In this paper, we consider an Open RAN (O-RAN)-based cell-free massive multiple-input multiple-output (mMIMO) system with multiple central processing units (CPUs). In this system, to achieve high network performance, suppressing the inter-cluster interference (ICI) is important. To this end, we study the problem of maximizing the sum of data rates for user equipments (UEs) served by each CPU by optimizing the beamforming of O-RAN radio units (O-RUs). To solve this problem, we develop an effective algorithm, and show that our proposed algorithm significantly enhances performance in O-RAN-based cell-free mMIMO systems with multiple CPUs.

To solve the problem that label noises downgrade the performance of a neural network, we propose an algorithm on clean sample selection and label correction.With the algorithm, we can detect unclean samples before training the neural network and correct their labels, which are finally used to train the neural network together with the clean samples. Through an experiment, we verify the performance of the proposed algorithm.

A cell-free massive multiple-input multiple-output (mMIMO) system employs a large number of access points (APs). Since the APs collaboratively serve user equipments (UEs), its energy and spectral efficiency can be much higher than that of a conventional cellular system. Typically, the inter-user interference (IUI) is suppressed appropriately to improve performance, which usually results in high computational complexity. To reduce this computational complexity, we propose a new cell-free mMIMO architecture, called a dedicated AP-based interference cancellation (DAP-IC) architecture with two types of APs: DS-APs that transmit the data signal (DS) to UEs and IUI-APs that transmit the signal to eliminate the IUI. To this end, we first formulate an optimization problem of maximizing the spectral efficiency while ensuring the fairness of UEs by optimizing the precoding vectors of the APs. We then develop DAP-IC algorithm that solves this problem with a low computational complexity. The simulation results show that the DAP-IC algorithm provides good performance with much lower computational complexity, compared with the weighted minimum mean square error (WMMSE) algorithm for the conventional cell-free mMIMO architecture.

In this paper, we consider an orthogonal frequency division multiple access (OFDMA)-based unmanned aerial vehicle (UAV) relaying system with multiple user equipment (UE) pairs, which has not yet been well studied despite its potential for promising use cases (e.g., a UAV relay-enabled standalone private 5G network). We study a joint optimization problem for UAV trajectory and sub-channel allocation to maximize the total average end-to-end throughput while satisfying the quality-of-serivce (QoS) requirements of UE pairs. To address this problem, we develop a block coordinate descent (BCD)-based algorithm that iteratively solves two sub-problems: 1) a sub-channel allocation optimization problem with a given UAV trajectory, and 2) a UAV trajectory optimization problem with a given sub-channel allocation. Through simulation, we demonstrate the effectiveness of the proposed algorithm.

In this paper, we consider an unmanned aerial vehicle (UAV)-enabled network in which a UAV equipped with a directional antenna relays data from a ground base station (GBS) to ground users (GUs). Under this system, we study a problem minimizing the weighted sum power consumption of the GBS and the UAV by jointly optimizing the location of the UAV, beamwidth of the directional antenna, and power allocation. To solve the problem, we develop a simple heuristic algorithm, and verify its effectiveness through simulation results.

In this paper, we investigate a laser-charged unmanned aerial vehicle (UAV) relaying network where a rotarywing UAV relays data from a ground base station (GBS) to a user equipment (UE), and a charging station (CS) transmits a laser beam to charge the UAV. Instead of using a simplified propulsion power consumption (PPC) model for UAVs, which was widely used in the previous works on the laser-charged UAV relaying networks, we leverage an intractable but more accurate PPC model for UAVs.We study an optimization problem of minimizing the power consumption of the CS while satisfying the UE’s minimum data requirement, and we develop an algorithm to solve this problem by jointly optimizing UAV trajectory and charging power. Through simulation, we demonstrate the effectiveness of our trajectory and charging power control algorithm and validate the importance of leveraging the accurate PPC model for UAVs rather than using the simplified one.

In this paper, we study a waveform design problem for a wireless power transfer (WPT) system where a device harvests power by using a double half-wave rectenna circuit. To this end, we formulate the waveform design problem that maximizes the output DC voltage of the device, and solve it with proper approximations based on the property of WPT. Through the simulation results, we show the performance of the proposed waveform design.

In this paper, we study the joint user and power scheduling for downlink NOMA over fading channels. Specifically, we focus on a stochastic optimization problem to maximize the weighted average sum rate while ensuring given minimum average data rates of users. To address this problem, we first develop an opportunistic user and power scheduling algorithm (OUPS) based on the duality and stochastic optimization theories. By OUPS, the stochastic problem is transformed into a series of deterministic ones for the instantaneous weighted sum rate maximization for each slot. Thus, we additionally develop a heuristic algorithm with very low computational complexity, called user selection and power allocation algorithm (USPA), for the instantaneous weighted sum rate maximization problem. Via simulation results, we demonstrate that USPA provides near-optimal performance with very low computational complexity, and OUPS well guarantees given minimum average data rates.

