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This paper considers a heavy loaded full-duplex (FD) small cell consisting of one FD base station and multiple half-duplex users where the number of subchannels is less than the number of uplink and downlink users. With the objective of maximizing the total sum-rate, a joint power allocation, and user pairing problem is formulated. To solve this problem, a novel decomposition method is proposed, which decouples the power allocation and user pairing problem into two subproblems. It is proven that the optimal solution of subproblems is the optimal solution of the original problem. Then, optimal algorithms are developed for the two subproblems. Numerical results show that the proposed algorithm achieves better performance compared with the baselines of the greedy algorithm and random pairing algorithm. [ABSTRACT FROM AUTHOR]

Ultradense heterogeneous networks (HetNets) are emerging as an inevitable approach to tackle the capacity crunch in cellular networks. However, imbalanced load among small and macrocells and poor resource utilization as a consequence in HetNets remains a long-standing problem. This paper addresses this problem by presenting a solution for maximization of coverage and capacity while minimizing load imbalance among macro and small cells. Most recent studies on the topic focus on either optimization of coverage, capacity or load, or a combination of two of these three intertwined objectives. We formulate the optimization problem as a function of two hard parameters namely antenna tilt and transmit power, and a soft parameter, cell individual offset, that affect the coverage, capacity, and load directly. The resulting solution is a combination of the otherwise conflicting coverage and capacity optimization (CCO) and load balancing (LB) self-organizing network (SON) functions. In the presented joint CCO-LB solution, a conflict free operation of CCO and LB is ensured by designing a novel load aware user association methodology and resolving the effects of shadowing on coverage probability using stochastic approximation. The problem is proven to be nonconvex and is solved using genetic algorithm, sequential quadratic programming, and pattern search algorithms. The proposed CCO-LB solution is compared against two recently proposed CCO and CCO-LB solutions in the literature. Results show that the proposed solution can yield significant gain in terms of throughput, spectral efficiency, and load distribution. [ABSTRACT FROM AUTHOR]

Traffic offloading via device-to-device communications is expected to play a major role to meet the exponential data traffic growth in wireless networks. In this paper, we focus on the problem of user capacity maximization in ultra-dense heterogeneous networks with device-to-device cooperation, where a large number of users in a given geographical area request common data content, such as video on demand streaming, with strict quality of service guarantees. We aim at finding the best strategy for delivering the content either over long range connectivity from the access points or short range connectivity from peer mobile terminals while meeting a target rate per served user. We formulate the multiuser resource management problem as an optimization problem including traffic offloading and channel allocation considering a very high user density reaching up to 1.75 users per square meter. We extend the system model to include mobile terminals acting as content owners by having the content cached locally, in order to further enhance the network capacity and coverage. We then propose an iterative resource management solution, which first solves the optimization subproblem of traffic offloading with orthogonal channels, and then optimally allocates channels to the transmitters with minimized interference due to channel reuse. We also propose efficient suboptimal hierarchical tree-based algorithms that operate in real time with dynamic and fast solutions for ultra-dense networks. For performance evaluation, we consider a realistic scenario consisting of a stadium topology with thousands of mobile terminals active simultaneously. We generate results as a function of a wide range of system parameters, and demonstrate that the proposed algorithms achieve near-optimal performance with notably low time complexity. [ABSTRACT FROM AUTHOR]

This paper examines the problem of optimizing hybrid electric vehicle (HEV) power management for fuel economy. This paper begins by presenting a pseudospectral algorithm to solve this optimization problem. Compared with traditional dynamic programming (DP)-based optimal power management approaches, this algorithm has two key advantages: It is numerically more efficient, and it furnishes both the optimal state and costate trajectories. Building on the second advantage, this paper proposes a two-level strategy for optimal power management in vehicles commuting along fixed routes. The upper level of the proposed strategy is a costate adaptation algorithm employing pseudospectral optimization, whereas the lower level is an instantaneous optimization controller employing Pontryagin's minimum principle (PMP). This paper shows its pseudospectral optimization algorithm and two-level strategy using numerical simulation for a series hybrid school bus. Parameters of the bus powertrain model are obtained from experimental component tests performed at the National Engineering Laboratory for Electric Vehicles, Beijing Institute of Technology. Simulation results show that the pseudospectral method reaches a solution close to DP with higher computational efficiency. Furthermore, the proposed two-level strategy is capable of adapting vehicle power management based on road-grade predictions, i.e., an attractive feature compared with more traditional online hybrid power management approaches such as the use of proportional integral derivative (PID) control for adaptive equivalent fuel consumption minimization [PID equivalent consumption minimization strategy (PID-ECMS)]. [ABSTRACT FROM PUBLISHER]

Parameters tuning of the model and the controller is an essential problem for autonomous vehicles. Traditionally, parameter tuning work is accomplished by manual operation or grid search, which is a tedious and time-consuming work. Recently, thanks to the development of machine learning community, several automatic controller parameter tuning approaches emerged, which usually model the performance function as a Gaussian process, and complete the automatic tuning procedure via Bayesian optimization. However, the existing approaches rarely consider the time-varying feature of the system performance, which is practical in many scenarios, induced by the unmodeled interactions between the system and the environment. In this paper, we take both of the dynamic model uncertainty and the controller parameters uncertainty into account, and tune them to find a global optimal choice for minimizing the time-varying control costs, which is modeled by the time-varying Gaussian process. We provide a novel algorithm, named as time-varying controller optimization. We validate our approach on synthetic simulation and real experiment, respectively. Taking the cumulative regret as the performance metric, we find that our approach has a better performance compared with the stationary algorithm. [ABSTRACT FROM AUTHOR]

