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In concurrent spectrum sharing scenarios between radars and wireless communication systems, one strict approach to protect the radar from undesired interference is locating the base stations outside a guard (or exclusion) zone of the radar. As a result, users inside that zone experience bad communication coverage. This paper uses Reconfigurable Intelligent Surfaces (RISs) to improve the base station coverage of users in the radar exclusion zone. The paper shows, through analysis and simulation, that joint optimization of base station and RIS locations improves the probability of coverage in the exclusion zone over the shared spectrum without disturbing the radar operation. [ABSTRACT FROM AUTHOR]

With low-cost energy consumption, the reconfigurable intelligent surface (RIS) technique is a potential solution to the real-time data processing for intelligent transportation systems (ITSs). In this paper, an intelligent transmissive surface is introduced into the vehicular communications, enabling vehicle-to-infrastructure (V2I) signals to penetrate the intelligent RIS to access the base station (BS) on the opposite side of the vehicle. Considering that the vehicle-to-vehicle (V2V) communication reuses the spectrum spanned for V2I link, we investigate the ergodic capacity optimization problem for the vehicle performing V2I communications with the assistance of RIS, while meeting the low-latency and high-reliability requirements of the V2V link. The RIS transmission coefficients and power allocation of vehicles are jointly optimized, for the management of the desired and undesired vehicular communication links. Moreover, the expression of optimal phase shifts is derived in a closed-form, which reveals that the performance gain brought by RIS is proportional to the number of intelligent elements, while inversely proportional to the distance from vehicle-to-BS, in a quadratic form. Moreover, in the case of discrete phase shifts, an intelligent algorithm is proposed for the beamforming design at RIS. Afterwards, with the objective to maximize the ergodic capacity of the V2I link, the optimal power allocation is also proposed. Simulation results confirm the accuracy of the proposed resource allocation strategy, and that the system performance in terms of the ergodic V2I capacity can be significantly improved by the RIS. [ABSTRACT FROM AUTHOR]

Plane spiral orbital angular momentum (PSOAM) mode-groups (MGs) and multiple-input multiple-output non-orthogonal multiple access (MIMO-NOMA) serve as two emerging techniques for achieving high spectral efficiency (SE) in the next-generation networks. In this paper, a PSOAM MGs based multi-user MIMO-NOMA system is studied, where the base station transmits data to users by utilizing the generated PSOAM beams. For such scenario, the interference between users in different PSOAM mode groups can be avoided, which leads to a significant performance enhancement. We aim to maximize the energy efficiency (EE) of the system subject to the constraints of the total transmission power and the minimum data rate. This designed optimization problem is non-convex owing to the interference among users, and hence is quite difficult to tackle directly. To solve this issue, we develop a dual layer resource allocation algorithm where the bisection method is exploited in the outer layer to obtain the optimal EE and a resource distributed iterative algorithm is exploited in the inner layer to optimize the transmit power. Besides, an alternative resource allocation algorithm with Deep Belief Networks (DBN) is proposed to cope with the requirement for low computational complexity. Simulation results verify the theoretical findings and demonstrate the proposed algorithms on the PSOAM MGs based MIMO-NOMA system can obtain a better performance comparing to the conventional MIMO-NOMA system in terms of EE. [ABSTRACT FROM AUTHOR]

In the present paper a closed-form framework is derived for the analysis and optimization of the coverage probability (Pcov) and of the area spectral efficiency (ASE) in long-range (LoRa) networks. The proposed framework exploits stochastic geometry tools to associate the Pcov and the ASE to the end device (ED) transmit power and to the ED density. The analysis reveals the trends of the Pcov and of the ASE curves, with respect to both of the two parameters, while the robustness of the framework holds even at the asymptotic cases. Building upon the derived framework, the analysis demonstrates that no joint global optimum exists that jointly maximizes the Pcov over both parameters, suggesting that the optimization of the Pcov must be performed separately, for the two key network parameters considered. As opposed to that, the analysis demonstrates that a set of global optima exists that jointly maximize the ASE over both parameters, and these global maxima are subsequently derived in closed form. Thus, the derived framework fully characterizes the performance of LoRa networks, while defining in closed form the optimal points of operation that can be proven of significant value, for the transceiver and network design, of practical LoRa networks. [ABSTRACT FROM AUTHOR]