Phase I clinical trials are designed to test the safety (non-toxicity) of drugs and find the maximum tolerated dose (MTD). This task becomes significantly more challenging when multiple-drug dose-combinations (DC) are involved, due to the inherent conflict between the exponentially increasing DC candidates and the limited patient budget. This paper proposes a novel Bayesian design, SDF-Bayes, for finding the MTD for drug combinations in the presence of safety constraints. Rather than the conventional principle of escalating or de-escalating the current dose of one drug (perhaps alternating between drugs), SDF-Bayes proceeds by cautious optimism: it chooses the next DC that, on the basis of current information, is most likely to be the MTD (optimism), subject to the constraint that it only chooses DCs that have a high probability of being safe (caution). We also propose an extension, SDF-Bayes-AR, that accounts for patient heterogeneity and enables heterogeneous patient recruitment. Extensive experiments based on both synthetic and real-world datasets demonstrate the advantages of SDF-Bayes over state of the art DC trial designs in terms of accuracy and safety.

Subgroup analysis of treatment effects plays an important role in applications from medicine to public policy to recommender systems. It allows physicians (for example) to identify groups of patients for whom a given drug or treatment is likely to be effective and groups of patients for which it is not. Most of the current methods of subgroup analysis begin with a particular algorithm for estimating individualized treatment effects (ITE) and identify subgroups by maximizing the difference across subgroups of the average treatment effect in each subgroup. These approaches have several weaknesses: they rely on a particular algorithm for estimating ITE, they ignore (in)homogeneity within identified subgroups, and they do not produce good confidence estimates. This paper develops a new method for subgroup analysis, R2P, that addresses all these weaknesses. R2P uses an arbitrary, exogenously prescribed algorithm for estimating ITE and quantifies the uncertainty of the ITE estimation, using a construction that is more robust than other methods. Experiments using synthetic and semi-synthetic datasets (based on real data) demonstrate that R2P constructs partitions that are simultaneously more homogeneous within groups and more heterogeneous across groups than the partitions produced by other methods. Moreover, because R2P can employ any ITE estimator, it also produces much narrower confidence intervals with a prescribed coverage guarantee than other methods.

In this paper, we study a grant-free (GF) scheme to efficiently support the IoT user equipments (UEs). Compared with the existing studies regarding the GF schemes where only one type of the IoT UEs is considered, we develop an integrated GF scheme to support both the ultra-reliable low-latency communications (URLLC) and massive machine type communications (mMTC) UEs. Specifically, the proposed integrated GF scheme provides high reliability and low latency for the URLLC UEs by adopting both the contention-based (CB) and contention-free (CF) transmissions, while also supporting the mMTC UEs by adopting the CB transmission. We evaluate the performance of the proposed integrated GF scheme with the ns-3 simulator. Simulation results show that our integrated GF scheme is able to support the both types of the UEs effectively, and especially, the URLLC UEs can achieve high success rate and low latency compared with other GF schemes.

In this paper, we develop a repetition-based grantfree scheme, called DMRS-Applied Repetition Transmission (DART), to support mMTC user equipments (UEs). In DART, the demodulation reference signals (DMRSs) are exploited to estimate the channels as well as to implicitly inform the repetition patterns to the gNB. Based on the detected DMRSs, the gNB performs successive interference cancellation (SIC) to further decode the data of mMTC UEs that transmit their data on the same resource block. DART achieves high success rate, low latency, and low SIC overhead by adopting DMRS-based SIC ordering. Simulation results show that DART is able to support mMTC UEs effectively.

In this paper, we study the performance of repetition-based grant-free (GF) schemes to efficiently in the massive machine type communication (mMTC) scenario. In specific, we investigate and compare the $K$ repetition proposed by the fifth generation (5G) standardization meeting and the irregular repetition slotted ALOHA (IRSA) which uses successive interference cancellation (SIC) to achieve better decoding performance.

In recent years, the emergence of a flying ad hoc network (FANET) with multiple unmanned aerial vehicles (UAVs) can make it possible to effectively accomplish not only far-off missions but also assorted complex missions. In particular, with a limited number of UAVs, it is important to determine both mission assignment of UAVs to enhance mission performance and locations of UAVs to maintain good communication with the ground control station (GCS). In this paper, we study a problem of joint mission assignment and location management for UAVs, with jointly considering communication and mission performance. To solve the problem, employing particle swarm optimization (PSO) algorithm in part, we develop a heuristic algorithm that assigns multiple UAVs to multiple missions and finds locations of all UAVs together to maximize the weighted sum of the communication and mission performance. The simulation results demonstrate that our proposed algorithm provides a good solution.

A flying ad hoc network (FANET) that consists of multiple unmanned aerial vehicles (UAVs) is a promising technology for future networked systems. In this paper, we study a way to construct a topology for FANET that guarantees end-to-end communication between the ground control station (GCS) and each mission UAV that performs its given task via optimizing the locations of the relay UAVs. To this end, we propose a topology construction algorithm based on a particle swarm optimization (PSO) algorithm. Numerical results show that the proposed algorithm achieves a significantly higher performance gain over the random-based topology construction scheme.