The rapidly growing mobile traffic calls for higher quality of service (QoS) and lower on-grid energy cost. Integrating energy harvesting to small-cell networks (SCNs) with caching has been regarded as a promising solution. However, considering the limited resources of the small-cell base stations (SBSs), how to cache contents and serve the mobile users (MUs) is a crucial problem. In this paper, we jointly design a green content caching and mobile user–base station (MU-BS) association mechanism in the SCNs, where the number of MUs’ content requests served by the SBSs is maximized. This mechanism maximizes the number of requests handled by the SBSs under the constraints of the available energy and backhaul capacity of the SBSs, while concurrently ensures the QoS of the MUs. First, the joint optimization problem is formulated, which is NP-hard. To decrease the complexity of the problem, we divide it into two subproblems: MU-BS association and content caching. But when the number of SBSs is large, these two subproblems cannot be efficiently solved by the conventional centralized approaches. Thus, we construct a two-dimensional-action weakly acyclic game to optimize these two subproblems distributedly and iteratively, in which the existence of the two-dimensional-action Nash equilibrium has been proved. Finally, we propose a two-dimensional iteration algorithm to solve the problem. The simulation results show that the proposed algorithm can achieve better performance when compared with the other existing algorithms. [ABSTRACT FROM AUTHOR]

This paper aims at solving two classes of energy efficiency (EE) maximization problems in multiple channels wireless communication systems. First, the EE maximization problem with sum power constraint is solved based on the geometric water-filling approach; and second, the approach is extended into the EE maximization problem with additional least throughput requirement constraint. Our proposed algorithms make use of the water-filling structure of the optimal solution and provide exact and computation efficient solution to the energy-efficient power allocation problems. The proposed algorithms also have excellent scalability, which is applicable for large-scale wireless communication systems. Optimality of the proposed algorithms is strictly proved, and the proposed algorithms only require low-degree polynomial computational complexity. Numerical results are presented to demonstrate the efficiency of the proposed algorithms. To the best of our knowledge, no prior algorithms in the existing literature could provide such solutions to the EE maximization problems under the merit of exactness and the efficiency. [ABSTRACT FROM AUTHOR]

In this paper, we study the energy efficiency (EE) optimization for simultaneous wireless information and power transfer in multiple-input-multiple-output two-way amplify-and-forward relay networks, where the energy-constrained relay harvests energy from two sources’ signals by power splitting (PS) scheme to forward the information of two sources. Our objective is to jointly design the sources and relay precoding matrices and the PS ratio to maximize the EE of the network, under the transmit power constraints at sources and relay and the minimum spectral efficiency requirement. We transform the nonconvex precoding and PS design problem into a difference of convex programming and propose an efficient iterative algorithm based on the constrained concave convex procedure to obtain a local optimum. To reduce computational complexity, we propose a generalized singular value decomposition based channel diagonalization scheme to simplify the optimization into a joint source and relay power allocation problem. The alternating optimization method is employed to solve this problem and a suboptimal solution to the EE maximization problem is obtained. Simulation results demonstrate the effectiveness of the proposed schemes. [ABSTRACT FROM AUTHOR]

This paper presents an on-line energy management strategy (EMS) based on trip condition prediction for commuter plug-in hybrid electric vehicles (P-HEVs). The purpose is to provide an on-line predictive control approach to minimize fuel consumption. Two pivotal contributions are provided to realize the purpose. First of all, we establish the trip condition prediction model by using back propagation neural network, to obtain the real-time vehicle-speed trajectory on-line. Particularly, both the genetic algorithm and particle swarm optimization algorithm are applied to improve the prediction accuracy of the trip condition prediction model. Next, to obtain an applicable EMS in real time, we propose a dynamic programming-based predictive control strategy. Finally, a simulation study is conducted for applying the proposed strategy to a practical trip path in the Beijing road network. The results show that the designed trip condition prediction model can effectively realize the on-line vehicle-speed prediction, and the prediction accuracy is more than 93%. In addition, compared to the offline global optimization EMS, although the proposed strategy makes the fuel consumption grow less than 5.2%, it can be implemented in real time. Moreover, compared with the existing real-time EMSs, it can further reduce the fuel consumption and emissions. It shows that the proposed EMS can provide an effective solution for commuter P-HEVs applying it on-line. [ABSTRACT FROM PUBLISHER]

Rising user expectations are changing the way mobile operators manage their networks. In this paper, a self-tuning algorithm for adjusting parameters in a multiservice packet scheduler of a Long-Term Evolution base station is proposed to optimize the overall system Quality of Experience (QoE) based on network performance statistics. For this purpose, the algorithm iteratively changes service priority parameters to reprioritize services so as to make the most of available resources. The proposed algorithm ensures that the best overall system QoE is always reached by analyzing optimality conditions, unlike previous works, which only guarantee a minimum user satisfaction level or aim to balance QoE among services. Method assessment is carried out with a dynamic system-level simulator in a realistic service scenario. Simulation results show that the overall network QoE can be improved up to $35\%$ by tuning service priority parameters. [ABSTRACT FROM PUBLISHER]

For massive MIMO public channel with any sector size in either microwave or millimeter wave (mmwave) band, this paper studies the beamforming design to minimize the transmit power while guaranteeing the quality of service for randomly deployed users. First the ideal beampattern is derived via Parseval Identity, based on which a beamforming design problem is formulated to minimize the gap with the idea beampattern. The problem is transformable to a multiconvex one and an iterative optimization algorithm is used to obtain the full-digital beamformer. In addition, with the help of same beampattern theorem, the power amplifier efficiency of the beamformer is improved with unchanged beampattern. Finally, the practical hybrid implementation is obtained that achieves the full-digital beamformer solution. Simulations verify the advantages of the proposed scheme over existing ones. [ABSTRACT FROM PUBLISHER]

A software-defined wireless local area network (SDWLAN) has gained significant interest recently from both the academic and industrial communities and initiated a paradigmatic reconsideration on the stereotyped management and control of current WLAN due to its flexibility and programmability. However, with the proliferation of mobile devices, efficient client association with quality of service (QoS) guarantees in the high-density SDWLAN is a very challenging issue. In this paper, we study the client association problem in the SDWLAN with new features, including centralized association, global network state awareness, seamless handoff, and flow-level association. We formulate the client association in a high-density scenario as an optimization problem aiming to minimize the interpacket delay of individual flows, based on an unsaturated and heterogeneous Markovian analytical model. Furthermore, we interpret the optimization problem as an NP-hard supermodular set function minimization problem, which is solved by two low-complexity heuristic methods, namely the greedy algorithm and the bounded local search algorithm. Through simulations, we demonstrate the effectiveness of our proposed client association solution. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the cross-layer resource optimization for wireless relay networks with dynamic node-selfishness information. The degree of intrinsic selfishness (DeIS) and the degree of extrinsic selfishness (DeES) of the relay node (RN) are defined as the effects of its own residual energy resource and the incentive in an incentive mechanism, respectively. A model of two-timescale node-selfishness dynamics is constructed to describe the time-varying characteristics of the RN's DeIS and DeES. Based on the two-timescale node-selfishness dynamics of the RNs, a two-timescale resource-optimization dynamical algorithm (TRODA) is employed to control the transmit powers of both RNs and end users (EU), as well as the flow rates of the data packets injected by all EUs. Meanwhile, the stability and the tracking error of the TRODA under the two-timescale node-selfishness dynamics are analyzed by Lyapunov theory. Our simulation results demonstrate that the two-timescale optimization algorithm is stable and has trivial tracking error. [ABSTRACT FROM AUTHOR]

