent massive RAN (mRAN) architecture and key technologies fulfilling the requirements. Particularly, we investigate potential artificial intelligence algorithms for network and resource management issues in 6G mRAN. Furthermore, we summarize the research issues in edge technologies and physical layer intelligence on 6G network architecture.

Network slicing is one of the most important enablers of an evolved network architecture for 5G networks that can support the challenging quality-of-service (QoS) requirements of future communication services. Network slicing is realized by technologies, such as software-defined networks, network function virtualization, and cloud radio access network (C-RAN) with packet-based fronthaul (P-FH) interfaces. However, it is still challenging to effectively support the diverse QoS requirements of network slices with limited FH link resources. To this end, FH payloads should be packetized in an appropriate way to apply transport differentiation provided in the P-FH interfaces since they are transported in a form of packets. In this article, we first comprehensively investigate the characteristics of the components of the FH payloads. Based on the investigation, we then design a packetization method that enables the P-FH interfaces to address the diverse QoS requirements by effectively applying their transport differentiation to the generated packets. Finally, we discuss open issues on realizing the P-FH interface in practice.

The use of unmanned aerial vehicles as aerial base stations (ABSs) can significantly enhance the capacity and coverage of wireless systems. In this paper, the problem of optimal ABS placement is studied while exploiting high-rise topographic features to maximize wireless coverage. In contrast to prior art that relies on simplified full line-of-sight (LoS) channel models or impractical probabilistic LoS channel models, this paper presents a novel feature-aware channel model that decisively discerns whether an air-to-ground (A2G) link is in LoS or non-LoS (NLoS) based on the topographical environment data for the target area. To resolve the challenges created by the dependence between the channel gain and the topographical environment in this feature-aware channel model, the LoS and NLoS zones in the target area are analyzed from a geometrical point of view. Then, based on the analysis results, the coverage area is derived in a tractable form and then used to develop a feature-aware ABS placement algorithm, called ABS-FA, based on particle swarm optimization (PSO). The effectiveness of the proposed approach is compared with two other baseline algorithms based on the full LoS and probabilistic LoS channel models, called ABS-LoS and ABS-Prob, respectively. Simulation results show that, depending on the topographical environment, ABS-LoS may outperform ABS-Prob, or vice versa, and even both may be very limited in some cases, because the full LoS and probabilistic LoS channel models cannot properly capture whether an A2G link is in LoS or NLoS. The results also show that the proposed ABS-FA scheme always outperforms these baseline algorithms and that, for instance, it can provide approximately 25% and 50% higher coverage performance compared to ABS-Prob and ABS-LoS, respectively. These results verify that considering a feature-aware channel model can be a very effective approach for determining the ABS location.

In this paper, we study a joint antenna and internet-of-things device (ID) scheduling problem in the full-duplex (FD) multiple-input multiple-output (MIMO) wireless powered communication network (WPCN) over time-varying fading channels. We first formulate an optimization problem to maximize the average sum rate of IDs while satisfying their minimum average data rate requirements by jointly scheduling ID selection for uplink data transmission, antenna switching, and beamforming. To deal with the problem, we propose a scheduling algorithm based on Lagrangian duality and the stochastic optimization theory. The proposed scheduling algorithm necessitates solving per-time-slot problems, each of which aims at maximizing the weighted sum of the selected ID’s uplink data rate and the non-selected IDs’ harvested power from the downlink by jointly optimizing ID selection, antenna switching between uplink and downlink, and beamforming at that time slot. To solve the per-time slot problem, we develop a joint ID selection, antenna switching, and beamforming (Joint-IAB) algorithm based on the block coordinate descent (BCD) and successive convex approximation (SCA) methods. Through simulation, we demonstrate that our scheduling algorithm with the proposed Joint-IAB algorithm provides better performance than the other scheduling algorithms while well satisfying the given minimum average data rate requirements of IDs.

In this paper, we study the waveform scheduling problem for a wireless power transfer (WPT) system consisting of a power beacon (PB) and multiple energy harvesting empowered Internet of Things (EH-IoT) devices. In each time slot, each device requests power to the PB if it needs power, and the PB transmits a WPT signal for which the waveform is designed based on the harvested power satisfaction rate of the power requesting devices. Under this setup, we formulate an optimization problem that maximizes the average number of EH-IoT devices whose power requests are satisfied. We first solve this problem assuming that the perfect channel state information (CSI) of all devices is known at the PB. Since the problem is difficult to solve even with perfect CSI, we transform it into a more tractable problem via proper approximations and propose an efficient algorithm to solve it. Next, to tackle the issue that it is practically difficult for the PB to acquire the perfect CSI of each device, we propose a contextual learning-based WPT waveform scheduling algorithm requiring only one-bit feedback from each device at one time. Numerical results show that our proposed waveform scheduling algorithm provides a higher satisfaction rate than existing algorithms under perfect CSI, and that with limited CSI feedback achieves performance close to the case with perfect CSI.

We propose a beamforming scheme called dynamic energy beamforming with a frequency diverse array (DEB-FDA) for enhancing the energy harvesting performance of multiple Internet of Things (IoT) devices in radio frequency-based wireless power transfer systems. We adopt the frequency diverse array (FDA) architecture, where the steering vector is a function of the distance and angle, to synthesize beam patterns that depend on both these parameters. We formulate an optimization problem for designing the transmit-beamforming vector and the frequency components of the FDA so that constructive interference occurs simultaneously at the locations of multiple IoT devices. We then develop an algorithm for dynamic energy beamforming based on alternating optimization using the minorize-maximization algorithm. Simulation results show that the proposed DEB-FDA scheme improves the energy harvesting performance of IoT devices in multiple locations.

In this paper, we study radio and energy resource management in renewable energy-powered wireless networks, where base stations (BSs) are powered by both on-grid and renewable energy sources and can share their harvested energy with each other. To efficiently manage those resources, we propose a hierarchical and distributed resource management framework based on deep reinforcement learning. The proposed framework minimizes the on-grid energy consumption while satisfying the data rate requirement of each user. It is composed of three different policies in a distributed and hierarchical way. An intercell interference coordination policy constrains the transmission power at each BS to coordinate the intercell interference among the BSs. Under the power constraints, a distributed radio resource allocation policy of each BS determines its own user scheduling and power control. Lastly, an energy sharing policy manages the energy resources of the BSs by sharing the harvested energy via power lines between them. Through the simulation, we demonstrate that the proposed framework can effectively reduce the on-grid energy consumption while satisfying the data rate requirements.

In this paper, we investigate dynamic resource scheduling (i.e., joint user, subchannel, and power scheduling) for downlink multi-channel non-orthogonal multiple access (MC-NOMA) systems over time-varying fading channels. Specifically, we address the weighted average sum rate maximization problem with quality-of-service (QoS) constraints. In particular, to facilitate fast resource scheduling, we focus on developing a very low-complexity algorithm. To this end, by leveraging Lagrangian duality and the stochastic optimization theory, we first develop an opportunistic MC-NOMA scheduling algorithm whereby the original problem is decomposed into a series of subproblems, one for each time slot. Accordingly, resource scheduling works in an online manner by solving one subproblem per time slot, making it more applicable to practical systems. Then, we further develop a heuristic joint subchannel assignment and power allocation (Joint-SAPA) algorithm with very low computational complexity, called Joint-SAPA-LCC, that solves each subproblem. Finally, through simulation, we show that our Joint-SAPA-LCC algorithm provides good performance comparable to the existing Joint-SAPA algorithms despite requiring much lower computational complexity. We also demonstrate that our opportunistic MC-NOMA scheduling algorithm in which the Joint-SAPA-LCC algorithm is embedded works well while satisfying given QoS requirements.

In this paper, we study asynchronous federated learning (FL) in a wireless distributed learning network (WDLN). To allow each edge device to use its local data more efficiently via asynchronous FL, transmission scheduling in the WDLN for asynchronous FL should be carefully determined considering system uncertainties, such as time-varying channel and stochastic data arrivals, and the scarce radio resources in the WDLN. To address this, we propose a metric, called an effectivity score, which represents the amount of learning from asynchronous FL. We then formulate an Asynchronous Learning-aware transmission Scheduling (ALS) problem to maximize the effectivity score and develop three ALS algorithms, called ALSA-PI, BALSA, and BALSA-PO, to solve it. If the statistical information about the uncertainties is known, the problem can be optimally and efficiently solved by ALSA-PI. Even if not, it can be still optimally solved by BALSA that learns the uncertainties based on a Bayesian approach using the state information reported from devices. BALSA-PO suboptimally solves the problem, but it addresses a more restrictive WDLN in practice, where the AP can observe a limited state information compared with the information used in BALSA. We show via simulations that the models trained by our ALS algorithms achieve performances close to that by an ideal benchmark and outperform those by other state-of-the-art baseline scheduling algorithms in terms of model accuracy, training loss, learning speed, and robustness of learning. These results demonstrate that the adaptive scheduling strategy in our ALS algorithms is effective to asynchronous FL.