The combination of time division multiple access (TDMA) and non-orthogonal multiple access (NOMA), referred to as hybrid TDMA-NOMA system, is considered as a potential solution to meet the unprecedented requirements for future wireless networks. While recent resource allocation techniques aiming to individually maximize either spectral efficiency (SE) or energy efficiency (EE), this paper considers an SE-EE trade-off based technique for a hybrid TDMA-NOMA system. This design offers an additional degree of freedom in resource allocation. The proposed design is formulated as a non-convex multi-objective optimization (MOO) problem. The MOO framework is reformulated as a single-objective optimization (SOO) problem by combining the multi-objectives through a weighted-sum objective function. With this, each of the original objectives is assigned with a weight factor to reflect its importance in the design. Then, the sequential convex approximation (SCA) and a second-order cone (SOC) approach are jointly utilized to deal with the non-convexity issues of the SOO problem. Simulation results reveal that the proposed trade-off based design strikes a good balance between the objective functions, while meeting the instantaneous requirements of the system. [ABSTRACT FROM AUTHOR]

In this paper, we study the outage probability minimizing problem in a two-hop cooperative relay network. To reduce outage probability, existing studies propose many schemes for relay selection and power allocation, which are usually based on the assumption of exact channel state information (CSI). However, it is difficult to obtain perfect instantaneous CSI in practical situations where channel states change rapidly, and thus traditional methods would not perform well. Considering these factors, we turn to the emerging reinforcement learning (RL) methods for solutions. RL methods do not need any prior knowledge of CSI, but use neural network for approximation and decision after interacting with communication environment. Nevertheless, conventional RL methods, including most deep reinforcement learning (DRL) methods, cannot perform well when the search space is too large. In addition, non-stationarity is a common problem when using hierarchical reinforcement learning (HRL), which is caused by the changing behavior in different hierarchies. Therefore, we first propose a DRL framework with an outage-based reward function, which is then used as a baseline. Then, we further design an HRL framework and training algorithm. By decomposing relay selection and power allocation into two hierarchical optimization objectives, and combining on- policy and off-policy methods in the HRL framework, our method successfully address the sparse reward and non-stationary problem. Simulation results reveal that compared with traditional DRL method, the proposed HRL training algorithm can converge faster and reduce the outage probability by 8% in two-hop relay network with the same outage threshold. [ABSTRACT FROM AUTHOR]

This paper focuses on the non-convex joint optimization with a dynamic resource of multi-user for an intelligent reflecting surface-enhanced mmWave system, where all users have individual rates or quality of service requirements. Firstly, the objective function of the above non-linear problem is converted into a quadratic programming form under the quality of service constraints. Further, a multi-blocks alternating optimization framework with dynamic power allocation is proposed to obtain the maximum sum-rate, where the relaxed ADMM algorithm is adopted to tackle the optimal full-digital precoder and the corresponding passive reflecting matrix is obtained by the gradient-projection. The numerical results verify that beam optimization should be emphasized in high SNR, but joint dynamic resource allocation can further improve system performance even if the hardware dimensions reaches the limit. [ABSTRACT FROM AUTHOR]

Terahertz (THz) band has attracted considerable interest recently due to its superior high frequency and large available bandwidth. THz could act a vital part in the sixth generation (6G) mobile communication networks. In this paper, we introduce the downlink non-orthogonal multiple access (NOMA) technology into THz band small cell networks, where the total performance is optimized considering the two key enabling technologies. In order to decrease the energy consumption triggered by increasing of wireless services, we pay great attention to energy efficiency (EE) optimization and resource allocation in the THz-NOMA downlink systems by solving the subchannel assignment and power optimization. We first exploit a channel model for the THz-NOMA downlink system by using the key features of THz-NOMA networks. Then we utilize Dinkelbach-style algorithm to solve the resource allocation problem and decompose it into two subproblems. A subchannel assignment algorithm and a power optimization based on alternative direction method of multipliers (ADMM) algorithm are developed to get the solution. Finally, to embody the strengths of THz-NOMA performance, we compare our proposed schemes against the conventional schemes. Simulation results yield substantially higher EE and further prove the availability of our proposed schemes. [ABSTRACT FROM AUTHOR]