In this paper, we investigate joint power allocation and relay beamforming design problem for a nonorthogonal multiple access (NOMA) amplify-and-forward (AF) relay network, which employs successive interference cancellation to decode signals. Our objective is to maximize the achievable rate of the destination, which has the best channel condition subject to achievable rate constraints at other destinations and individual transmit power constraints. We propose an alternating optimization-based algorithm where, given power allocation, we transform the problem into a second-order cone programming and, given relay beamforming, we transform the problem into a convex linear-fractional programming. When the number of destinations is two, we propose a semidefinite programming-based one-dimensional search algorithm which achieves the globally optimal solution. Simulation results demonstrate that our proposed NOMA AF relay network outperforms the conventional orthogonal multiple access AF relay network. [ABSTRACT FROM AUTHOR]

In this paper, the design of minimum Euclidean distance (MED) precoders is developed for multiple-input multiple-output non-orthogonal multiple access (MIMO-NOMA) downlink. First, we propose the MED precoding algorithm for the two-user 2\,\times\, 2 MIMO-NOMA downlink by combining the methodology of MED precoding previously developed for unicast MIMO and multicast MIMO scenarios. Then, to extend MED precoders to general multiuser scenarios, we propose two algorithms for user pairing, based on the condition number and orthogonality defect, respectively. Finally, computer simulations validate the effectiveness of the proposed MED precoders as well as the user pairing algorithms. [ABSTRACT FROM AUTHOR]

Multicarrier low-density-spreading multiple access (MC-LDSMA) is a promising multiple access technique that enables near-optimal multiuser detection. In MC-LDSMA, each user's symbol is spread over a small set of subcarriers, and each subcarrier is shared by multiple users. The unique structure of MC-LDSMA makes the radio resource allocation more challenging compared with some well-known multiple access techniques. In this paper, we study the radio resource allocation for a single-cell MC-LDSMA system. First, we consider the single-user case and derive the optimal power allocation and subcarriers partitioning schemes. Then, by capitalizing on the optimal power allocation of the Gaussian multiple-access channel, we provide an optimal solution for MC-LDSMA that maximizes the users' weighted sum rate under relaxed constraints. Due to the prohibitive complexity of the optimal solution, suboptimal algorithms are proposed based on the guidelines inferred by the optimal solution. The performance of the proposed algorithms and the effect of subcarrier loading and spreading are evaluated through Monte Carlo simulations. Numerical results show that the proposed algorithms significantly outperform conventional static resource allocation, and MC-LDSMA can improve the system performance in terms of spectral efficiency and fairness in comparison with orthogonal frequency-division multiple access. [ABSTRACT FROM PUBLISHER]

Small cells are introduced to cellular systems to enhance coverage and improve capacity. Densely deploying small cells can not only offload the traffic of macrocells but also provide an energy- and cost-efficient way to meet the sharp increase in traffic demands in mobile networks. However, such a cell deployment paradigm also leads to heterogeneous network (HetNet) infrastructure and raises new challenges for cell planning. In this paper, we study the cell planning issue in the HetNet. Our optimization task is to select a subset of candidate sites to deploy macro or small cells to minimize the total cost of ownership (TCO) or the energy consumption of the cellular system while satisfying practical constraints. We introduce approximation algorithms to cope with two different cell-planning cases, which are both NP-hard. First, we discuss the macrocell-only case. Our proposed algorithm achieves an approximation ratio of O(\log R) in this scenario, where R is the maximum achievable capacity of macrocells. Then, we introduce an O(\log \widetildeR)-approximation algorithm to the small-cell scenario, where \widetildeR is the maximum achievable capacity of a macrocell with small cells overlaid on it. Numerical results indicate that the HetNet can significantly reduce the TCO and the energy consumption of the cellular system. [ABSTRACT FROM PUBLISHER]

To efficiently trade off system sum rate and link fairness, power allocation in wireless spectrum-sharing networks often concentrates upon proportional fairness. The corresponding problem has been proved to be convex for a single tone but NP-hard for more than two tones. However, in the two-tone case, the complexity of the problem for achieving proportional fairness has not been addressed yet. In this paper, we prove that the issue of proportional-fairness optimization for the two-tone situation is NP-hard by reducing the problem of finding the maximum independent set in an undirected graph to it. Moreover, a computationally efficient algorithm is proposed to provide an efficient suboptimal solution. Simulation results are presented to illustrate the effectiveness of our proposal. [ABSTRACT FROM PUBLISHER]

This paper investigates simultaneous wireless information and power transfer (SWIPT) for a multiple-input–single-output (MISO) secrecy channel. First, transmit beamforming without artificial noise (AN) is designed to achieve the secrecy rate maximization problem subject to the transmit power and the energy harvesting (EH) constraints. This problem is not convex, but this can be solved by applying a bisection search to a sequence of the associated power minimization problems, each of which can be solved by using a novel relaxation approach. Moreover, we extend our proposed algorithms to the robust case by incorporating channel uncertainties. Then, transmit beamforming with AN is investigated for the secrecy rate maximization problem, where two relaxation approaches are presented, i.e., a two-level optimization algorithm and a successive convex approximation (SCA), to solve the secrecy rate maximization problem. In addition, tightness analyses of rank relaxation are provided to show that the optimal transmit covariance matrix exactly returns rank one. Simulation results are provided to validate the performance of the proposed algorithms. [ABSTRACT FROM PUBLISHER]