Although intelligent reflecting surface (IRS) is regarded as a promising solution to enhance the efficiency of wireless energy transfer (WET), the acquiring of channel state information (CSI) is a crucial challenge for the system in which a training sequence for channel estimation is sent by low-power Internet of Things (IoT) devices. In this paper, an IRS-aided multi-device WET system is considered. To overcome the limitation in channel estimation, we propose a received power-based channel estimation scheme that can be easily implemented and scalable in wirelessly empowered IoT devices. Specifically, at every single time slot, each device measures the received power of a randomly generated radio frequency (RF) signal and feeds it back to the transmitter. We formulate a channel estimation problem to use the history of received power measurements based on the maximum likelihood estimation using the phase retrieval framework and temporal channel evolution model. Moreover, we propose an algorithm that can be employed to obtain the stationary solution for the channel estimation problem, which is based on the inexact block coordinate descent method. We also perform algorithm modification to deal with the special case in which the transmitter-IRS channel is available. The simulation results show that the performance of the proposed algorithm approaches the upper bound as the channel slowly changes, although the proposed channel estimation protocol requires only one scalar value feedback.

Massive multiple-input multiple-output (MIMO) technology, mainly equipped with dozens or even hundreds of antennas at transmitter and/or receiver, is one of the most important technologies in 5G/6G era due to its capability of achieving high transmission rate. However, since each transmit antenna typically needs a complete radio frequency (RF) chain, a large number of RF chains need to be installed accordingly, resulting in high economic cost, high hardware complexity, and high power consumption. To resolve these problems, architectures where a small number of RF chains are installed have been proposed in recent years, and an antenna selection technique that activates antennas only as many as the number of RF chains has been envisioned as one of solutions. In this paper, we study the joint antenna and user scheduling problem for the downlink massive MIMO system over time-varying fading channels to maximize the weighted average sum rate while ensuring users’ minimum average data rate requirements. To solve the problem, we first develop an opportunistic joint antenna and user scheduling algorithm (OJAUS) using the dual and stochastic subgradient methods, which makes it possible to schedule antennas and users without any underlying distributions of the fading channels. However, it requires solving a joint antenna and user selection (JAUS) problem to maximize the instantaneous weighted sum rate in every time slot. Thus, we additionally develop a simple heuristic JAUS algorithm with low computational complexity, called JAUS-LCC, which is executed in every time slot within OJAUS. Finally, through simulation results, we first show that our JAUS-LCC provides near-optimal performance despite requiring very low computational complexity, and then show that our OJAUS with JAUS-LCC well guarantees given minimum average data rate requirements.

In this article, we study a random access (RA) scheme to alleviate the RA channel (RACH) overload problem in the massive-machine-type communication (mMTC) environment. We first propose a timing advance-based preamble resource expansion (TAPRE) scheme which effectively increases preamble resources and reduces the preamble collision probability by adjusting preamble transmission timing with timing advance (TA) information. We also propose a resource allocation wait (RAW) scheme which efficiently reduces the number of RA failures due to the lack of physical uplink shared channel (PUSCH) resources. We then propose an overall procedure for enhanced RA with TAPRE and RAW (ERATAR). In addition, we provide the analysis of RA performance by applying more practical assumptions than the existing analysis. We validate our analysis with the system level simulation based on NS-3, and compare the various performances of our ERATAR to those of existing works. Numerical results show that our analysis provides more accurate results than the existing work and our ERATAR provides significantly improved performances compared with those of existing works.

In this paper, we study wireless power transfer (WPT) beam scheduling for a system which consists of IoT devices and a power beacon (PB) using switched beamforming. In such a system, the IoT devices have a non-static behavior (e.g., their location and power requests keep changing) in general, which conventional WPT beam scheduling algorithms are not capable of adaptively dealing with. To address the non-static behavior, we propose a procedure of deep neural network (DNN)-based WPT beam scheduling. In the procedure, the power-deficient IoT devices transmit a common pilot signal simultaneously. Then, the PB effectively provides power to them with a DNN-based WPT beam scheduling policy. In the DNN-based policy, an estimation of the non-static behavior from the received pilot signals and an adaptive beam generation considering the estimated non-static behavior are integrated thanks to the powerful representational capability of DNNs. To allow the DNN-based policy to learn the optimal policy, we propose a Deep WPT Beam scheduling policy Gradient (DWBG) algorithm using deep reinforcement learning. Through the simulation, we show that DWBG achieves a close performance to the optimal policy. This demonstrates that our algorithm can be applied for practical WPT IoT systems with non-static IoT devices.

In this paper, we study a waveform scheduling problem for a multi-receiver wireless power transfer (WPT) system considering time-varying channel conditions and minimum average output direct-current (DC) voltage requirement of each receiver. To this end, we formulate a stochastic optimization problem that aims at maximizing the average of the sum of output DC voltages of receivers while satisfying the minimum average output DC voltage requirements of all receivers, and by solving it, we develop a waveform scheduling algorithm. In the waveform scheduling algorithm, we need to solve a problem for maximizing the weighted-sum of output DC voltages of receivers, which is a non-convex optimization problem. To cope with this difficulty, we develop a low-complexity approximated algorithm with which the waveform for the multi-receiver WPT system is optimized to maximize the weighted sum of output DC voltages of receivers. Numerical results show that our waveform design algorithm provides the higher performance of the weighted-sum of the output DC voltages than the existing algorithms, and our opportunistic waveform scheduling provides good performance while well satisfying the minimum average output DC voltage requirement of each receiver.

In the conventional approaches using reinforcement learning (RL) for resource allocation in wireless networks, the structure of the policy depends on network circumstances such as the number of users and quality-of-service requirements. Due to this dependence, the policy is hard to be used in a practical system where the network circumstance is dynamically changing. To resolve this issue, we propose a circumstance-independent policy that can effectively address the different network circumstances even with a single policy. Thus, contrary to the conventional RL approaches, the proposed policy can be easily applied in the practical system. We then develop a deep RL algorithm to learn it. Through simulation results, we show that a single proposed policy can be used over different circumstances, and it achieves a close performance to the circumstance-dependent policy for each circumstance, which learns the optimal policy for the corresponding circumstance.

In this paper, we study distributed scheduling for a device-to-device (D2D) communication system with radio frequency energy harvesting (RF-EH) in which D2D devices can harvest energy using the ambient RF signals from other transmitting D2D devices. In the system, due to RF-EH, the effects of link scheduling for each D2D link on the system performance, i.e., average sum rate and average total harvested energy, become much more complicated compared with conventional D2D communication without RF-EH. To address such complicated effects in a distributed manner, we study a centralized optimal link scheduling algorithm, which aims at maximizing the weighted sum of average sum rate and average total harvested energy, while satisfying the quality of service (QoS) requirements of D2D links. We then abstract the scheduling principles in the optimal algorithm, and propose a distributed scheduling algorithm for D2D communication with RF-EH, EHLinQ, which realizes the principles in a distributed manner. Moreover, we extend EHLinQ to a power beacon (PB) aided D2D communication system in which a PB transmits RF signals dedicated for RF-EH. The extended EHLinQ allows the PB to determine its own PB scheduling considering the QoS satisfaction of D2D links. Through simulation results, we show that EHLinQ outperforms conventional algorithms, while closely meeting the QoS requirements.

In recent years, the emergence of flying ad hoc networks (FANETs) with multiple unmanned aerial vehicles (UAVs) can make it possible to effectively perform not only far-off missions but also assorted complex missions. In this paper, we consider a mission-critical FANET to perform given missions using multiple UAVs, taking into account a dynamic environment with a time-varying network topology. To effectively operate the mission-critical FANET, we study joint mission assignment and topology management problem aiming at maximizing the weighted sum of mission and network performances, while guaranteeing end-to-end communications between mission-performing UAVs and their corresponding ground control stations (GCSs), inter-UAV safety distance maintenance, and other mission-related constraints. To address this problem, we first develop three algorithms: One is to construct a mission-critical FANET from scratch, and the others are to manage the network topology and to switch UAV roles between mission-performing and data-relaying in response to the changes in the network topology. Then, we develop a dynamic mission-critical FANET operation algorithm incorporating the three algorithms with a few rules, by which the mission-critical FANET can be effectively managed and operated with reasonable computational complexity in the dynamic environment. Through simulation results, we show that our proposed algorithm works well in the dynamic environment while satisfying the constraints, and that its performance is not only superior to the existing algorithms, but also close to the optimal performance.