This paper investigates the performance of cooperative networks based on non-orthogonal multiple access (NOMA) with multiple full-duplex decode-and-forward relays. Taking into account rate fairness between the users and assuming imperfect successive interference cancellation (SIC), the problem is formulated so that power allocation is performed adaptively by the multiple relays in order to maximize the minimum users’ achievable rate. We demonstrate that the proposed optimization problem is convex and a closed-form expression for the power allocation factor is derived. The attained results show that the proposed scheme achieves satisfactory results in terms of achievable rate, fairness, and outage probability when compared to other adaptive power allocation strategies. [ABSTRACT FROM AUTHOR]

Precoder optimization with full channel state information for finite alphabet signals over multiple-input multiple-output random channels is investigated in this paper. The precoder is represented by a product of power allocation matrix and constellation-forming matrix. There was an optimal algorithm introduced in the literature to globally maximize the channel mutual information by iteratively optimizing these two matrices. However, the computational complexity of the optimal algorithm is painfully high, especially when it is used with the high-order modulation and the high-data stream number. In this paper, we propose a novel sub-optimal low-complexity precoding algorithm and compare it with the optimal one. The new algorithm proceeds in two steps. First, the constellation-forming matrix is fixed in order to maximize the minimum Euclidean distance between the received symbols, which ensures high channel mutual information. Then, given the constellation-forming matrix, an iterative algorithm searches for the power allocation matrix that maximizes the channel mutual information. Since optimizing only one matrix instead of two, the new algorithm not only achieves a lower computational complexity but also avoids the use of initial values, which must be carefully selected for each channel and signal-to-noise ratio for fast convergence. Another advantage of the new algorithm is that the resulting precoder has a fixed form of received constellation thanks to the fixed constellation-forming matrix. This allows us to optimize the symbol mapping on the received constellation. Simulation results show that the proposed low-complexity precoder achieves error-rate performance that is close to performance of the optimal one when the conventional mapping is used. In addition, the new precoder used with optimized mapping at received constellation shows significant error-rate performance improvement. [ABSTRACT FROM AUTHOR]

Layered transmission is studied in this paper to accommodate various device capabilities and channel conditions. The problem of efficiently broadcasting a common set of layer-encoded data from a single base station to multiple users over quasi-static fading channels is examined. Considering the maximum tolerable delay, a block-level utility is defined in terms of average probability of successful reception of each layer. Based on non-orthogonal multiple access scheme, we propose a complete algorithm to jointly optimize power allocation and transmission rates. By utilizing the idea of opportunistic scheduling, we first derive the success probability of one single layer in homogeneous scenarios, then extend the results to heterogeneous scenarios where users are subject to different channel statistics. The multi-layer optimization is realized by iteratively repeating the single-layer operations. Starting from the base layer, we first choose an optimal subset of users as the corresponding target group, then apply the single-layer allocation process until no power resources are left. Computer simulation and discussion confirm that our method maximizes the block-level utility at the same time balances the tradeoff between multiuser diversity gain and multicast gain. [ABSTRACT FROM AUTHOR]

In this paper, we study the problem of downlink (DL) sum rate maximization in codebook based multiuser (MU) multiple input multiple output (MIMO) systems. The user equipments (UEs) estimate the DL channels using pilot symbols sent by the access point (AP) and feedback the estimates to the AP over a control channel. We present a closed form expression for the achievable sum rate of the MU-MIMO broadcast system with codebook constrained precoding based on the estimated channels, where multiple data streams are simultaneously transmitted to all users. Next, we present novel, computationally efficient, minorization-maximization (MM) based algorithms to determine the selection of beamforming vectors and power allocation to each beam that maximizes the achievable sum rate. Our solution involves multiple uses of MM in a nested fashion. Based on this approach, we propose and contrast two algorithms, which we call the square-root-MM (SMM) and inverse-MM (IMM) algorithms. The algorithms are iterative and converge to a locally optimal beamforming vector selection and power allocation solution from any initialization. We evaluate the performance and complexity of the algorithms for various values of the system parameters, compare them with existing solutions, and provide further insights into how they can be used in system design. [ABSTRACT FROM AUTHOR]