For emerging future services, such as virtual reality and remote control, low latency for uplink transmission in a few milliseconds is required. However, with a conventional uplink transmission scheme in LTE/LTE-A, such low latency is hard to be achieved since the scheduling request and grant procedure in the conventional scheme causes a fixed minimum delay. In this paper, we propose a novel uplink transmission scheme which achieves lower latency than the conventional uplink transmission scheme by reducing such delay. Through the simulation results, we show that the performance of the proposed uplink transmission scheme.

In this paper, we study a unit commitment (UC) problem minimizing operating costs of the power system with renewable energy sources. We develop a contextual learning algorithm for UC (CLUC) which learns which UC schedule to choose based on the context information such as past load demand and weather condition. CLUC does not require any prior knowledge on the uncertainties such as the load demand and the renewable power outputs, and learns them over time using the context information. We characterize the performance of CLUC analytically, and prove its optimality in terms of the longterm average cost. Through the simulation results, we show the performance of CLUC and the effectiveness of utilizing the context information in the UC problem.

In this paper, we study joint energy cooperation and traffic management in renewable energy powered cellular system where a centralized unit manages the traffic and the energy cooperation among BSs. We first formulate a stochastic optimization problem which aims at minimizing the total on-grid energy consumption while satisfying the quality-of-service (QoS) requirement of classes of services, i.e., the minimum average data rates. By using the Lyapunov optimization framework, we develop a joint adaptive energy cooperation and traffic management algorithm which does not need the statistical information of the system. Then, we provide the performance analysis which shows our proposed algorithm is asymptotically optimal. Through the simulation results, we verify the theoretical analysis and show that the performance of our algorithm.

In this paper, we study a joint cell association and opportunistic resource scheduling scheme for an OFDMA multicell heterogeneous network (HetNet) in an integrated and unified framework. To this end, we formulate the optimization problem and solve it with a dual approach to develop a scheduling scheme.

In this paper, we study a distributed link scheduling problem for device-to-device (D2D) communication with considering the quality-of-service (QoS) requirement and time-varying channel condition of each D2D link. To this end, we first study an optimal centralized link scheduling algorithm maximizing the total average sum-rate. We then abstract the important scheduling principles of the optimal algorithm in order to use them to develop a distributed link scheduling algorithm. In our distributed link scheduling algorithm, we develop a procedure with which D2D links are able to share their degree of QoS satisfaction and channel condition with each other in a distributed manner. By utilizing those information for link scheduling, our link scheduling algorithm is able to satisfy the QoS requirement of each D2D link while achieving throughput improvement by exploiting time-varying channel condition of each D2D link in a distributed manner.

The information-centric network (ICN) is a new paradigm shift from the traditional IP-based network architecture. In the ICN architecture, any node can have a storage capacity to store contents. Hence, the content allocation algorithm has been actively studied. However, many researches have proposed heuristic content allocation algorithms without considering the system-wise performance. In this paper, we study a content allocation algorithm to minimize the sum of the average end-to-end service times of client nodes. Numerical results show that the proposed content allocation algorithm significantly decreases the sum of the average end-to-end service times of client nodes.

In practice, there are two main usages of the battery in home: reducing electricity energy cost by utilizing time-varying electricity energy price and enhancing privacy by concealing the electricity energy usage pattern of appliances. By operating the battery such that it is charged/discharged during periods with low/high prices, we can reduce the electricity energy cost. On the other hand, by operating the battery such that the electricity usage pattern can be significantly changed from the original usage pattern of appliances, we can protect user’s privacy. However, those two operations are not complement with each other and there exists a trade-off between reducing cost and enhancing privacy. In this paper, we study this issue. To consider both cost and privacy in a unified way, we first define cost metric and privacy metric that should be minimized. Then, we define an overall metric as a weighted sum of the two metrics to study the trade-off between them. We formulate and solve an optimization problem that aims at minimizing the overall metric. Finally, through numerical results, we study and discuss the usage of the battery for reducing cost and enhancing privacy.

In this paper, we study joint topology management and energy cooperation in cellular networks. The topology management scheme transfers users (traffic) from one base station (BS) to another BS by adjusting the cell-size of the BSs, and the energy cooperation scheme transfers harvested energy from one BS to another BS. We first formulate a joint topology management and energy cooperation problem which aims at minimizing the total on-grid energy consumption while satisfying the quality-of-service (QoS) requirement of each user. Then, by solving the problem, we develop a joint topology management and energy cooperation scheme. Topology management and energy cooperation are different approaches to save the on-grid energy consumption, and each scheme is effective to save the on-grid energy consumption in different environments such as different traffic and weather conditions. Through the simulation results, we show which scheme is more effective to save the on-grid energy consumption considering various environments. In addition, we also show that our joint scheme is most effective to deal with the environment which changes dynamically.