In this paper, we consider a cellular system, in which base stations (BSs) are powered by both on-grid and renewable energy sources. To efficiently utilize the harvested energy of the BSs, we study adaptive traffic management (TM) and energy cooperation (EC) that aim at minimizing the on-grid energy consumption, while guaranteeing minimum average throughputs. To achieve this, we develop an adaptive TM and EC algorithm that jointly decides the energy sharing among BSs, the user association to BSs, and the sub-channel and power allocation in BSs.Within the algorithm, a network scheduling problem, which is mixed-integer non-linear programming (MINLP), should be solved in each timeslot. To efficiently solve it, we develop a network scheduling algorithm applying generalized Benders decomposition (GBD) that optimally solves the MINLP problem. In addition, we also develop a heuristic network scheduling algorithm that has a much lower computational complexity than the GBD algorithm, while providing comparable performance. Through the numerical results, we show that our algorithms always outperform the algorithms that use only one of TM or EC regardless of the system conditions.

We propose a multi-tone amplitude modulation (MAM) scheme and its receiver architecture for simultaneous wireless information and power transfer (SWIPT). The MAM symbol consists of two-dimensional signaling of the amplitude component and the subcarrier-number component. The two components are decoded through the current intensity of rectifier output and peak to average power ratio (PAPR) value, respectively. Harvested energy and symbol error rate (SER) of proposed scheme are analyzed and evaluated for various bit assignments of two components. The simulation results show that proposed scheme achieves improved SER performance while achieving high energy harvesting.

In this paper, we consider IoT systems in which IoT devices request power to a power beacon (PB) when their available power is deficient and the PB provides power to the IoT devices using switched beamforming. We study WPT beam scheduling for the IoT systems under one-bit feedback which aims at maximizing the time-average number of the IoT devices whose power requests are satisfied. To achieve this, we propose a Contextual learning based WPT Beam scheduling algorithm with One-bit feedback (CWBO) that learns the channel information using only one-bit feedback information and exploits it for the beam scheduling. Within CWBO, a beam pattern generation (BPG) problem should be solved in each time-slot. To efficiently solve it, we develop a BPG algorithm based on monotonic optimization that can optimally solve the BPG problem. In addition, we also develop a heuristic BPG algorithm that has a lower computational complexity than the monotonic optimization-based BPG algorithm, while providing comparable performance. For CWBO in single-device WPT, we prove an analytical performance bound, which shows its optimality in terms of the long-term average performance even with one-bit feedback. In addition, through the simulation results, we show that our algorithms achieve performances close to that of the optimal beam scheduling policy in multi-device WPT as well. This demonstrates that our algorithms can be used for WPT IoT systems with IoT devices having only limited capabilities for feedback and estimation of the channel information due to their limited power.

We propose a receiver architecture and two modulation schemes for simultaneous wireless information and power transfer (SWIPT), in which two differently rectified signals are used for information decoding (ID) and energy harvesting (EH). The first scheme is called amplitude difference shift keying (ADSK), which gives a low bit error rate (BER) by transferring information through the amplitude difference between the two rectified signals. The second is called amplitude ratio shift keying (ARSK), which offers considerable harvested energy by transferring information through the amplitude ratio between the two rectified signals. We demonstrate how they work and provide their performance through simulations.

In this paper, we consider IoT systems that can be applied to various applications with low-mobility or static IoT devices such as wireless sensor networks and charging systems for low-power devices with communication. The IoT systems consist of IoT devices and a hybrid access point (H-AP) powered by both on-grid and renewable energy sources. The IoT devices have a capability to harvest energy from the H-AP's RF signal, and they perform their tasks by using only their harvested energy. We consider the tasks do not have a real-time requirement which can be stored in the task queues of the IoT devices and performed later. We study resource and task scheduling for the IoT systems which aims at minimizing the on-grid energy consumption at the H-AP while guaranteeing the minimum average data rate and minimum task performing rates of IoT devices. To achieve the goal, we first propose a centralized resource and task scheduling algorithm. However, its computational complexity and signaling overhead are too large due to the task scheduling for each IoT device. Thus, to resolve these issues, we propose a hybrid resource and task scheduling algorithm in which each IoT device determines its own task scheduling in a distributed manner and the H-AP determines the resource scheduling. We then provide performance analyses showing that our proposed algorithms are asymptotically optimal and well satisfy the QoS requirements of IoT devices even with distributed task scheduling. Through the simulation results, we verify the analyses and show the performance of our algorithms.

This letter considers simultaneous wireless information and power transfer (SWIPT) in multiple-input single-output (MISO) broadcasting systems based on the Integer Forcing (IF) technique where multiple receivers are equipped with a power splitting circuit and able to harvest energy and decode information at the same time. The optimization problem that incorporates the beamforming matrix and IF coefficients matrix designs is formulated to minimize the transmit power at transmitter, while guaranteeing the achievable rate and harvested energy threshold of each receiver. To tackle the NP-hardness and non-convexity of the optimization problem, we propose an iterative algorithm consisting of two sub-problems. Simulation results provide the comparison of the performance of SWIPT systems with and without IF technique.

In this paper, we study the waveform design in the wireless power transfer (WPT) system with multiple receivers. In the multi-receiver WPT system, due to the severe power attenuation of RF signals according to the distance and the heterogeneity in the energy harvesting capability, there exists severe unfairness in energy harvesting among receivers at different distances from the transmitter and/or different energy harvesting capabilities. Hence, alleviating unfairness in energy harvesting among receivers is one of the critical challenges in the multi-receiver WPT system. To tackle this challenge in a systematic way, we consider the fairness in our waveform design applying the α-proportional fairness. With the analysis of the rectenna circuit, we derive the output dc voltage and power of the rectifier in closed forms. Thereby, we formulate an optimization problem to design the waveform for fair WPT. The problem is shown to be a non-convex optimization problem, which is hard to solve in general. However, with proper approximations, we convert it into a convex optimization problem that can be solved easily and obtain the waveform for fair WPT. Numerical results show that our designed waveform makes receivers harvest energy fairly and can control the degree of the fairness easily.

Flying ad hoc networks (FANETs) that consist of multiple unmanned aerial vehicles (UAVs) are promising technologies for future networked systems due to the versatility of UAVs. One of the most distinguishing features of FANET is frequent and rapid topological fluctuations due to the high-mobility of UAVs. Hence, the topology management adapting to the movements of UAVs is one of the most critical issues in FANET. In this paper, we study a FANET topology management problem that optimizes the locations and movements of UAVs to maximize the network performance, adapting to the topological changes while UAVs carry out their missions. When formulating the problem, we take into account the routing protocol as an arbitrary function since the network performance is inseparably linked with the routing protocol in use. We first develop two algorithms. One is the topology construction algorithm, which constructs a FANET topology from the scratch without any given initial topology, based on particle swarm optimization. The other is the topology adjustment algorithm, which incrementally adjusts the FANET topology adapting to the movements of UAVs with low-computational costs, based on gradient descent. Then, by defining a logical distance (the so-called topology edit distance) that measures the degree of changes in FANET topology, we develop an integrated topology management algorithm that contains the topology construction and adjustment algorithms. The simulation results show that our algorithm achieves a good network performance with low computational overhead, which is one of the most essential virtues in FANETs with rapidly varying topology.

In this paper, we study a random access (RA) procedure to support the massive connectivity of the Internet of Things (IoT) devices, also known as the IoT connectivity. Compared with the previous RA procedures that have limitations to support the IoT connectivity due to the exponentially increased access delay, we develop an RA procedure by applying the sparse code multiple access (SCMA) to reduce the access delay and increase the ratio of the IoT devices that successfully complete their RA procedures. We provide the theoretical performance analysis of the proposed RA procedure with the performance metrics such as the RA success probability, the average access delay, the RA throughput, and the average number of preamble transmissions. Then, we provide the numerical results to evaluate the performance of the proposed RA procedure based on our analysis and the ns-3 simulator. Numerical results show that our proposed RA procedure is able to support the massive connectivity requirement with improved RA performance metrics compared with the conventional RA procedures.