In this paper, we study energy-efficient power allocation in distributed antenna systems (DASs). Unlike previous works that assumed perfect channel-state information (CSI) at the transmitter, we investigate a more practical case, where only the slowly varying large-scale CSI is known, to reduce the system overhead for CSI acquisition. Under a generalized DAS model, the power allocation problem for energy-efficiency (EE) maximization is formulated as a nonconvex fractional programming problem. Using the random matrix theory, the nonlinear fractional programming theory, and the saddle-point theory, we propose an iterative algorithm that achieves the global optimal solution. We also show that deploying the antennas in a distributed manner may enlarge the region where the optimal EE and the optimal spectral efficiency (SE) can be simultaneously achieved. Accordingly, we suggest the DAS-like way to densify the future 5G and beyond cellular networks to achieve EE and SE simultaneously. [ABSTRACT FROM PUBLISHER]

The increasing densification of telecommunications mobile networks raises the compelling problem of exposure to electromagnetic fields (EMF). In this paper, we address the particular concept of heterogeneous networks, where small cells (SCs) are deployed to build up a second layer to the existing macrocell network. SC deployment is shown to be particularly effective in reducing EMF exposure, in addition to capacity enhancement. Previous studies on EMF have focused on the uplink (UL) and downlink (DL) components separately, although they are strongly correlated. Relying on the definition of the exposure index (EI) from the European FP7 Lexnet project, we evaluate the impact on EMF exposure due to SC deployment. Guidelines for EMF-efficient deployment of SCs are first presented and evaluated. Then, a new enhanced intercell interference coordination (eICIC) algorithm using UL and DL metrics to optimize capacity and exposure to EMF is proposed. Static and self-optimizing dynamic implementations of the solution are considered, using a utility function related to the sojourn time of the users. Time-domain and frequency-domain self-optimized eICIC are compared in terms of EMF exposure reduction and capacity enhancement. The eICIC algorithms are combined with a self-optimized coverage range expansion function leading to further reduction of the EI. The proposed solution for optimizing both capacity and exposure to EMF is compared with a capacity-driven solution based on the DL metrics only. Simulation results illustrate the performance gains obtained for the different SC deployment scenarios. [ABSTRACT FROM PUBLISHER]

In heterogeneous networks (HetNets), the load between macrocell base stations (MBSs) and small-cell base stations (SBSs) is imbalanced due to their different transmission powers and locations. This load imbalance significantly impacts system performance and affects the experience of mobile users (MUs) with different priorities. In this paper, we aim to distributively optimize the user association in HetNets with various user priorities to solve the load balancing problem. Since the user association is a binary matching problem, which is NP-hard, we propose a distributed belief propagation (BP) algorithm to approach the optimal solution. We first develop a factor graph model, using the network topology, to represent this user association problem. With this factor graph, we propose a novel distributed BP algorithm by adopting the proportional fairness as the objective. Next, we theoretically prove the existence of the fixed point in our BP algorithm. To be more practical, we develop an approximation method to significantly reduce the computational and communication complexity of the BP algorithm. Furthermore, we analyze some properties of the factor graph relevant to the performance of the BP algorithm using the stochastic geometry. Simulation results show that 1) the proposed BP algorithm well approaches the optimal system performance and achieves a much better performance compared with other association schemes and that 2) the analytical results on the average degree distribution and sparsity of the factor graph match with those obtained from the Monte Carlo simulations. [ABSTRACT FROM PUBLISHER]

Direct device-to-device (D2D) links are proposed as a possible enabler for vehicle-to-vehicle (V2V) communications, where the incurred intracell interference and the stringent latency and reliability requirements are challenging issues. In this paper, we investigate the radio resource management problem for D2D-based V2V communication. First, we analyze and transform the latency and reliability requirements of V2V communication into optimization constraints that are computable using only the slowly varying channel information. This transformation opens up the possibility of extending certain existing D2D techniques to cater to V2V communication. Second, we propose a problem formulation that fulfills the different requirements of V2V communication and traditional cellular communication. Moreover, a Separate resOurce bLock and powEr allocatioN (SOLEN) algorithm is proposed to solve this problem. Finally, simulations are presented to evaluate different schemes, which illustrate the necessity of careful design when extending D2D methods to V2V communication, as well as show promising performance of the proposed SOLEN algorithm. [ABSTRACT FROM AUTHOR]

Multistream carrier aggregation (MSCA) is a promising technique to boost data rates by enabling users to connect with multiple base stations (BSs) simultaneously. In this paper, we investigate joint user association and resource allocation for MSCA systems to achieve energy efficiency (EE) balance among different BSs. We aim at maximizing the weighted summation of EE values for different BSs, which is modeled as a nonconvex combinatorial sum-of-ratios optimization problem. To develop an upper bound algorithm, we first relax the combinatorial variables and then transform the problem into a series of convex optimization problems by the successive convex approximation (SCA) method. We also propose a low-complexity suboptimal algorithm, which divides the original optimization problem into two steps: user association and channel allocation, as well as power allocation. Numerical results show that the proposed algorithms can achieve flexible EE tradeoff among BSs, and the EE can be significantly improved with MSCA. [ABSTRACT FROM AUTHOR]

This paper presents a methodology of calculating the optimal torque and speed commands for the engine–generator system of a series hybrid electric vehicle (HEV). In series HEVs, the engine–generator subsystem provides electrical energy to the dc link. This paper proposes an optimal control strategy of the engine–generator subsystem to generate a desired amount of energy within a given period of time. The optimization algorithm, based on trajectory optimization, determines the torque and speed reference signals for the engine–generator subsystem that achieve maximum efficiency. A simplified version of the controller is also presented for online implementation. The proposed control strategy is compared with nonoptimized control techniques, and simulation results show the improvements in energy efficiency. [ABSTRACT FROM PUBLISHER]