In this paper, we consider IEEE 802.15.4 based wireless body area sensor networks(WBASN) in the healthcare environment. Connectivity is a critical constraint in the healthcare environment. In particular, disconnection from a sensor hub is a significant problem that leads to sensor node orphaning. To address this, we propose to use a sub hub designed to enhance WBASN connectivity. The sub hub understudies the original (main) sensor hub and acts to resolve the problems associated with disconnection. We also propose two protocols for use in operating the sub hub. Each protocol prevents the sensor nodes from being orphaned, and forwards data from the sensor nodes to the original sensor hub. In addition, we evaluate the connectivity performance of our proposed protocols and analyze the disconnection tolerance and the number of packet drops. To demonstrate the performances of the proposed protocols, we developed an IEEE 802.15.4 compliant system level simulator with an OPNET Modeler. Simulation results show the individual merits and the demerits of each of the protocols in terms of connectivity. In addition, the energy efficiency of our proposed protocol is evaluated.

In this paper, we study a subchannel scheduling problem for device-to-device (D2D) multicast communication with considering the quality-ofservice (QoS) requirement and time-varying channel condition of each D2D link. By solving the problem, we first develop an optimal opportunistic subchannel scheduling algorithm maximizing the average sumrate, while guaranteeing the QoS requirement of each D2D link. We then develop a heuristic subchannel scheduling algorithm which has low signaling overhead and complexity. Through simulations, we show that both algorithms guarantee the QoS requirements of D2D links.

In the smart grid system, dynamic pricing can be an efficient tool for the service provider which enables efficient and automated management of the grid. However, in practice, the lack of information about the customers’ time-varying load demand and energy consumption patterns and the volatility of electricity price in the wholesale market make the implementation of dynamic pricing highly challenging. In this paper, we study a dynamic pricing problem in the smart grid system where the service provider decides the electricity price in the retail market. In order to overcome the challenges in implementing dynamic pricing, we develop a reinforcement learning algorithm. To resolve the drawbacks of the conventional reinforcement learning algorithm such as high computational complexity and low convergence speed, we propose an approximate state definition and adopt virtual experience. Numerical results show that the proposed reinforcement learning algorithm can effectively work without a priori information of the system dynamics.

The Internet of Things (IoT) has been a hot topic for the future of computing and communication. It will not only have a broad impact on our everyday life in the near future, but also create a new ecosystem involving a wide array of players such as device developers, service providers, software developers, network operators, and service users. In this paper, we present an open service framework for the Internet of Things, facilitating entrance into the IoTrelated mass market, and establishing a global IoT ecosystem with the worldwide use of IoT devices and softwares. We expect that the open IoT service framework we proposed will play an important role in the widespread adoption of the Internet of Things in our everyday life, enhancing our quality of life with a large number of innovative applications and services, but also offering endless opportunities to all of the stakeholders in the world of information and communication technologies.

The wireless mesh networks (WMN) has attracted significant interests as a next-generation 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. In addition, due to directional antennas, the interference range of each transmission can be significantly reduced and we can further utilize the frequency separation. Hence, we can improve the throughput of the WMN significantly with multiple orthogonal channels and multiple directional antennas. In this paper, we study a joint channel and flow assignment problem for the WMN with multiple channels and multiple directional antennas. We first formulate an optimization problem as a mixed integer optimization problem that aims at maximizing the network utility. Since it is inherently hard to solve the mixed integer optimization problem, we propose a heuristic algorithm to solve the problem. The simulation results show our heuristic algorithm provides a reasonable performance in terms of the network utility compared to its upper bound.

In this paper, we study a mobility-aware adaptive transmission between two mobile nodes in the mobile ad-hoc network. We model the amount of data that can be transmitted between two mobile nodes in a single encounter as a function of the start time of the transmission. From this model, we show that there exists a tradeoff between the data rate and the time duration for the transmission through the start time of the transmission. Hence, it is important to have the optimal start time of the transmission to maximize the amount of data that can be transmitted between two mobile nodes. In this paper, we obtain the optimal start time and show that in general, the optimal start time depends on the mobilities (i.e., velocities) of two mobile nodes.

In this paper, we analyze the throughput of the IEEE 802.15.4 medium access control (MAC) protocol with considering both contention access period (CAP) and contention free period (CFP). We first analyze the CSMA/CA algorithm in the CAP using a Markov chain considering the guaranteed time slot (GTS) request packets for the CFP. And we provide an analytical model of the GTS allocation algorithm in the CFP using a Markov chain and the results of the CAP. Numerical results show that the ratio of the length of the CAP to that of the CFP affects the throughput of the system.

Recently, joint subchannel allocation and transmission power control problems for multi-cell 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 the problems without trying to achieve the optimal resource allocation and the maximum system capacity. In this paper, we study the joint subchannel allocation and transmission power control problem for multi-cell OFDMA systems from the point of the optimal resource allocation and the maximum system capacity with a monotonic optimization approach. Even though we do not obtain the exact optimal resource allocation that achieves the maximum system capacity, we develop an algorithm for resource allocations that provide both upper and lower bounds on the maximum system capacity. Through numerical results, we evaluate our algorithm showing that it provides good approximations to the maximum system capacity in multi-cell OFDMA systems in most cases.