In this paper, we study a unit commitment (UC) problem where the goal is to minimize the operating costs of a microgrid that involves renewable energy sources. Since traditional UC algorithms use a priori information about uncertainties such as the load demand and the renewable power outputs, their performances highly depend on the accuracy of the a priori information, especially in microgrids due to their limited scale and size. This makes the algorithms impractical in settings where the past data is not sufficient to construct an accurate prior of the uncertainties. To resolve this issue, we develop an adaptively partitioned contextual learning algorithm for UC (APCLUC) that learns the best UC schedule and minimizes the total cost over time in an online manner without requiring any a priori information. AP-CLUC effectively learns the effects of the uncertainties on the cost by adaptively considering context information strongly correlated with the uncertainties, such as the past load demand and weather condition. For AP-CLUC, we first prove an analytical bound on the performance, which shows that its average total cost converges to that of the optimal policy with perfect a priori information. Then, we show via simulations that AP-CLUC achieves competitive performance with respect to the traditional UC algorithms with perfect a priori information, and it achieves better performance than them even with small errors on the information. These results demonstrate the effectiveness of utilizing the context information and the adaptive management of the past data for the UC problem.

In this paper, we study a multi-residential electricity load scheduling problem with multi-class appliances in smart grid. Compared with the previous works in which only limited types of appliances are considered or only single residence grids are considered, we model the grid system more practically with jointly considering multi-residence and multi-class appliance. We formulate an optimization problem to maximize the sum of the overall satisfaction levels of residences which is defined as the sum of utilities of the residential customers minus the total cost for energy consumption. Then, we provide an electricity load scheduling algorithm by using a PL-Generalized Benders Algorithm which operates in a distributed manner while protecting the private information of the residences. By applying the algorithm, we can obtain the near-optimal load scheduling for each residence, which is shown to be very close to the optimal scheduling, and also obtain the lower and upper bounds on the optimal sum of the overall satisfaction levels of all residences, which are shown to be very tight.

In this paper, we consider a heterogeneous mobile cloud computing (HMCC) system that consists of remote cloud servers, local cloudlets, task offloading mobile devices (TMDs), non-task offloading MDs (NTMDs), and radio access networks such as cellular networks and WLANs. TMDs have the capability of task offloading to remote cloud servers or cloudlets, whereas NTMDs are conventional cellular users that do not have such capability. By using stochastic geometry, we analyze the outage probability of task offloading in the MCC system with only remote cloud servers and that in the HMCC with both remote cloud servers and cloudlets. The analysis provides useful information, i.e., how the varying system parameters affect the outage probability. From the analysis, we show that there is an intrinsic limitation in reducing the outage probability in the MCC system due to the outage when accessing remote cloud servers. In addition, we show that the use of cloudlets is a promising solution to overcome this limitation. However, a tradeoff exists in using cloudlets due to their deployment and operation costs. Thus, to address this tradeoff, we also study the optimal cloudlet deployment to maximize the cloud service provider’s profit while guaranteeing maximum outage probability requirements.

As the Internet of Things (IoT) revolution presents an enormous opportunity for all industry verticals ranging from startups to large enterprises to create new types of services, standard bodies and global alliances have been working on establishing common standards for IoT systems. The oneM2M is the global partnership developing standards for Machine-to-Machine (M2M) communications and the Internet of Things. It develops technical specifications for the globally-applicable, interoperable common M2M/IoT service layer platforms, which play a pivotal role in building the ecosystem driven by key players, including developers and consumers. In this paper, we analyze the oneM2M standards, and introduce Mobius and &Cube, which are oneM2M-compliant M2M/IoT software platforms for servers and devices, respectively. We also present four pilot services using the platforms and several prototype IoT devices. Finally, we discuss three aspects, advanced discovery, open API, and peer-to-peer that are required for the oneM2M to build IoT ecosystem by attracting developers and consumers into the emerging IoT ecosystem.

Radio frequency energy transfer (RET) has been proposed as a promising solution to power sensor nodes in wireless sensor networks (WSNs). However, RET has a significant drawback to be directly applied to WSNs, i.e., unfairness in the achieved throughput among sensor nodes due to the difference of their energy harvesting rates that strongly depend on the distance between the energy emitting node and the energy harvesting nodes. The unfairness problem should be properly taken into account to mitigate the drawback caused from the features of RET. To resolve this issue, in this paper, we propose a medium access control (MAC) protocol for WSNs based on RET with two distinguishing features: energy adaptive (EA) duty cycle management that adaptively manages the duty cycle of sensor nodes according to their energy harvesting rates and EA contention algorithm that adaptively manages contentions among sensor nodes considering fairness. Through analysis and simulation, we show that our MAC protocol works well under the RET environment. Finally, to show the feasibility of WSNs with RET, we test our MAC protocol with a prototype system in a real environment.

We study a distributed link scheduling problem for device-to-device (D2D) communication considering the quality-of-service (QoS) requirements and time-varying channel conditions of D2D links. To this end, we first study an optimal centralized link scheduling problem maximizing the total average sum-rate while satisfying the QoS requirements of D2D links. We then abstract the important scheduling principles of the optimal link scheduling, i.e., giving more chance to be scheduled to the links which have a good channel condition and do not satisfy the QoS requirement, in order to utilize them to develop distributed link scheduling algorithms. With the scheduling principles, we develop a procedure with which D2D links can share their degree of QoS unsatisfaction and channel condition with each other and generate their scheduling priorities according to the shared information in a distributed manner. We also develop a novel distributed link scheduling criterion with which D2D links determine their link scheduling. By using them, we propose distributed link scheduling algorithms, QCLinQ and QC²LinQ, which have significantly smaller signaling overhead and low computational complexity compared with the centralized optimal link scheduling algorithm. Moreover, they closely meet the QoS requirements of D2D links while achieving significant sum-rate improvement over conventional distributed algorithms.

The wireless mesh network (WMN) has attracted significant interests as a broadband wireless network to provide ubiquitous wireless access for broadband services. Especially with incorporating multiple orthogonal channels and multiple directional antennas into the WMN, each node can communicate with its neighbor nodes simultaneously without interference between them. However, as we allow more freedom, we need a more sophisticated algorithm to fully utilize it and developing such an algorithm is not easy in general. In this paper, we study a joint channel assignment, link scheduling, routing, and rate control problem for the WMN with multiple orthogonal channels and multiple directional antennas. This problem is inherently hard to solve, since the problem is formulated as a mixed integer nonlinear problem (MINLP). However, despite of its inherent difficulty, we develop an algorithm to solve the problem by using the generalized Benders decomposition approach [2]. The simulation results show the proposed algorithm provides the optimal solution to maximize the network utility, which is defined as the sum of utilities of all sessions.

In this paper, we study a dynamic pricing and energy consumption scheduling problem in the microgrid where the service provider acts as a broker between the utility company and customers by purchasing electric energy from the utility company and selling it to the customers. For the service provider, even though dynamic pricing is an efficient tool to manage the microgrid, the implementation of dynamic pricing is highly challenging due to the lack of the customer-side information and the various types of uncertainties in the microgrid. Similarly, the customers also face challenges in scheduling their energy consumption due to the uncertainty of the retail electricity price. In order to overcome the challenges of implementing dynamic pricing and energy consumption scheduling, we develop reinforcement learning algorithms that allow each of the service provider and the customers to learn its strategy without a priori information about the microgrid. Through numerical results, we show that the proposed reinforcement learning-based dynamic pricing algorithm can effectively work without a priori information about the system dynamics and the proposed energy consumption scheduling algorithm further reduces the system cost thanks to the learning capability of each customer.

In this paper, we study an electricity load scheduling problem in a residence. Compared with previous works in which only limited sets of appliances are considered, we classify various appliances into five sets considering their different energy consumption and operation characteristics, and provide mathematical models for them. With these appliance models, we propose an electricity load scheduling algorithm that controls the operation time and energy consumption level of each appliance adapting to time-of-use pricing in order to maximize the overall net utility of the residence while satisfying its budget limit. The optimization problem is formulated as a mixed integer nonlinear programming (MINLP) problem, which is in general, difficult to solve. In order to solve the problem, we use the generalized Benders decomposition approach with which we can solve the MINLP problem easily with low computational complexity. By solving the problem, we provide an algorithm to obtain the optimal electricity load scheduling of various appliances with different energy consumption and operation characteristics in a unified way.