This paper studies the performance of a wireless sensor network (WSN) in the context of binary detection of a deterministic signal. This paper aims to develop a numerical solution for the optimal power allocation scheme via a variation of the biogeography-based optimization (BBO) algorithm, which is called the constrained BBO-DE algorithm. This new stochastic optimization algorithm is a hybridization of a very recently proposed stochastic optimization algorithm, i.e., the BBO algorithm, with another popular stochastic optimization algorithm called the differential evolution (DE) algorithm. The objective is to minimize the total power spent by the whole sensor network under a desired performance criterion, which is specified as the detection error probability. The proposed algorithm has been tested for several case studies, and its performances are compared with those of two constrained versions of the BBO and DE algorithms. [ABSTRACT FROM PUBLISHER]

This paper tackles a power minimization problem of interleave-division multiple-access (IDMA) systems over a fading multiple-access channel. The problem is minimizing the total power received by the receiver while keeping the bit error rates (BERs) of all users below a predefined value. The original formulation of the problem has highly nonlinear and implicitly defined functions, which render most existing optimization methods incapable. A new formulation is proposed in this paper, whose solution can effectively be obtained by a branch-and-bound (B&B) technique. An algorithm is devised based on B&B, and its effectiveness is also demonstrated by numerical experiments of systems with a moderate numbers of users. [ABSTRACT FROM AUTHOR]

We consider a wireless ad hoc network where each node employs a single-beam directional antenna and is provisioned with limited energy. We are interested in an online routing algorithm for successive multicast communication requests with the aim of maximizing the network lifetime. The beam-forming property, which is associated with single-beam directional antennas, introduces some unique problems that do not exist for omnidirectional antennas and, therefore, significantly increases the design space for routing algorithms. The contributions of this paper are twofold. First, we provide some important theoretical understanding on various multicast problems and deduce that even an offline version of this problem is NP-hard. Second, we develop a highly competitive online routing algorithm that takes the network lifetime consideration directly into iterative calculations and show that an algorithm that is designed under this methodology provides consistently better performance than the current state-of-the-art algorithm that only considers remaining energy. The theoretical results and routing algorithm in this paper offer some important insights on algorithm design for energy-constrained wireless ad hoc networks with directional antennas. [ABSTRACT FROM AUTHOR]

This paper focuses on precoding design for sum-rate maximization while considering the effects of residual self-interference for multiuser multiple-input–multiple-output (MU-MIMO) full-duplex (FD) systems. The problem formulation leads to a nonconvex matrix-variable optimization problem, where we develop two efficient sum-rate maximization algorithms using sequential convex programming (SCP), namely, the difference of convex functions (DC)-based and the sequential convex approximations for matrix-variable programming (SCAMP) algorithms. In addition, we derive the achievable sum rate under the effect of residual self-interference. Simulation results show that, even in cases of high self-interference and high estimation error, the SCAMP algorithm provides approximately 20%–30% sum-rate improvements over both conventional optimized half-duplex (HD) transmission and the existing state-of-the-art FD algorithm in a wide range of scenarios. Finally, the convergence results indicate that the DC-based algorithm tends to initially give the best performance; however, at convergence, the SCAMP algorithm tends to significantly outperform the other algorithms. [ABSTRACT FROM AUTHOR]

In this paper, we consider the problem of power control for sum-rate maximization on multiple interfering links [transmitter–receiver pairs (Tx–RX)] under a sum power constraint. We consider a single-frequency network, where all pairs are operating in the same frequency band, thereby creating interference with each other. We study the power-allocation problem for sum-rate maximization with and without quality-of-service (QoS) requirements on individual links. When the objective is only sum-rate maximization without QoS guarantees, we develop an analytic solution to decide optimal power allocation for a two-TX–RX-pair problem. We also develop a low-complexity iterative algorithm for a three-TX–RX-pair problem. For a generic N>\3 TX–RX pair problem, we develop two low-complexity suboptimal power-allocation algorithms. The first algorithm is based on the idea of making clusters of two or three TX–RX pairs and then leveraging the power-allocation results obtained for the two- and three-TX–RX-pair problems. The second algorithm is developed by using a high signal-to-interference-plus-noise ratio (SINR) approximation, and this algorithm can be also implemented in a distributed manner by individual TXs. We then consider the same problem but with additional QoS guarantees for individual links. We again develop an analytic solution for the two-TX–RX-pair problem and a distributed algorithm for N>\2 TX–RX pairs. [ABSTRACT FROM PUBLISHER]

In cooperative communication networks, owing to the nodes' arbitrary geographical locations and individual oscillators, the system is fundamentally asynchronous. Such a timing mismatch may cause rank deficiency of the conventional space–time codes and, thus, performance degradation. One efficient way to overcome such an issue is the delay-tolerant space–time codes (DT-STCs). The existing DT-STCs are designed assuming that the transmitter has no knowledge about the channels. In this paper, we show how the performance of DT-STCs can be improved by utilizing some feedback information. A general framework for designing DT-STC with limited feedback is first proposed, allowing for flexible system parameters such as the number of transmit/receive antennas, modulated symbols, and the length of codewords. Then, a new design method is proposed by combining Lloyd's algorithm and the stochastic gradient-descent algorithm to obtain optimal codebook of STCs, particularly for systems with linear minimum-mean-square-error receiver. Finally, simulation results confirm the performance of the newly designed DT-STCs with limited feedback. [ABSTRACT FROM PUBLISHER]

In the area of energy-efficient (EE) resource allocation, only limited work has been done on consideration of both transmitter and receiver energy consumption. In this paper, we propose a novel EE resource-allocation scheme for orthogonal frequency-division multiple-access (OFDMA) networks, where both transmitter energy consumption and receiver energy consumption are considered. In addition, different quality-of-service (QoS) requirements, including minimum-rate guarantee service and best effort service, are taken into account. The time slot, subcarrier (frequency), and power-allocation policies are jointly considered to optimize system EE. With all these considerations, the EE resource-allocation problem is formulated as a mixed combinatorial and nonconvex optimization problem, which is extremely difficult to solve. To reduce the computational complexity, we tackle this problem in three steps. First, for a given power allocation, we obtain the time–frequency resource unit (RU) allocation policy via binary quantum-behaved particle swarm optimization (BQPSO) algorithm. Second, under the assumption of known RU allocation, we transform the original optimization problem into an equivalent concave optimization problem and obtain the optimal power-allocation policy through the Lagrange dual approach. Third, an iteration algorithm is developed to obtain the joint time–frequency power–resource-allocation strategy. We validate the convergence and effectiveness of the proposed scheme by extensive simulations. [ABSTRACT FROM PUBLISHER]