Future generation smart grids will allow customers to trade energy bidirectionally. Specifically, each customer will be able to not only buy energy from the aggregator during its peak hours but also sell its surplus energy during its off-peak hours. In these emerging energy trading markets, a key component will be the deployment of effective energy billing schemes which consider the customers residential load scheduling. In this paper, we consider a residential load scheduling problem with bidirectional energy trading. Compared with the previous work, in which customers are assumed to be obedient and agree to maximize the social welfare of the smart grid system, in this paper, we consider a non-collaborative approach, where consumers are self-interested. We model the energy scheduling problem as a non-cooperative game, where each customer determines its load scheduling and energy trading to maximize its own profit. In order to resolve the unfairness between heavy and light customers, we propose a novel tiered billing scheme that can control the electricity rates for customers according to their different energy consumption levels. We also propose a distributed energy scheduling algorithm that converges to the unique Nash equilibrium of the studied non-cooperative game. Through the numerical results, we study the impact of the proposed tiered billing scheme on the selfish customers’ behavior and on their incentives to participate in the energy trading market.

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 a novel electricity load scheduling which, unlike previous works, jointly considers 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. Under the assumption of the collaborative system where the customers agree to maximize the social welfare of the power system, we develop an optimal distributed load scheduling algorithm that maximizes the social welfare. Through numerical results, we show when the energy trading leads to an increase in the social welfare in various usage scenarios.

In this paper, we study a dynamic topology management problem in the wireless cellular network, in which base stations can be switched on or off to save energy consumption of the network. To model non-uniform traffic demand over the network, we divide the entire network area into subareas, assuming that each of subareas has its own traffic demand, which can be satisfied by allocating enough bandwidth and power from a base station. With this network model, we propose a two-step algorithm for dynamic topology management. In the first step, we obtain the maximum radius of the service area for each base station by solving an optimization problem. In the second step, we first construct a graph based on the maximum radii obtained in the first step. We then identify the minimal set of base stations that should be switched on to satisfy the demand of each subarea in the network based on the shortest path algorithm on the constructed graph. From numerical results, we show that our algorithm provides a significant amount of energy saving, while adaptively considering the traffic demand of each subarea in the network.

In this paper, we study an optimal topology and routing problem in the body area network (BAN). Aiming at maximizing the network lifetime, we consider the guarantee of two important quality-of-service (QoS) requirements for BAN applications: the minimum bit error rate and the minimum data rate. Especially, we study the effect of the maximum number of hops that is allowed in routing to the system performance.

We study an opportunistic subchannel scheduling and transmission mode selection problem for the OFDMA system with device-to-device (D2D) communication. We allow D2D users to opportunistically select its transmission mode between two transmission modes: direct transmission between D2D users (direct one- hop transmission) and indirect transmission through the BS (indirect two-hop transmission). We develop a framework with which opportunistic transmission mode selection can be modeled as opportunistic subchannel scheduling, which enables our problem to be reduced to an opportunistic subchannel scheduling problem. We formulate a stochastic optimization problem that aims to maximize the average sum-rate of the system, while satisfying the quality-of-service (QoS) requirement of each user. By solving the problem, we develop an optimal opportunistic subchannel scheduling algorithm, which enables us to perform both subchannel scheduling and transmission mode selection opportunistically.

In this paper, we study a relaying node re-selection problem in the IEEE 802.15.6 body area network (BAN) protocol, which requires to alleviate energy consumption burden of a relaying node and enhance the network life time. To this end, we develop a procedure for wake-up schedule control for a node that can be accommodated in the IEEE 802.15.6 standard. Numerical results show that by allowing a node to re-select its relaying node with our procedure, we can improve the network lifetime.

In this paper, we study a mobility control problem in mobile ad-hoc networks with controllable mobility. Especially, we consider mission-critical networks in which nodes have their own specific missions whose degree of satisfaction depends on the their locations as well as want to maintain a good communication quality with their neighbor nodes that also depends on their locations. Hence, in this paper, we study a joint mission and communication aware mobility control 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 machine-type communications (MTC), since there are a large number of devices, the signaling congestion is an important issue. In this paper, we try to reduce the signaling overhead, which is generated by MTC devices for the mobility management. It is possible that multiple MTC devices have the same mobility. For example, the MTC devices which are attached to a vehicle or loaded in a container have the same mobility. In this paper, we propose a method of group based mobility management for MTC devices, which have the same mobility, in order to reduce the signaling overhead for the tracking area update procedure in the LTE system.

In this paper, we study an electricity load scheduling problem in a residence. Compared with previous works in which only limited classes of appliances are considered, to consider various appliances with different operation and energy consumption characteristics with each other, we classify appliances into four classes and provide mathematical models to describe them. With the developed appliance models, we also propose an electricity load scheduling algorithm that controls the operation time and energy consumption of each appliance adapting to Time of Use (TOU) pricing and its operation and energy consumption characteristics to minimize the total electricity bill.