The device-to-device (D2D) relaying is considered one of promising technologies to improve the spectral efficiency and extend the coverage of the cellular system with low additional costs. In the system with D2D relaying, some of user equipments (UEs) can act as relay stations (RSs) that forward other UEs' data from/to the base station (BS). Compared with the RS, the D2D relaying has several advantages such as low deployment costs and high flexibility. We study an opportunistic subchannel scheduling problem in the OFDMA cellular network with D2D relaying in this paper. We formulate a stochastic optimization problem to maximize the sum-rate of the system with D2D relaying while satisfying the minimum average data rate requirement for each UE, and then develop an opportunistic scheduling algorithm by solving it. Due to a high computational complexity of the optimal scheduling algorithm, we also propose a heuristic algorithm with a lower computational complexity. In addition, since UEs that participate in D2D relaying sacrifice their resources to relay other UEs' data, we also study incentive mechanisms to compensate their sacrifices. Through simulation results, we show the performance of our algorithms and the effects of our incentive mechanisms.

Recently, joint subchannel allocation and (transmission) power control problems for multicell orthogonal frequencydivision multiple access (OFDMA) systems have been actively studied. However, since the problems are notoriously difficult and complex, only heuristic approaches are mainly used to study them, instead of the optimal approach for achieving maximum system capacity. In this paper, we study this problem from the viewpoint of optimal subchannel allocation and power control, aiming at maximizing the sum rate of the multicellOFDMA system. By using a monotonic optimization approach, we develop an algorithm for the optimal subchannel allocation and power control that achieves the maximum sum rate of the system. In addition, we also develop an algorithm that provides both upper and lower bounds on the maximum sum rate of the system with lower computational complexity. To evaluate the tightness of the upper and lower bounds, we also study the conditions when the two bounds are close to each other so that they can be good approximations to the maximum sum rate of the system. Through numerical results, we show that the bounds provide good approximations to the maximum sum rate of the multicell OFDMA system in most cases.

In this paper, we study a resource scheduling problem for D2D communication in OFDMA cellular networks. In addition to opportunistically scheduling power and subchannels to users as in the conventional OFDMA network, we also consider two distinguishing features for D2D communication to fully utilize the advantages of D2D communication: transmission mode selection and channel reuse. First, we allow each D2D user to opportunistically select its transmission mode between direct one-hop transmission and indirect two-hop transmission through the BS. Second, we allow channel reuse within a cell such that users can share the same subchannel within a cell. We formulate an optimization problem that aims at maximizing the average weighted sum-rate of users, while satisfying the QoS requirement of each user. Our problem is a non-convex and coupled optimization problem, which is inherently difficult to solve, especially due to channel reuse within a cell. Despite of it, we develop an optimal opportunistic resource scheduling algorithm that solves the problem. In addition, we also propose two simple heuristic algorithms to reduce the computational complexity of the optimal algorithm. Through simulations, we show our opportunistic scheduling algorithms achieve a high performance with appropriately utilizing the advantages of D2D communication.

In this paper, we study a node placement problem in mobile ad hoc networks with controllable mobility. Especially, we consider mission-critical networks in which nodes have their own specific mission whose degree of satisfaction depends on their locations. In addition to accomplish their mission, nodes want to maintain a good communication quality with their neighbor nodes that also depends on their locations. In general, the best location of a node for its mission is not coincident with that for its communication quality, and thus it is important to control the mobility of a node to find its appropriate location jointly considering both its mission and communication quality. Hence, in this paper, we study a joint mission and communication aware node placement problem. We formulate the problem as a potential game and develop a distributed algorithm that converges to the Nash equilibrium. In addition, we also show that if some minor conditions are satisfied, our algorithm provides a global optimal solution that minimizes the weighted sum of costs for mission and communication.

In femtocell networks, due to their small cell size, we can achieve higher spatial diversity from the channel reuse between multiple femtocells. In addition, if femtocells operate on OFDMA systems, each subchannel can be reused separately among femtocells, improving system efficiency more significantly. However, due to a large number of femtocells and their uncoordinated and irregular deployment, we need to treat intercell interferences very carefully in OFDMA-based femtocell networks, which makes developing efficient resource allocation schemes more difficult.

In this paper, we study a subchannel allocation problem that aims at maximizing the sum utility of the OFDMA-based femtocell network, which can be applied to both the dedicated channel and shared channel femtocell deployments. Since the problem is formulated as a nonlinear integer program, which is inherently difficult to solve, we propose a suboptimal subchannel allocation algorithm. The proposed subchannel allocation algorithm consists of two steps: calculating the number of subchannels that should be granted to each cell to maximize the sum utility and finding actual subchannel allocation that achieves the granted number of subchannels for each cell. Numerical results show that the proposed subchannel allocation algorithm provides near-optimal performance.

Electric vehicles (EVs) will play an important role in the future smart grid because of their capabilities of storing electrical energy in their batteries during off-peak hours and supplying the stored energy to the power grid during peak hours. In this paper, we consider a power system with an aggregator and multiple customers with EVs and propose novel electricity load scheduling algorithms which, unlike previous works, jointly consider the load scheduling for appliances and the energy trading using EVs. Specifically, we allow customers to determine how much energy to purchase from or to sell to the aggregator while taking into consideration the load demands of their residential appliances and the associated electricity bill. We propose two different approaches: a collaborative and a non-collaborative approach. In the collaborative approach, we develop an optimal distributed load scheduling algorithm that maximizes the social welfare of the power system. In the noncollaborative approach, we model the energy scheduling problem as a non-cooperative game among self-interested customers, where each customer determines its own load scheduling and energy trading to maximize its own profit. In order to resolve the unfairness between heavy and light customers in the noncollaborative approach, we propose a tiered billing scheme that can control the electricity rates for customers according to their different energy consumption levels. In both approaches, we also consider the uncertainty in the load demands, with which customers' actual energy consumption may vary from the scheduled energy consumption. To study the impact of the uncertainty, we use the worst-case-uncertainty approach and develop distributed load scheduling algorithms that provide the guaranteed minimum performances in uncertain environments. Subsequently, we show when energy trading leads to an increase in the social welfare and we determine what are the customers' incentives to participate in the energy trading in various usage scenarios including practical environments with uncertain load demands.

In this paper, we study a flow control problem considering network coding in wireless ad-hoc networks with multi-path routing. As a network coding scheme, we use XOR network coding, in which each node bitwise-XORs some packets received from different sessions, and then broadcasts this coded packet to multiple nodes in a single transmission. This process can reduce the number of required transmissions, and thus can improve network utilization, especially if it is used with appropriate network coding-aware protocols. Considering this XOR network coding, we formulate an optimization problem for flow control that aims at maximizing network utility. By solving the optimization problem in a distributed manner, we implement a distributed flow control algorithm that provides the optimal transmitting rate on each of multiple paths of each session. The simulation results show that our flow control algorithm performs well exploiting the advantages of network coding and provides significant performance improvement.

In our previous work [2], we proposed a new concept of utility functions for rate control in communication networks. Unlike conventional utility-based rate control in which the utility function of each user is defined as a function of its transmitting data rate, in [2], we defined the utility function of each user as a function of not only its transmitting data rate but also it receiving data rate. The former is called a session-level utility function and the latter is called a user-level utility function. The user-level utility function reflects the satisfaction with the service of a user with two-way communication, which consists of transmitting and receiving sessions, better than the session-level utility function, since user's satisfaction depends on not only the satisfaction with its transmitting session but also that for its receiving session. In [2], an algorithm that required each user to know the exact utility function of its correspondent was developed. However, in some cases, this information might not be available due to some reasons such as security and privacy issues, and in such cases, the algorithm developed in [2] cannot be used. Hence, in this paper, we develop a new distributed algorithm that does not require each user to know the utility function of its correspondent. Numerical results show that our new algorithm, which does not require the utility information of the correspondent, converges to the same solution to that with the algorithm that requires the utility information of the correspondent.

In this paper, we study a cross-layer and end-to-end optimization problem for the integrated wireless and wireline network that consists of one wireline core network and multiple wireless access networks. We consider joint end-to-end flow control/distribution at the transport and network layers and opportunistic scheduling at the data link and physical layers. We formulate a single stochastic optimization problem and solve it by using a dual approach and a stochastic sub-gradient algorithm. The developed algorithm can be implemented in a distributed way, vertically among communication layers and horizontally among all entities in the network, clearly showing what should be done at each layer and each entity and what parameters should be exchanged between layers and between entities. Numerical results show that our crosslayer and end-to-end optimization approach provides more efficient resource allocation than the conventional layered and separated optimization approach.