In this paper, we investigate the energy-efficient job-dispatching algorithm for transcoding as a service (TaaS) in a multimedia cloud. We aim to minimize the energy consumption of service engines in the cloud while achieving low delay for TaaS. We formulate the job-dispatching problem as a constrained optimization problem under the framework of Lyapunov optimization. Using the drift-plus-penalty function, we propose an online algorithm that dispatches the transcoding jobs to service engines, with an objective to Reduce Energy consumption while achieving the QUEue STability (REQUEST). We first characterize the fundamental tradeoff between energy consumption and queue delay for the REQUEST algorithm numerically and obtain its performance bound theoretically. Second, we study the robustness of the REQUEST algorithm, with numerical results indicating that the REQUEST algorithm is robust to the inaccuracy of estimating the transcoding time. Third, we compare the performance of the REQUEST algorithm with the other two algorithms, i.e., the Round Robin and Random Rate algorithms. By simulation and real trace data, we show that by appropriately choosing the control variable, the REQUEST algorithm outperforms the Round Robin and Random Rate algorithms, with smaller time average energy consumption and time average queue length. The proposed REQUEST algorithm can be applied in cloud-assisted multimedia transcoding service. [ABSTRACT FROM PUBLISHER]

High-speed railways (HSRs) have been widely deployed all over the world in recent years. Different from traditional cellular communications, the high mobility of HSR communication makes it essential to implement power allocation (PA) along time. In the HSR case, the transmission rate greatly depends on the distance between the base station (BS) and the train. As a result, the train receives a time-varying data rate service when passing by a BS. It is clear that the most efficient PA will spend all the power when the train is nearest the BS, which will cause great unfairness along time. On the other hand, channel inversion allocation achieves the best fairness in terms of constant rate transmission. However, its power efficiency is much lower. Therefore, power efficiency and fairness along time are two incompatible objects. For the HSR cellular system considered in this paper, a tradeoff between the two is achieved by proposing a temporal proportional-fair PA scheme. In addition, a near-optimal closed-form solution and one algorithm finding \epsilon-optimal allocation are presented. [ABSTRACT FROM PUBLISHER]

Joint scheduling and resource allocation in uplink orthogonal frequency-division multiplexing (OFDM) systems is complicated and even becomes intractable with a large subcarrier and a large user number. This paper investigates the resource allocation for OFDM-based multiuser multiple-access relay channels (MARCs) with network coding. We formulate a joint optimization problem considering source-node pairing, subcarrier assignment, subcarrier pairing, and power allocation to maximize the sum rate under per-user power constraints. The problem is solved in polynomial time by optimizing three separate subproblems. To further reduce the complexity, three low-complexity suboptimal algorithms are then proposed when fixing partial resource. The simulation results show the performance gains of the proposed algorithms versus per-node transmit power and source-node number, and the impact of relay location is also evaluated. [ABSTRACT FROM PUBLISHER]

This paper investigates the resource-sharing problem in vehicular networks, including both vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication links. A novel underlaying resource-sharing communication mode for vehicular networks is proposed, in which different V2V and V2I communication links are permitted to access the same resources for their individual data transmission. To solve the resource-sharing problem in vehicular networks, we, for the first time, apply graph theory and propose the following two interference graph-based resource-sharing schemes: 1) the interference-aware graph-based resource-sharing scheme and 2) the interference-classified graph-based resource-sharing scheme. Compared with the traditional orthogonal communication mode in vehicular networks, the proposed two resource-sharing schemes express better network sum rate. The utility of the proposed V2V and V2I underlaying communication mode and the two proposed interference graph-based resource-sharing schemes are verified by simulations. [ABSTRACT FROM PUBLISHER]

Joint channel and rate allocation with power minimization in orthogonal frequency-division multiple access (OFDMA) has attracted extensive attention. Most of the research has dealt with the development of suboptimal but low-complexity algorithms. In this paper, the contributions comprise new insights from revisiting tractability aspects of computing the optimum solution. Previous complexity analyses have been limited by assumptions of fixed power on each subcarrier or power-rate functions that locally grow arbitrarily fast. The analysis under the former assumption does not generalize to problem tractability with variable power, whereas the latter assumption prohibits the result from being applicable to well-behaved power-rate functions. As the first contribution, we overcome the previous limitations by rigorously proving the problem's NP-hardness for the representative logarithmic rate function. Next, we extend the proof to reach a much stronger result, namely, that the problem remains NP-hard, even if the channels allocated to each user are restricted to be a consecutive block with given size. We also prove that, under these restrictions, there is a special case with polynomial-time tractability. Then, we treat the problem class where the channels can be partitioned into an arbitrarily large but constant number of groups, each having uniform gain for every individual user. For this problem class, we present a polynomial-time algorithm and provide its optimality guarantee. In addition, we prove that the recognition of this class is polynomial-time solvable. [ABSTRACT FROM PUBLISHER]

Femto base stations (FBSs) are cost-effective choices for wireless service providers (WSPs) to provide better indoor coverage and capacity in cellular networks. With the introduction of femtocells, spectrum allocation among macro base stations (MBSs) and numerous FBSs becomes challenging. WSPs will allocate resources among hierarchical infrastructures to mitigate mutual interference and maximize WSPs' revenue from service provisioning. In this paper, we propose a channel allocation and service provision framework to help WSPs allocate resources and increase revenue. The spectrum allocation and revenue maximization problem is formulated as a convex problem, and a centralized method is proposed to efficiently solve it. We also propose a distributed algorithm called LD (based on Lagrangian decomposition) to achieve a near-optimal solution with low computational and communication complexity, which can be used when the system is on a large scale. The simulation results show that our scheme can significantly improve the system's revenue. [ABSTRACT FROM PUBLISHER]