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 are operated on OFDMA systems, each subchannel can be reused separately among femtocells by carefully treating inter-cell interferences. 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 fmetocell networks, which make 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. Since the problem is formulated as a nonlinear integer programming, 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 femtocell to maximize the sum utility and finding actual subchannel allocation that achieves the granted number of subchannels for each femtocell. Numerical results show that the proposed subchannel allocation algorithm provides near-optimal performance.

Recently, various energy harvesting techniques from ambient environments were proposed as alternative methods for powering sensor nodes, which convert the ambient energy from environments into electricity to power sensor nodes. However, those techniques are not applicable to the wireless sensor networks (WSNs) in the environment with no ambient energy source. To overcome this problem, an RF energy transfer method was proposed to power wireless sensor nodes. However, the RF energy transfer method also has a problem of unfairness among sensor nodes due to the significant difference between their energy harvesting rates according to their positions. In this paper, we propose a medium access control (MAC) protocol for WSNs based on RF energy transfer. The proposed MAC protocol adaptively manages the duty cycle of sensor nodes according to their the amount of harvested energy as well as the contention time of sensor nodes considering fairness among them. Through simulations, we show that our protocol can achieve a high degree of fairness, while maintaining duty cycle of sensor nodes appropriately according to the amount of their harvested energy.

Thus far, most researches on the cooperative transmission focused on the reliability point of view, showing that we can significantly improve the reliability of data transmissions by the cooperative transmission. On contrary, in this paper, we study the performance of the cooperative transmission from different perspectives such as system throughput and fairness.We consider a three-node system that consists of two source nodes, which can choose to either cooperate with each other or not to transmit their data, and one destination node. Based on this system, we study the conditions under which the cooperative transmission is beneficial to improve system throughput and satisfy the fairness requirement between source nodes.

In this paper, we study an opportunistic subchannel scheduling problem for the relay-based OFDMA cellular network where relay stations (RSs) can perform opportunistic network coding with downlink and uplink sessions of a mobile station (MS). In wireless networks, network coding is one of promising techniques to improve network performance in various aspects. However, previous studies did not provide optimal opportunistic resource allocation solutions for the OFDMA networks with network coding-aware RSs as well as they did not consider practical duplexing schemes for the transmission of network coded data. In this paper, to allow network coding with downlink and uplink sessions at RSs, we consider a time-division duplexing (TDD) based time-slot structure. We formulate a stochastic optimization problem to maximize the average weighted-sum rate for both downlink and uplink sessions. Then, by solving the problem, we provide an optimal opportunistic subchannel scheduling algorithm that can opportunistically use network coding with considering time-varying channel states.

To satisfy a target utilization of each link in com· munication networks, re.routing of sessions and expansion of the link capacity could be considered. In this paper, we study a problem that finds joint optimal link weights for routing and capacity expansion so as to alleviate over·utilization of links in communication networks with minimum costs. We compare the performance of our joint algorithm with that of the algorithm that considers only link weight optimization [1], which shows that the joint optimization provides better performance than the link weight optimization only.

In this paper, we consider a rate control problem in communication networks based on the network utility maximization framework. To accurately model the satisfaction of a user to its service, we propose a new concept of the utility function, which is called a user-level utility function, considering both transmitting and receiving sessions of a user together. We then formulate an optimization problem for rate control with user-level utility functions and develop a distributed rate control algorithm.

Since the capacity of a wireless link depends on the distance between its transmitter and receiver nodes, the optimal placement of nodes in the wireless network is important to improve the system efficiency. We study this issue in this paper considering a wireless network that consists of multiple nodes that do not have controllable mobility and a relay node that has controllable mobility. We model the capacity of a wireless link between node and relay node as a function of the distance between them. We then formulate the optimization problem that aims at maximizing the weighted throughput of a node that achieves the lowest weighted throughput among all nodes. Unfortunately, the problem is inherently non-convex, which is in general difficult to solve. However, in this paper, we develop the algorithm for the optimal placement of the relay node based on duality theories in optimization. We also show that the optimal position of the relay node obtained by our algorithm is in fact the weighted max-min fair position.

We address an opportunistic subchannel scheduling problem in relay-based OFDMA cellular networks. In most previous works on relay-based cellular networks, immediate relaying at relay stations (RSs) was assumed, without allowing the opportunistic scheduling at RSs. Hence, 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 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 worse channel state among two links, resulting in wastes of radio resources. However, if the opportunistic scheduling is allowed at each RS, the time-varying channel state of each link can be fully exploited, and thus more efficient radio resource allocation is possible. Hence, in this paper, we consider an opportunistic subchannel scheduling at both BS and RSs. We formulate a stochastic optimization problem that aims at maximizing the average sum-rate of the relay-based network where the time-varying wireless channel is modeled as a stochastic process. By solving the problem, we develop an optimal opportunistic subchannel scheduling algorithm for both BS and RSs. Numerical results show that we can significantly improve system performance by allowing the opportunistic scheduling at RSs.