The cooperative transmission, in which some nodes help the transmission of other nodes, has been actively studied to overcome the channel fading effects that deteriorate the communication quality. Thus far, most researches on the cooperative transmission have been studied from the reliability point of view focusing on a single transmission and showing that the cooperative transmission can increase transmission reliability. In this paper, we study the effects of the cooperative transmission from the system throughput and fairness point of view, considering the following fundamental questions: Is the cooperative transmission always helpful to increase the system throughput and improve the degree of fairness among nodes? If not, when is it helpful to increase the system throughput and improve the degree of fairness among nodes? We provide the answers to the above questions with a simple system in which two source nodes are capable of being cooperative with each other by using the decode-and-forward cooperative scheme to transmit their data to a single destination node.

In this paper, we study an opportunistic scheduling problem in an OFDMA system, in which sub-carriers of the system are allocated to each user in each time slot considering the time-varying channel condition and QoS requirement of each user. We consider two different classes of services that are represented with different types of utility functions. The utility function for a user in one class is defined as a function of its average data rate, which can be applicable to best-effort services and the utility function for a user in the other class is defined as a function of its instantaneous data rate, which can be applicable to rate-sensitive services. Those two types of utility functions have been extensively considered in opportunistic scheduling in wireless networks. However, in most of the previous work, they are considered separately in different problems. In this paper, we formulate a stochastic optimization problem that can treat those two types of utility functions in a single problem, which enables us to implement an opportunistic scheduling algorithm that can consider those two classes of services in a single system in a unified way. Through simulations, we first show that our algorithm provides a good approximation to the optimal solution. In addition, we also verify the appropriateness of our utility models.

In this paper, we propose an algorithm for joint relay node placement and node scheduling in wireless networks.We consider a system that consists of a relay node with controllable mobility and multiple nodes that communicate with each other via the relay node. Each node communicates with the relay node according to its schedule. The objective of our algorithm is to maximize the lowest weighted throughput among those of all nodes. To this end, we consider a problem to optimally place the relay node and optimally schedule all nodes in the network.We develop three algorithms for relay node placement and node scheduling considering three different cases: when the location of the relay node is controllable while the node scheduling is fixed, when the node scheduling is controllable while the location of the relay node is fixed, and when both the location of the relay node and node scheduling are controllable. The simulation results show that by jointly optimizing relay node placement and node scheduling, we can improve system performance significantly in many cases.

In this paper, we study a utility based flow control problem for a communication network. In most previous works on utility based flow control, the utility function of each user, which represents its satisfaction to the allocated data rate, is assumed to be fixed. This implies that the degree of the rate requirement of each user is assumed to be fixed over the entire duration of its session. However, in communication networks, many services are variable rate services, i.e., the degree of their rate requirement varies over time, which cannot be modeled with traditional static utility functions. To resolve this issue and appropriately model services with variable rate requirements, we propose a stochastic utility function that varies stochastically according to the variation of the degree of the rate requirement of a service. We formulate a flow control problem as a stochastic optimization problem with stochastic utility functions that aims at maximizing the average network utility while satisfying the constraint on link capacity and QoS requirement. By solving the stochastic optimization problem, we develop a distributed flow control algorithm that converges to the optimal rate allocation.

In this paper, we study an opportunistic resource scheduling problem for the relay-based OFDMA cellular network where relay stations (RSs) perform opportunistic network coding with downlink and uplink sessions of a mobile station (MS). To this end, we consider time-division duplexing (TDD) where each time-slot is divided into three phases according to the type of transmitter nodes, i.e., the base station (BS), MSs, and RSs. Moreover, to improve the flexibility for resource allocation, we allow dynamic TDD, in which the time duration of each phase in each time-slot can be adjusted. For opportunistic network coding, we introduce a novel model for network coding aware RSs with which an opportunistic network coding problem can be reduced to an opportunistic subchannel scheduling problem. We formulate an optimization problem that aims at maximizing the average weighted-sum rate for both downlink and uplink sessions of all MSs, while satisfying the quality-of-service (QoS) requirements of each MS. By solving it, we develop a resource scheduling algorithm that optimally and opportunistically schedule subchannel, transmission power, network coding, and time duration of each phase in each time-slot. Through the numerical results, we study how each of network coding strategy and dynamic TDD affects the network performance with various network environments.

In this paper, we study an end-to-end rate control problem in communication networks with the network utility maximization (NUM) framework. In many cases, a communication of a user consists of two sessions: transmitting and receiving sessions, and its overall satisfaction to the communication service depends on the satisfaction to both sessions. However, in most previous approaches with the NUM framework, the utility function of a user, which represents its satisfaction to the service, is modeled considering only the satisfaction to its transmitting session through its transmitting rate. The receiving session of a user is treated independently and indirectly through the utility function of its correspondent. Hence, it is not possible to jointly consider the satisfaction of transmitting and receiving sessions of one user. To resolve this issue, in this paper, we first propose a new concept of the utility function, which is called a user-level utility function. The user-level utility function is modeled considering both transmitting and receiving sessions of a user. We then formulate an optimization problem for end-to-end rate control with user-level utility functions. Even though two users in a communication are coupled with each other through their utility functions, we developed a distributed algorithm with appropriate message exchanges. The numerical results show that our user-level utility function and algorithm can control the transmitting rate of each user more appropriately according to the type of its communication service.

In this paper, we study an end-to-end rate control problem in communication networks with the network utility maximization (NUM) framework. In many cases, a communication of a user consists of two sessions: transmitting and receiving sessions, and its overall satisfaction to the communication service depends on the satisfaction to both sessions. However, in most previous approaches with the NUM framework, the utility function of a user, which represents its satisfaction to the service, is modeled considering only the satisfaction to its transmitting session through its transmitting rate. The receiving session of a user is treated independently and indirectly through the utility function of its correspondent. Hence, it is not possible to jointly consider the satisfaction of transmitting and receiving sessions of one user. To resolve this issue, in this paper, we first propose a new concept of the utility function, which is called a user-level utility function. The user-level utility function is modeled considering both transmitting and receiving sessions of a user. We then formulate an optimization problem for end-to-end rate control with user-level utility functions. Even though two users in a communication are coupled with each other through their utility functions, we developed a distributed algorithm with appropriate message exchanges. The numerical results show that our user-level utility function and algorithm can control the transmitting rate of each user more appropriately according to the type of its communication service.

In this paper, we study a communication-aware position control problem for mobile nodes in vehicular networks, in which the positions of some nodes can be controlled considering the network performance. We model the average achievable data rate of a link between two nodes as a function of the distance between the two nodes, i.e., as a function of positions of the two nodes. We then try to find the positions of some nodes whose positions can be controlled so as to maximize the minimum weighted average data rate among those of all links in the network. To tackle this problem, we take two approaches: optimization and game theoretic approaches. In the optimization theoretic approach, even though the optimization problem is formulated as non-convex optimization, we can develop algorithms for the optimal solution. However, since those algorithms are centralized algorithms, which may not be applicable to some cases such as vehicular ad-hoc networks (VANETs), we also use the game theoretic approach to develop distributed algorithms. In addition to developing algorithms, we also analyze and compare the performances of our algorithms, showing that the game theoretic approach could provide not only distributed algorithms but also efficient algorithms in our problem.

In this paper, we propose a unified framework for opportunistic fair scheduling in wireless systems. We consider a TDMA type of multiple access scheme, in which only one user can be scheduled in each time-slot. For opportunistic fair scheduling in such a system, some nice frameworks have been developed in the previous works, such as Agrawal and Subramanian (Allerton conference on communication, control and computing, 2002), Liu et al. (IEEE Journal of Selected Areas in Communications 19(10): 2053–2065, 2001) and Liu et al. (Computer Networks 41(4): 451–474, 2003). However, in this paper, we consider a more general problem that can accommodate more general types of fairness, and more general types of utility functions than those in the previous works. In addition to those generalizations, we develop a new framework for opportunistic fair scheduling based on the duality theory, which is different from those in the previous works. The duality theory is a well-defined theory in the mathematical optimization area. Hence, it can provide a unified framework for many different types of problems. In fact, we show that two different frameworks in Agrawal and Subramanian (Allerton conference on communication, control and computing, 2002), Liu et al. (IEEE Journal of Selected Areas in Communications 19(10): 2053–2065, 2001) and Liu et al. (Computer Networks 41(4): 451–474, 2003) are special cases of ours. In addition, by using the unified framework developed in this paper, we can not only develop various opportunistic fair scheduling schemes but also analyze the developed algorithm more rigorously and systematically.