In this paper, resource allocation for energy-efficient secure communication in an orthogonal frequency-division multiple-access (OFDMA) downlink network is studied. The considered problem is modeled as a nonconvex optimization problem that takes into account the sum-rate-dependent circuit power consumption, multiple-antenna eavesdropper, artificial noise generation, and different quality-of-service (QoS) requirements, including a minimum required secrecy sum rate and a maximum tolerable secrecy outage probability. The power, secrecy data rate, and subcarrier allocation policies are optimized for maximization of the energy efficiency of secure data transmission (bit/joule securely delivered to the users). The considered nonconvex optimization problem is transformed into a convex optimization problem by exploiting the properties of fractional programming, which results in an efficient iterative resource allocation algorithm. In each iteration, the transformed problem is solved by using dual decomposition. Simulation results illustrate that the proposed iterative resource allocation algorithm not only converges in a small number of iterations but maximizes the system energy efficiency and guarantees a nonzero secrecy data rate for the desired users as well. In addition, the obtained results unveil a tradeoff between energy efficiency and secure communication. [ABSTRACT FROM PUBLISHER]

Algorithms that jointly allocate resources across different layers are envisioned to boost the performance of wireless systems. Recent results have revealed that two of the most important parameters that critically affect the resulting cross-layer designs are channel- and queue-state information (QSI). Motivated by these results, this paper relies on stochastic convex optimization to develop optimal algorithms that use instantaneous fading and queue length information to allocate resources at the transport (flow-control), link, and physical layers. Focus is placed on a cellular system, where an access point exchanges information with different users over flat-fading orthogonal channels. Both uplink and downlink setups are considered. The allocation strategies are obtained as the solution of a constrained utility maximization problem that involves average performance metrics. It turns out that the optimal allocation at a given instant depends on the instantaneous channel-state information (CSI) and Lagrange multipliers, which are associated with the quality-of-service (QoS) requirements and the operating conditions of the system. The multipliers are estimated online using stochastic approximation tools and are linked with the window-averaged length of the queues. Capitalizing on those links, queue stability and average queuing delay of the developed algorithms are characterized, and a simple mechanism is devised to effect delay priorities among users. [ABSTRACT FROM PUBLISHER]

This paper is concerned with the problem of relay assignment in cooperative wireless networks having multiple sources, multiple relays, and a single destination. With the objective of maximizing the minimum capacity among all sources in the network, a mixed-integer linear programming (MILP) problem is formulated, which can be solved by standard branch-and-bound algorithms. To reduce computational complexity, a greedy solution in the form of a lexicographic bottleneck assignment algorithm is proposed. Simulation results obtained for the IEEE 802.16j uplink scenarios show that the greedy algorithm performs very close to the optimal solution but at a much lower computational cost. The proposed greedy solution can also be tailored to provide further improvements on other network performance criteria. [ABSTRACT FROM PUBLISHER]

Spectral efficiency has improved significantly with the deployment of beyond third generation (3G) cellular air interfaces. However, the scarcity of unallocated radio spectrum bands, coupled with the need to provide ubiquitous wireless data services with different Quality of Service (QoS) requirements to a large number of users, has continued to drive extensive research efforts in radio resource management (RRM). In order to adapt to the changing wireless channel conditions and meet the varying and diverse QoS requirements, much of the published work in RRM has focused on exploiting multi-user and multi-channel diversities and more recently on exploiting multi-application diversity to take advantage of the mechanisms and optimization features introduced in the air interfaces. In this paper, we propose a bitQoS-aware resource allocation framework to increase the flexibility and granularity of the resource allocation algorithms by adaptively matching the QoS requirements of the user application bits to the characteristics of the Orthogonal Frequency Division Multiplexing (OFDM) subcarriers in a mixed-traffic environment. We show through an adaptive joint subcarrier, power and bit allocation algorithm, that with the finesse control of bitQoS-aware scheduling, it is possible to simultaneously achieve both an increase in user throughput and a reduction in user packet drop probability by accepting a within scheduling delay threshold increase in user latency. The performance gains obtainable are quantified in terms of system throughput, user throughput, user latency, user jitter and user packet drop probability for systems under different traffic loads. [ABSTRACT FROM PUBLISHER]

In this paper, we propose a set of joint transmit diversity selection (TDS) and relay selection (RS) algorithms based on discrete iterative stochastic optimization for the uplink of cooperative multiple-input–multiple-output (MIMO) systems. Decode-and-forward (DF) and amplify-and-forward (AF) multirelay systems with linear minimum mean square error (MSE), successive interference cancelation, and adaptive reception are considered. The problems of TDS and RS are expressed as MSE and mutual information (MI) joint discrete optimization problems and solved using iterative discrete stochastic algorithms. Such an approach circumvents the need for exhaustive searching and results in a range of procedures with low complexity and increased speed of convergence that can track the optimal selection over an estimated channel. The proposed schemes are analyzed in terms of their complexity, convergence, and diversity benefits and are shown to be both stable and computationally efficient. Their performance is then evaluated via MSE, MI, and bit error rate comparisons and shown to outperform conventional cooperative transmission and, in the majority of scenarios, match that of the optimal exhaustive solution. [ABSTRACT FROM PUBLISHER]

In this paper, we investigate the problem of fair resource allocation based on the Nash bargaining solution (NBS) over wireless amplify-and-forward (AF) relay networks in which variable-rate users and constant-rate users coexist. Via the dual decomposition method, a distributed algorithm, including relay selection, relay power allocation, and rate adaptation, is proposed. When the number of users is large and/or the rate constraints are stringent, the problem becomes infeasible, and admission control is necessary. We formulate the admission control and fair resource allocation problem as a two-stage optimization problem, aiming to first find the maximal number of users the network can support with guaranteed rates and then allocate resources based on NBS for the admitted users. Since the problem is combinatorially hard, we transform it into an equivalent one-stage optimization problem, which can be solved by existing methods but with a higher computational complexity. To further reduce the computational complexity, a suboptimal algorithm is then developed. Simulations verify the convergence and fairness of the distributed algorithm. Simulations also show that the suboptimal algorithm achieves an approximate optimal performance but with a significant running time reduction. [ABSTRACT FROM PUBLISHER]