In this paper, we study a cross-layer optimization problem for a network that consists of one wireline core network and multiple wireless access networks. We consider four layers among five layers in the network layering architecture: application characteristics through the utility function and quality of service requirements in the application layer; end-to-end flow control in the transport layer; opportunistic scheduling in the data link layer; adaptive modulation and coding in the physical layer. We formulate a stochastic optimization problem considering above four layers and both wireline and wireless parts of the network jointly that results in a utility-based joint end-to-end flow control and opportunistic scheduling problem for the integrated wireless and wireline network. We solve the problem by using a dual approach and a stochastic sub-gradient algorithm. The developed algorithm can be implemented in a distributed way: vertically among four layers and horizontally among all entities in the network, clearly showing what should be done in each layer and each entity and what parameters should be exchanged between layers vertically and between network entities horizontally.

Abstract—In this paper, we study the problem that finds optimal link weights for routing in communication networks with a target utilization for each link. Since the problem is known as NP-hard, we take a heuristic approach based on the simulated annealing method. However, to make the algorithm to be more robust and converge fast, we modify the basic simulated annealing method. Numerical results show that our algorithm provides a better performance than the basic simulated annealing method.

In most of researches in resource allocation for wireless networks, uplink and downlink problems are considered separately, especially when resources for uplink and downlink are statically partitioned, as in FDD and static TDD systems. However, even in those systems, joint resource allocation for uplink and downlink can improve system efficiency and we study this issue in this paper with the concept of the user-level utility function. In most cases, a user has a two-way communication that consists of two sessions: uplink and downlink sessions and its overall satisfaction to its communication depends on its satisfaction to each of its sessions. To model user’s overall satisfaction to its communication, we define a user-level utility function, which is defined as a function of its session-level utility functions. We then formulate and solve the optimization problem with user-level utility functions for cell-level resource scheduling that jointly considers uplink and downlink resource allocation. Simulation results show that our cell-level scheduling in which resource allocation in both uplink and downlink is done jointly outperforms link-level scheduling, in which resource allocation in each of uplink and downlink is done separately in most cases, especially when the asymmetry between uplink and downlink is large.

So far, many researches on network coding are performed with higher layer protocols such as MAC, routing, and flow control protocols without consideration of physical layer issues such as channel conditions of links. However, in wireless networks, the consideration of properties at the physical layer is important to improve system performance. Hence, in this paper, we study an opportunistic scheduling and adaptive modulation problem for wireless networks with network coding, which is a joint problem for MAC and physical layers. A similar problem was studied in [1] considering an idealized system in which the data rate of each link is modeled with the Shannon capacity. They showed that to maximize the throughput of a transmission, the optimal subset of native packets that are encoded within a coded packet should be selected based on the channel condition at the destination for each native packet. Moreover, they also showed that it may not be the optimal selection to encode all possible native packets within a coded packet. In this paper, we consider a more realistic model than that of [1] with practical modulation schemes such as M-PSK and MQAM. We show that the optimal policy that maximizes the throughput of a transmission is to encode all available native packets within a coded packet regardless of the channel condition at the destination for each native packet, which is a different conclusion from that of [1]. However, we show that adaptive modulation, in which its constellation size in a coded packet is adjusted based on the channel condition of each destination node, provides a higher throughput than the scheme with fixed modulation, in which its constellation size is always fixed regardless of the channel condition at each destination node.

In this paper, we study an end-to-end flow control algorithm considering network coding in wireless ad-hoc networks. As a network coding scheme, we use XOR network coding, in which each node bitwise-XORs some packets from different sessions, and then broadcasts the XORed packets. This process can reduce the number of required transmissions and, thus improve network utilization, especially if it is used with appropriate network protocols. Considering this XOR network coding, we formulate an optimization problem for end-to-end flow control in wireless ad-hoc networks that aims at maximizing network utility. We then develop a distributed flow control algorithm by solving the optimization problem. The simulation results show that our distributed flow control algorithm performs well exploiting the advantages of network coding and provides higher network utility than the flow control algorithm without considering network coding.

Abstract—In this paper, we study an opportunistic power scheduling problem in relay-based OFDMA cellular networks. In most previous works on relay-based cellular networks, scheduling at relay stations (RSs) is not allowed. Hence, each RS should immediately transmit all the received data from the base station (BS) in the first phase in a time-slot to mobile stations (MSs) in the second phase in the same time-slot. In this strategy, the effective data rate of the two-hop transmission (BS-RS and RS-MS) is limited to the rate of the link with worse channel state among two links. Hence, in those systems, even though opportunistic scheduling is allowed in the BS, time-varying channel state of each link cannot be fully exploited, which may result in wastes of radio resources. However, if opportunistic scheduling is allowed not only at the BS but also at each RS, the BS and each RS can fully exploit time-varying channel state of each link, so that more efficient radio resource allocation is possible. We formulate a stochastic optimization problem that aims at maximizing the average sum-rate of relay-based networks where the time-varying wireless channel is modeled as a stochastic process. By solving the problem, we develop an optimal power scheduling algorithm which can be implemented in a distributed manner at the BS and each RS. Simulation results show that by allowing opportunistic scheduling at both the BS and RSs, we can improve system performance more significantly.