In this paper, we study an opportunistic scheduling scheme for the TDMA wireless network with relay stations. We model the time-varying channel condition of a wireless link as a stochastic process. Based on this model, we formulate an optimization problem for the opportunistic scheduling scheme that maximizes the expected system throughput while satisfying the QoS constraint of each user. In the opportunistic scheduling scheme for the system without relay stations, each user has only one communication path between the base station and itself, and thus only user selection is considered. However, in our opportunistic scheduling scheme for the system with relay stations, since there may exist multiple paths between the base station and a user, not only user selection but also path selection for the scheduled user is considered. In addition, we also propose an opportunistic time-sharing method for time-slot sharing between base station and relay stations. With the opportunistic time-sharing method, our opportunistic scheduling provides opportunistic resource sharing in three places in the system: user selection in a time-slot, path selection for the selected user, and time-slot sharing between base station and relay stations. Simulation results show that as the number of places that opportunistic resource sharing is applied to increases, the performance improvement also increases.

In this paper, we study a joint opportunistic subchannel and power-scheduling problem in relay-based orthogonal frequency-division multiple-access (OFDMA) networks. In most previous works on relay-based networks, immediate relaying at relay stations (RSs) without allowing opportunistic scheduling at RSs was considered. Under this strategy, each RS should transmit the received data from the base station (BS) to the corresponding mobile stations (MSs) immediately within a single time slot, and thus, the effective data rate of the two-hop transmission (BS–RS and RS–MS links) is limited by the achievable data rate of the link with a worse channel state between the two links, resulting in a waste of radio resources. However, if opportunistic scheduling is allowed not only at the BS but at each RS as well, then more efficient radio resource allocation could be possible. Considering time-varying wireless channels, we formulate a stochastic optimization problem that aims at maximizing the average sum rate of the system while satisfying the quality-of-service (QoS) requirement of each MS. By solving the problem, we develop a joint opportunistic subchannel and power-scheduling algorithm for transmission at both the BS and the RSs. Numerical results show that the proposed scheduling algorithm can significantly improve system performance by allowing opportunistic scheduling at both the BS and the RSs.

To reduce energy consumption, in most MAC protocols for wireless sensor networks, listen-sleep cycles are adopted. However, even though it is a good solution for energy efficiency, it may introduce a large end-to-end delay due to sleep delay, since a node with a packet to transmit should wait until the next-hop node of the packet awakes. To resolve this issue, in this paper, we propose the Average Velocity-Based Routing (AVR) protocol for wireless sensor networks that aims at reducing the end-toend delay. The AVR protocol is a kind of a geographic routing protocol that considers both location of a node and waiting time of a packet at the MAC layer. When a node can use information of n-hop away neighbor nodes, it calculates the n-hop average velocity for each of its one-hop neighbor nodes and forwards a packet to the neighbor node that has the highest n-hop average velocity. Simulation results show that as the knowledge range, n, increases, the average end-to-end delay decreases.

To reduce energy consumption, in most MAC protocols for wireless sensor networks, listen-sleep cycles are adopted. However, even though it is a good solution for energy efficiency, it may introduce a large end-to-end delay due to sleep delay, since a node with a packet to transmit should wait until the next-hop node of the packet awakes. To resolve this issue, in this paper, we propose the Average Velocity-Based Routing (AVR) protocol for wireless sensor networks that aims at reducing the end-toend delay. The AVR protocol is a kind of a geographic routing protocol that considers both location of a node and waiting time of a packet at the MAC layer. When a node can use information of n-hop away neighbor nodes, it calculates the n-hop average velocity for each of its one-hop neighbor nodes and forwards a packet to the neighbor node that has the highest n-hop average velocity. Simulation results show that as the knowledge range, n, increases, the average end-to-end delay decreases.

We study power-allocation problems in wireless systems through a network utility maximization framework. In this framework, the type of utility function represents the characteristics of each service. Hence, to deal with multiclass services in the system, it is important to accommodate various types of utility functions. In this paper, we consider four types of utility functions that can represent most of the services in wireless networks.We first develop a simple algorithm that solves a general optimization problem with those four types of utility function. We show that, even though the proposed algorithm might not provide the optimal solution, it provides an asymptotically optimal solution, which could be a good approximation to the optimal solution. The proposed algorithm can be applied to various resource-allocation problems in wireless systems. Among them, in this paper, we show that the proposed algorithm can be applied to both uplink and downlink power-allocation problems.

To reduce energy consumption, in most MAC protocols for wireless sensor networks, listen-sleep cycles are adopted. However, even though it is a good solution for energy efficiency, it may introduce a large end-to-end delay due to sleep delay, since a node with a packet to transmit should wait until the next-hop node of the packet awakes. To resolve this issue, in this paper, we propose the Average Velocity-Based Routing (AVR) protocol for wireless sensor networks that aims at reducing the end-toend delay. The AVR protocol is a kind of a geographic routing protocol that considers both location of a node and waiting time of a packet at the MAC layer. When a node can use information of n-hop away neighbor nodes, it calculates the n-hop average velocity for each of its one-hop neighbor nodes and forwards a packet to the neighbor node that has the highest n-hop average velocity. Simulation results show that as the knowledge range, n, increases, the average end-to-end delay decreases.

To reduce energy consumption, in most MAC protocols for wireless sensor networks, listen-sleep cycles are adopted. However, even though it is a good solution for energy efficiency, it may introduce a large end-to-end delay due to sleep delay, since a node with a packet to transmit should wait until the next-hop node of the packet awakes. To resolve this issue, in this paper, we propose the Average Velocity-Based Routing (AVR) protocol for wireless sensor networks that aims at reducing the end-toend delay. The AVR protocol is a kind of a geographic routing protocol that considers both location of a node and waiting time of a packet at the MAC layer. When a node can use information of n-hop away neighbor nodes, it calculates the n-hop average velocity for each of its one-hop neighbor nodes and forwards a packet to the neighbor node that has the highest n-hop average velocity. Simulation results show that as the knowledge range, n, increases, the average end-to-end delay decreases.

This paper reverse-engineers backoff-based random-access MAC protocols in ad-hoc networks. We show that the contention resolution algorithm in such protocols is implicitly participating in a non-cooperative game. Each link attempts to maximize a selfish local utility function, whose exact shape is reverse-engineered from the protocol description, through a stochastic subgradient method in which the link updates its persistence probability based on its transmission success or failure. We prove that existence of a Nash equilibrium is guaranteed in general. Then we establish the minimum amount of backoff aggressiveness needed, as a function of density of active users, for uniqueness of Nash equilibrium and convergence of the best response strategy. Convergence properties and connection with the best response strategy are also proved for variants of the stochastic-subgradient-based dynamics of the game. Together with known results in reverse-engineering TCP and BGP, this paper further advances the recent efforts in reverse-engineering layers 2-4 protocols. In contrast to the TCP reverse-engineering results in earlier literature, MAC reverse-engineering highlights the non-cooperative nature of random access.

This paper reverse-engineers backoff-based random-access MAC protocols in ad-hoc networks. We show that the contention resolution algorithm in such protocols is implicitly participating in a non-cooperative game. Each link attempts to maximize a selfish local utility function, whose exact shape is reverse-engineered from the protocol description, through a stochastic subgradient method in which the link updates its persistence probability based on its transmission success or failure. We prove that existence of a Nash equilibrium is guaranteed in general. Then we establish the minimum amount of backoff aggressiveness needed, as a function of density of active users, for uniqueness of Nash equilibrium and convergence of the best response strategy. Convergence properties and connection with the best response strategy are also proved for variants of the stochastic-subgradient-based dynamics of the game. Together with known results in reverse-engineering TCP and BGP, this paper further advances the recent efforts in reverse-engineering layers 2-4 protocols. In contrast to the TCP reverse-engineering results in earlier literature, MAC reverse-engineering highlights the non-cooperative nature of random access.

This paper reverse-engineers backoff-based random-access MAC protocols in ad-hoc networks. We show that the contention resolution algorithm in such protocols is implicitly participating in a non-cooperative game. Each link attempts to maximize a selfish local utility function, whose exact shape is reverse-engineered from the protocol description, through a stochastic subgradient method in which the link updates its persistence probability based on its transmission success or failure. We prove that existence of a Nash equilibrium is guaranteed in general. Then we establish the minimum amount of backoff aggressiveness needed, as a function of density of active users, for uniqueness of Nash equilibrium and convergence of the best response strategy. Convergence properties and connection with the best response strategy are also proved for variants of the stochastic-subgradient-based dynamics of the game. Together with known results in reverse-engineering TCP and BGP, this paper further advances the recent efforts in reverse-engineering layers 2-4 protocols. In contrast to the TCP reverse-engineering results in earlier literature, MAC reverse-engineering highlights the non-cooperative nature of random access.