Cognitive radio (CR) has been proposed to improve spectral efficiency while avoiding interference with licensed users. In this paper, we propose a resource allocation (RA) algorithm to perform uplink frequency allocation and power allocation among noncooperative multicells with multiuser per cell in CR systems. The maximization of the total information rate of multiple users in one cell is considered for the Rayleigh channel with path loss subject to the power constraint on each user. Since the optimization formulation for rate maximization of multiple users in each cell is an integer optimization problem, the multiple access channel (MAC) technique is proposed. With the aid of MAC, the original integer optimization problem is transformed into a concave optimization problem, thereby establishing a distributed game model in which the base station of each cell, which tries to maximize the sum rate of its own users, is a player. The proposed game-theoretic algorithm for distributed multiuser power allocation is viewed as an extension of the iterative water-filling algorithm, which is applied to distributed single-user power allocation. From the perspective of effectiveness, the proposed game-theoretic algorithm based on the MAC technique is compared with the traditional algorithm based on frequency-division multiplexing access (FDMA). Final numerical results show that the MAC-based RA algorithm can achieve more information rate and better convergence performance than the FDMA-based RA algorithm. [ABSTRACT FROM PUBLISHER]

This paper presents and analyzes a novel low-complexity reduced-rank linear interference suppression technique for direct-sequence code-division multiple access (DS-CDMA) systems based on the set-membership joint iterative optimization of receive parameters. Set-membership filtering is applied to the design and adaptation of the dimensionality-reducing projection matrix and the reduced-rank interference suppression filter. The specification of error bounds on the projection matrix and reduced-rank filter lead to the formation of two constraint sets from which estimates of the adaptive structures are selected at each time instant. The result is a low-complexity sparsely updating reduced-rank technique that does not require eigendecomposition or subspace tracking procedures. We develop least squares and stochastic gradient-type algorithms and a low-complexity rank-selection algorithm and also devise a time-varying adaptive error bound implementation. We present a stability and mean-square-error convergence analysis of the proposed algorithms along with a study of their complexity. The proposed schemes are applied to interference suppression in the uplink of a multiuser spread-spectrum DS-CDMA system, and the results confirm the validity of the analysis and the effective operation of the schemes. Performance comparisons are given against existing reduced-rank and full-rank algorithms, which act to highlight the improvements obtained by the proposed technique and algorithms. [ABSTRACT FROM PUBLISHER]

In this paper, the average rate maximization problem given transmission power constraints in a decode-and-forward (DF) relay network over slow fading channels is studied. For the problem, either a long- or short-term power constraint is imposed on the source and relays. In each combination of the power constraints at the source and relays, the original optimization problem is decomposed into subproblems, each corresponding to a specific channel gain realization. For each subproblem, a fast algorithm with closed-form solutions is provided. The case with additional peak power spectrum density constraints is also investigated. Numerical results are presented to demonstrate the effectiveness of the algorithms. [ABSTRACT FROM PUBLISHER]

In wireless sensor networks, transmission power has a significant impact on network throughput as wireless interference increases with transmission power, and interference negatively impacts the network throughput. In this paper, we try to improve the network throughput through cross-layer optimization. We first present two algorithms to compute the transmission power of each node with the objectives of minimizing the total transmission power and minimizing the total interference, respectively, from which we can obtain a network topology that ensures a connected path from each source to the sink; then, we compute the maximum achievable throughput from the obtained topology by using joint routing and link rate control. The power control algorithms can generate symmetric links or asymmetric links if so desired. Based on different link models, we use different algorithms to compute the maximum achievable throughput. Since computing the maximum throughput is an NP-hard problem, we use efficient heuristics that use a sufficient condition instead of the computationally expensive-to-get optimal condition to capture the mutual conflict relation in a collision domain. The formal proof for the sufficient condition is provided, and the proposed algorithms are compared with previous work. Simulation results show that the proposed algorithms improve the network throughput and reduce the energy consumption, with significant improvement over previous work on both aspects. [ABSTRACT FROM PUBLISHER]

This paper describes the application of a novel multiobjective self-adaptive differential evolution (MOSADE) algorithm for the simultaneous optimization of component sizing and control strategy in parallel hybrid electric vehicles (HEVs). Based on an electric assist control strategy, the HEV optimal design problem is formulated as a nonlinear constrained multiobjective problem with competing and noncommensurable objectives of fuel consumption and emissions. The driving performance requirements are considered constraints. The proposed MOSADE approach adopts an external elitist archive to retain nondominated solutions that are found during the evolutionary process. To preserve the diversity of Pareto optimal solutions, a progressive comparison truncation operator based on the normalized nearest neighbor distance is proposed. Moreover, a fuzzy set theory is employed to extract the best compromise solution. Finally, the optimization is performed over the following three typical driving cycles that are currently used in the U.S. and European communities: 1) the file transfer protocol; 2) ECE +EUDC; and 3) Urban Dynamometer Driving Schedule. The results demonstrate the capability of the proposed approach to generate well-distributed Pareto optimal solutions of the HEV multiobjective optimization design problem. The comparison with the reported results of genetic-algorithm-based weighting sum approaches and Nondominated Sorting Genetic Algorithm II reveals the superiority of the proposed approach and confirms its potential for optimal HEV design. [ABSTRACT FROM PUBLISHER]

This paper presents a novel adaptive reduced-rank multiple-input–multiple-output (MIMO) equalization scheme and algorithms based on alternating optimization design techniques for MIMO spatial multiplexing systems. The proposed reduced-rank equalization structure consists of a joint iterative optimization of the following two equalization stages: 1) a transformation matrix that performs dimensionality reduction and 2) a reduced-rank estimator that retrieves the desired transmitted symbol. The proposed reduced-rank architecture is incorporated into an equalization structure that allows both decision feedback and linear schemes to mitigate the interantenna (IAI) and intersymbol interference (ISI). We develop alternating least squares (LS) expressions for the design of the transformation matrix and the reduced-rank estimator along with computationally efficient alternating recursive least squares (RLS) adaptive estimation algorithms. We then present an algorithm that automatically adjusts the model order of the proposed scheme. An analysis of the LS algorithms is carried out along with sufficient conditions for convergence and a proof of convergence of the proposed algorithms to the reduced-rank Wiener filter. Simulations show that the proposed equalization algorithms outperform the existing reduced- and full- algorithms while requiring a comparable computational cost. [ABSTRACT FROM PUBLISHER]