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 the communication network, many services are variable rate services, i.e., the degree of their rate requirement varies over time. Hence, in this paper, we allow the degree of the rate requirement of each user to change over time and model it by using a stochastic utility function that varies stochastically according to the variation of the degree of its rate requirement. We formulate a stochastic optimization problem with stochastic utility functions that aims at maximizing the average network utility while satisfying the constraint on the link capacity. By solving the stochastic optimization problem, we develop a distributed flow control algorithm that converges to the optimal rate allocation.

In this paper, we propose a new framework for opportunistic 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 scheduling for such a system, a nice framework has been developed in. However, in this paper, we consider a more general system that has a probabilistic scheduling policy and more general fairness constraints. Moreover, in addition to this generalization, we develop a new framework for opportunistic scheduling based on the duality theory. The duality theory is a well-defined theory in the mathematical optimization area. Hence, by using the framework developed in this paper, we can analyze the developed algorithm more rigorously and systematically.

We study power allocation problems in wireless systems through 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 develop a simple algorithm that can accommodate four types of utility functions in a unified way, while providing asymptotically optimal power allocation. Those types of utility functions can represent most of services in wireless networks. We first develop an algorithm that solves a general optimization problem and, then, show that our algorithm can be applied to the downlink power allocation problem.

In this paper, we design an energy efficient MAC protocol for mobile sensor networks. In our MAC protocol, to reduce idle listening, each node has its listen-sleep schedule. A subset of nodes that follow the same schedule forms a virtual cluster. The network consists of multiple virtual clusters and nodes in one virtual cluster may follow a different listensleep schedule from that of nodes in another virtual cluster. Hence, to support a mobile sensor node that moves into a new virtual cluster that follows a different listen-sleep schedule, it is necessary to have a fast and energy efficient listen-sleep schedule adaptation protocol. To this end, in our MAC protocol, we utilize schedule information on border nodes between virtual clusters. This allows us to implement fast and energy efficient listensleep schedule adaptation, which consists of two main functions: energy efficient secondary listen period and smart scheduling adaptation. Simulation results show that our protocol can provide fast scheduling adaptation while achieving energy efficiency.

In this paper, we propose an energy efficient MAC protocol for wireless sensor networks. Since sensor nodes are equipped with a limited energy of batteries, reducing energy consumption is a critical issue for extending network lifetime. To resolve this issue, we adopt a listen-sleep cycle as in S-MAC [1], allowing sensor nodes to turn their transceiver off during a sleep period. In S-MAC, sensor nodes have a fixed duty cycle and a synchronized scheduling in a virtual cluster. Hence, in S-MAC, it is not easy to adapt to the variation of network environment. Moreover, due to the synchronized scheduling, transmission collisions will increase resulting in energy waste and low throughput. To cope with such inefficiencies in S-MAC, we propose probability sensor MAC (PS-MAC), in which each node determines ‘listen’ or ‘sleep’ pseudo-randomly based on its own pre-wakeup probability and pre-wakeup probabilities of its neighbor nodes in each time slot. This allows the listen-sleep schedule of each transmitter and receiver pair to be synchronized while that of rest of nodes can be asynchronous. Therefore, collisions can be reduced even under heavy traffic conditions resulting in reducing energy waste and achieving high throughput. In addition, since pre-wakeup probability of each node can be adjusted adapting to the change of the network environment, by dynamically adjusting pre-wakeup probabilities of sensor nodes, system throughput can be further improved.

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. The minimum amount of backoff aggressiveness needed for uniqueness of Nash equilibrium and convergence of the best response strategy are established as a function of user density. 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 completes the recent efforts in reverse engineering the main protocols in layers 2-4.

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 (lower) rate on the transmission ‘pipes’ by allowing a higher (lower) decoding error probability. Despite non-separability and nonconvexity of these optimization problems, we propose new pricebased 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 ratereliability 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. The distributed algorithms and convergence proofs are extended for wireless MIMO multi-hop networks, in which diversity and multiplexing gains of each link are controlled to achieve the optimal ratereliability tradeoff.

This paper analyzes and designs medium access control (MAC) protocols for wireless ad-hoc networks through the network utility maximization (NUM) framework. We first reverse-engineer the current exponential backoff (EB) type of MAC protocols such as the BEB (binary exponential backoff) in the IEEE 802.11 standard through a non-cooperative gametheoretic model. This MAC protocol is shown to be implicitly maximizing, using a stochastic subgradient, a selfish local utility at each link in the form of expected net reward for successful transmission. While the existence of a Nash equilibrium can be established, neither convergence nor social welfare optimality is guaranteed due to the inadequate feedback mechanism in the EB protocol. This motivates the forward-engineering part of the paper, where a network-wide utility maximization problem is formulated, using a collision and persistence probability 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 through a rigorous and systematic design. We develop two distributed algorithms to solve the MAC design NUM problem, which lead to random access protocols that have slightly more message passing overhead than the current EB protocol, 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 through numerical examples illustrate the value of the NUM approach to the complexity-performance tradeoff in MAC design.