This paper designs medium access control (MAC) protocols for wireless networks through the network utility maximization (NUM) framework. A network-wide utility maximization problem is formulated, using a collision/persistenceprobabilistic model and aligning selfish utility with total social welfare. By adjusting the parameters in the utility objective functions of the NUM problem, we can also control the tradeoff between efficiency and fairness of radio resource allocation. We develop two distributed algorithms to solve the utility-optimal random-access control problem, which lead to random access protocols that have slightly more message passing overhead than the current exponential-backoff protocols, but significant potential for efficiency and fairness improvement. We provide readily-verifiable sufficient conditions under which convergence of the proposed algorithms to a global optimality of network utility can be guaranteed, and numerical experiments that illustrate the value of the NUM approach to the complexityperformance tradeoff in MAC design.

It is known that opportunistic scheduling that accounts for channel variations due to mobility and fading can give substantial improvement over nonopportunistic schemes. However, most work on this subject has focused on single-hop cellular types of architectures. The situation is quite different in ad hoc networks due to the inherent multihop nature of transmissions. In this paper, we present a joint opportunistic power scheduling and end-to-end rate control scheme for wireless ad hoc networks. We model the time-varying wireless channel as a stochastic process and formulate a stochastic optimization problem, which aims at maximizing system efficiency by controlling the power allocation of each link and the data rate of each user in the system. The joint power scheduling and rate control algorithm is obtained by using stochastic duality and implemented via stochastic subgradient techniques. We illustrate the efficacy of our approach via numerical examples.

In this paper, we present an opportunistic power scheduling scheme, i.e., a joint time-slot and power allocation scheme for downlink communication in wireless systems. Unlike past works, we allow multiple transmissions in a time-slot that could potentially interfere with each other. These multiple transmissions are allowed to achieve high system efficiency. Hence, it is important to not only select the mobiles to be scheduled in a timeslot, but also to allocate an appropriate transmission power level to these scheduled mobiles. We model the time-varying wireless channel as a stochastic process and formulate a stochastic optimization problem that attempts to maximize the expected total system utility with general constraints on performance or fairness. The power scheduling algorithm is obtained by using stochastic duality and implemented via stochastic subgradient techniques.

The current framework of network utility maximization for rate allocation and its price-based algorithms assumes that each link provides a fixed-size transmission “pipe” and each user’s utility is a function of transmission rate only. These assumptions break down in many practical systems, where, by adapting the physical layer channel coding or transmission diversity, different tradeoffs between rate and reliability can be achieved. In network utility maximization problems formulated in this paper, the utility for each user depends on both transmission rate and signal quality, with an intrinsic tradeoff between the two. Each link may also provide a higher (or lower) rate on the transmission “pipes” by allowing a higher (or lower) decoding error probability. Despite nonseparability and nonconvexity of these optimization problems, we propose new price-based distributed algorithms and prove their convergence to the globally optimal rate-reliability tradeoff under readily-verifiable sufficient conditions. We first consider networks in which the rate-reliability tradeoff is controlled by adapting channel code rates in each link’s physical layer error correction codes, and propose two distributed algorithms based on pricing, which respectively implement the “integrated” and “differentiated” policies of dynamic rate-reliability adjustment. In contrast to the classical price-based rate control algorithms, in our algorithms, each user provides an offered price for its own reliability to the network, while the network provides congestion prices to users. The proposed algorithms converge to a tradeoff point between rate and reliability, which we prove to be a globally optimal one for channel codes with sufficiently large coding length and utilities whose curvatures are sufficiently negative. Under these conditions, the proposed algorithms can thus generate the Pareto optimal tradeoff curves between rate and reliability for all the users. In addition, the distributed algorithms and convergence proofs are extended for wireless multiple-inpit–multiple-output multihop networks, in which diversity and multiplexing gains of each link are controlled to achieve the optimal rate-reliability tradeoff. Numerical examples confirm that there can be significant enhancement of the network utility by distributively trading-off rate and reliability, even when only some of the links can implement dynamic reliability.

We study joint end-to-end congestion control and per-link medium access control (MAC) in ad-hoc networks. We use a network utility maximization formulation, in which by adjusting the types of utility functions, we can accommodate multiclass services as well as exploit the tradeoff between efficiency and fairness of resource allocation. Despite the inherent difficulties of non-convexity and non-separability of the optimization problem, we show that, with readily-verifiable sufficient conditions, we can develop a distributed algorithm that converges to the globally and jointly optimal rate allocation and persistence probabilities.

In this paper, we jointly consider the resource allocation and base-station assignment problems for the downlink in CDMA networks that could carry heterogeneous data services. We first study a joint power and rate allocation problem that attempts to maximize the expected throughput of the system. This problem is inherently difficult because it is in fact a nonconvex optimization problem. To solve this problem, we develop a distributed algorithm based on dynamic pricing. This algorithm provides a power and rate allocation that is asymptotically optimal in the number of mobiles.We also study the effect of various factors on the development of efficient resource allocation strategies. Finally, using the outcome of the power and rate allocation algorithm, we develop a pricing-based base-station assignment algorithm that results in an overall joint resource allocation and base-station assignment. In this algorithm, a base-station is assigned to each mobile taking into account the congestion level of the base-station as well as the transmission environment of the mobile.

In this paper, we investigate the problem of distributively allocating transmission data rates to users in the Internet. We allow users to have concave as well as sigmoidal utility functions as appropriate for different applications. In the literature, for simplicity, most works have dealt only with the concave utility function. However, we show that applying rate control algorithms developed for concave utility functions in a more realistic setting(with both concave and sigmoidal types of utility functions) could lead to instability and high network congestion. We show that a pricing-based mechanism that solves the dual formulation can be developed based on the theory of subdifferentials with the property that the prices “self-regulate” the users to access the resources based on the net utility. We discuss convergence issues and show that an algorithm can be developed that is efficient in the sense of achieving the global optimum when there are many users.

In this paper we consider a power allocation problem in multi-class wireless systems. We focus on the downlink of the system. Each mobile has a utility function that characterizes its degree of satisfaction for the received service. The objective is to obtain a power allocation that maximizes the total system utility. Typically, natural utility functions for each mobile are nonconcave. Hence, we cannot use existing convex optimization techniques to derive a global optimal solution.We develop a simple (distributed) algorithm to obtain a power allocation that is asymptotically optimal in the number of mobiles. The algorithm is based on dynamic pricing and consists of two stages. At the mobile selection stage, the base station selects mobiles to which power is allocated. At the power allocation stage, the base station allocates power to the selected mobiles. We provide numerical results that illustrate the performance of our scheme. In particular, we show that our algorithm results in system performance that is close to the performance of a global optimal solution in most cases.

In this paper, we propose and analyze a new multiple access protocol for a local area optical Internet based on a wavelength division multiplexing technique which uses a passive star coupler. The proposed contention-based reservation protocol can support variable length as well as fixed length messages for transporting Internet packets with one reservation of a minislot at the beginning of a packet transmission. The minislot is used to reserve the data channel on the basis of the slotted ALOHA protocol and the control node ensures subsequent message transmission on the same wavelength. Thus, all messages need not be broken down to many fixed-length packets, and consecutive messages are transmitted through the same wavelength. Moreover, the proposed protocol reduces the collision probability of minislots and improves wavelength utilization. We determine the maximum throughput and verify the results with simulation.

In this paper, we study an input queueing asynchronous transfer mode (ATM) switch with the preemptive priority scheme. Up to now, many researchers have studied this switch, but their models do not yield accurate results. Therefore, we propose another model and analyze the performance of the switch for two different arrival processes, that is, the independent priority arrival process (IPAP) and the dependent priority arrival process (DPAP). In this paper, we model a virtual head of line (HOL) queue like the previous works, and analyze it using a three-dimensional Markov chain. As for each input queue, we model it as a two-class Geomx/Geom/1 queue with preemptive priority. By using this model, we obtain the mean queueing delay of the low priority class and the maximum throughput of the switch. In case of the IPAP, we compare our result with simulation and those of the previous works to show that our model yields more accurate results. In addition, we compare the performances of those two arrival processes to illustrate their difference.