In this paper, we propose a two-way cooperative relay scheme based on non-orthogonal multiple access (NOMA) where bidirectional communication is supported for two users in three phases. We jointly optimize power and time allocations to minimize system outage probability whose exact closed-form expression is derived and verified by Monte Carlo simulations. For performance comparison, four schemes based on power allocation and time allocation as well as the conventional one-way cooperative NOMA (OW-CNOMA) scheme are provided. It is shown that the proposed scheme can improve outage probability and ergodic rate performances over a conventional OW-CNOMA system. [ABSTRACT FROM AUTHOR]

In heterogeneous networks (HetNets), user association approaches should be able to achieve load balancing among base stations (BSs). This paper investigates the joint optimization of user association and resource allocation in Backhaul-constrained HetNets for capacity enhancements. We consider two major limitations in HetNets: the backhaul bottleneck of BSs and the capability of user equipment (UE). We establish a framework based on a multi-leader multi-follower Stackelberg game, in which resource allocation is formulated as a follower-level game and user association is cast as a leader-level game. Because of the backhaul bottleneck of small BSs, the given preference order of users renders the final association result unstable. Thus, the resident-oriented Gale-Shapley (GS) algorithm is included in the proposed framework to obtain a stable single-BS association. Furthermore, congestion factors are introduced to reflect the relative backhaul congestion degrees of BSs, which enables load balancing among the small BSs in the proposed algorithm. The study considers user association and resource allocation with and without limitations on the number of serving users for small BSs in HetNets. Extensive simulation results suggest that the proposed algorithm can adaptively respond to a wide variety of network situations. [ABSTRACT FROM AUTHOR]

This paper considers the problem of user association to small-cell base stations (SBSs) in a heterogeneous and small-cell network (HetSNet). Two optimization problems are investigated, namely, maximizing the set of associated users to the SBSs (the unweighted problem) and maximizing the set of weighted associated users to the SBSs (the weighted problem), under signal-to-interference-plus-noise ratio constraints. Both problems are formulated as linear integer programs. The weighted problem is known to be NP-hard, and in this paper, the unweighted problem is proved to be NP-hard as well. Therefore, this paper develops two heuristic polynomial-time algorithms to solve both problems. The computational complexity of the proposed algorithms is evaluated and is shown to be far more efficient than the complexity of the optimal brute-force (BF) algorithm. Moreover, this paper benchmarks the performance of the proposed algorithms against the BF algorithm, the branch-and-bound CPLEX-based algorithm, and state-of-the-art algorithms, through numerical simulations. The results demonstrate the close-to-optimal performance of the proposed algorithms. They also show that the weighted problem can be solved to provide solutions that are fair between users or to balance the load among SBSs. [ABSTRACT FROM PUBLISHER]

Because of its outstanding performance in extending coverage and reducing transmit power, wireless relay cooperation is deemed as one of the promising techniques to realize green-broadband communication in the future. In such relay cooperative systems, optimal power allocation not only can prolong the lifetime of users, but also is an effective way to reduce radiation in the environment. In this paper, for amplify-and-forward-relay cooperative networks, where multiple communication pairs share a common relay, we propose a novel power allocation scheme, which has considerably lower complexity compared with the existing solution schemes. The objective of this paper is to minimize sum-source-power consumption under the conditions that the transmit power of all nodes in the network is constrained and their predetermined target signal-to-noise ratios are satisfied. By providing sufficient analytical evidence, we study the optimality, convergence, and computational complexity of our proposed scheme. Through numerical simulation, we further justify the effectiveness and efficacy of our scheme compared with the existing works. [ABSTRACT FROM AUTHOR]

