In aerial base station (ABS) placement studies, leveraging information on topographic features for air-to-ground (A2G) channel analysis has been considered a promising approach. Recently, this approach has been studied in several works. However, these studies have predominantly focused on simplistic 3-D cuboid representations of topographic features, which do not adequately capture the complexity of real-world environments, thereby impeding accurate A2G channel analysis. In this article, we propose a more advanced strategy by generalizing the shapes and arrangements of topographic features to enable their realistic and accurate representations. Utilizing these generalized topographic models, we study the problem of finding the ABS location for coverage maximization. To solve this problem, we identify so-called line-of-sight (LoS) and non-LoS (NLoS) zones formed by these generalized topographic features through geometric analysis and obtain straightforward derivations of the coverage area for any given ABS location. Building upon this foundation, we develop a new ABS placement strategy, termed the polygonal feature-aware ABS placement algorithm (FA-poly). Our simulation results demonstrate that the proposed FA-poly significantly outperforms existing baseline methods in terms of the coverage area, as it accurately identifies channel conditions by leveraging information on generalized topographic features and obtains the coverage area based on them.

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.

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.

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.

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.

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 article, 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 1-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.

In this article, 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 nonselected 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 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 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.

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.

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.

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 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 article, an IRS-aided multidevice 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 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.

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 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.

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 recent years, the emergence of flying ad hoc networks (FANETs) with multiple unmanned aerial vehicles (UAVs) has made it possible to effectively perform not only the far-off missions but also assorted complex missions. In this article, 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 the 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, 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 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 this paper, we consider Internet of Things (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 radio frequency 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.

In this paper, we consider Internet of Things (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 wireless power transfer (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 multidevice 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.

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.

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 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 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 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.

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.

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.

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.

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 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 non-collaborative 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 non-collaborative 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.

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.

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 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.

ABSTRACT 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. Copyright © 2010 John Wiley & Sons, Ltd.

Abstract 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. Copyright © 2010 John Wiley & Sons, Ltd.

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.

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 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.

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.

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.

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 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 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. 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 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.

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 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.

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 listen- sleep 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 listen- sleep 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.

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/persistence-probabilistic 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 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 complexity-performance tradeoff in MAC design.

The current framework of network utility max- imization 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 formu- lated 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 non- convexity 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 of- fered 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 rate- reliability 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 game- theoretic 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 for- mulated, 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.

We study joint end-to-end congestion control and per-link medium access control (MAC) in ad-hoc wireless networks. We use a network utility maximization formulation, in which by adjusting the types of utility functions, we can accommodate multi-class 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, under readily-verifiable sufficient conditions, we can develop a distributed algorithm that converges to the globally and jointly optimal rate allocation and persistence probabilities. A key contribution is that our results can accommodate general concave utility function rather than just the logarithmic utility function in existing results.

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 (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.

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 time-slot, 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 distributed rate allocation assumes fixed channel code rates. However, by adapting the physical layer channel coding, different rate-reliability tradeoffs can be achieved on each link and for each end user. Consider a network where each user has a utility function that depends on both signal quality and data rate, and each link may provide a 'fatter' ('thinner') information 'pipe' by allowing a higher (lower) decoding error probability. We propose two distributed, pricing-based algorithms to attain optimal rate-reliability tradeoff, with an interpretation that each user provides its willingness to pay for reliability to the network and the network feeds back congestion prices to users. The proposed algorithms converge to a tradeoff point between rate and reliability, which is proved to be globally optimal for codes with sufficiently large codeword lengths and user utilities with sufficiently negative curvatures

Network Utility Maximization (NUM) has significantly extended the classical network flow problem and provided an emerging framework to design resource allocation algorithms such as TCP congestion control and to understand layering as optimization decomposition. We present a summary of very recent results in the theory and applications of NUM. We show new distributed algorithms that converge to the globally optimal rate allocation for NUM problems with nonconcave utility functions representing inelastic flows, with coupled utility functions representing interference effects or hybrid social-selfish utilities, and with rate-reliability tradeoff through adaptive channel coding in the physical layer. We conclude by discussing how do different decompositions of a generalized NUM problem correspond to different layering architectures.

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 investigate the problem of distributively allocating transmission rates to users on the Internet. We allow users to have concave as well as sigmoidal utility functions that are natural in the context of various applications. In the literature, for simplicity, most works have dealt only with the concave case. However, we show that when applying rate control algorithms developed for concave utility functions in a more realistic setting (with both concave and sigmoidal types of utility functions), they 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 resource 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.

We present and power scheduling scheme, i.e., a joint time-slot and power allocation method for wireless cellular systems. We allow multiple transmissions in a time-slot that can interfere with each other. Hence, it is important to not only select the mobiles to he scheduled in a time-slot, hut also important to allocate an appropriate power level for transmission to these scheduled mobiles in order to achieve high system performance and quality of service. We model the time-varying wireless channel as a stochastic process and formulate a stochastic programming problem that attempts to maximize the expected total system utility, with constraints on the minimum expected utility for each mobile. The power scheduling algorithm is obtained by using stochastic duality and implemented via stochastic subgradient techniques.

We use a utility based power allocation framework in the downlink to treat multi-class CDMA wireless services in a unified way. Our goal is to obtain a power allocation which maximizes the total system utility. Natural utility functions for each mobile are non-concave. Hence we cannot use existing techniques on convex optimization problems to derive a social optimal solution. We propose a simple distributed algorithm to obtain an approximation to the social optimal power allocation. The algorithm is based on dynamic pricing and allows partial cooperation between mobiles and the base station. The algorithm consists of two stages. At the first stage, the base station selects mobiles to which power is allocated, considering their partial-cooperative nature. This is called partial-cooperative optimal selection, since in a partial-cooperative setting and pricing scheme, this selection is optimal and satisfies system feasibility. At the next stage, the base station allocates power to the selected mobiles. This power allocation is a social optimal power allocation among mobiles in the partial-cooperative optimal selection, thus, we call it a partial-cooperative optimal power allocation. We compare the partial-cooperative optimal power allocation with the social optimal power allocation for the single class case. From these results, we infer that the system utility obtained by partial-cooperative optimal power allocation is quite close to the system utility obtained by social optimal allocation.