To facilitate the ability of phased array radar to manage both its search and track (SAT) tasks simultaneously within a predetermined illumination time budget, a resource allocation (RA) scheme for integrated SAT application is built in this paper. We formulate the RA scheme as a bi-objective constrained optimization framework, and use Pareto's theory to determine its multiple Pareto optimal solutions. With these Pareto solutions, one can find a suitable tradeoff between SAT tasks, and correspondingly choose an illumination scheme, for an arbitrary application demand. By exploiting the unique structure of the bi-objective optimization problem, we strictly prove that the multiple Pareto solutions with cardinality $M$ can be obtained by parallelly solving $M + 1$ convex minimax problems. These problems are shown to correlate with the SAT tasks independently, and are correspondingly solved by the well-known linear programming methods and a proposed minimax solving algorithm, respectively. Finally, some numerical results are provided to illustrate the effectiveness and reveal the intrinsic mechanism of the Pareto theory based bi-objective RA strategy. [ABSTRACT FROM AUTHOR]

The paper introduces a novel hybrid network coding scheme in a three-node two-way relaying network in a wireless fading environment. The proposed hybrid scheme is featured by an opportunistic mechanism which employs one of the two network coding schemes, namely physical-layer network coding (PNC) and digital network coding (DNC), according to the instant rate requirement and dynamic channel condition. We first develop a close-form expression for the power consumption of the proposed scheme, which is further used to formulate a series of optimization problems under both symmetric and asymmetric traffic scenarios. Solving the optimization problems yields important system parameters that determine the optimal power allocations, time split between uplink and downlink transmissions as well as between PNC and DNC, respectively. We will discuss the possible overhead in the real implementation of the proposed scheme. Extensive numerical experiments are conducted to compare the performance of the proposed hybrid scheme with the conventional PNC and DNC scheme, respectively. [ABSTRACT FROM AUTHOR]

This paper considers the problem of dynamic power allocation in the downlink of multi-cell networks, where each cell utilizes non-orthogonal multiple access (NOMA)-based resource allocation. Also, coordinated multi-point (CoMP) transmission is utilized among multiple cells to serve users experiencing severe inter-cell interference (ICI). Under this CoMP- NOMA framework, CoMP transmission is applied to a user experiencing less distinctive channel gain with multiple base stations (BSs)/cells (i.e., severe ICI-prone user) and non-CoMP transmission (i.e., transmission without any coordination among multiple BSs) is applied to a user experiencing dominating channel gain with only one BS/cell, while NOMA is utilized at each BS to schedule CoMP and non-CoMP users over the same transmission resources, i.e., time, spectrum and space. After discussing various CoMP- NOMA models for downlink power allocation in multi-cell networks, we focus on a joint transmission CoMP- NOMA (JT-CoMP-NOMA) model. For the JT-CoMP-NOMA model, an optimal joint power allocation problem is formulated and the solution is derived for each CoMP- set consisting of multiple cooperating BSs (i.e., CoMP BSs). To avoid the huge computational complexity of the joint power optimization approach, we propose a distributed power optimization approach at each cooperating BS whose optimal solution is independent of the solution of other coordinating BSs. The distributed solution for the joint power optimization problem is validated and numerical performance evaluation is carried out for the proposed CoMP- NOMA models including JT-CoMP-NOMA and coordinated scheduling CoMP- NOMA (CS-CoMP-NOMA). The obtained results reveal significant gains in spectral and energy efficiency in comparison with conventional CoMP- orthogonal multiple access (CoMP-OMA) systems. [ABSTRACT FROM AUTHOR]

To improve the spectral efficiency in long-term evolution systems, the resource blocks (RBs) are shared among different cells/base stations (BSs) resulting in interference among the cells/BSs on each RB, although all the sub-carriers (SCs) in an RB may not be used in a cell. Defining the load of a given BS per RB as the fraction of the active SCs in that RB, in this paper, we present a generalized signal-to-interference-and-noise-ratio (SINR) model for downlink users on a given RB. This model considers both the transmit powers of the BSs and the loads of the cells over that RB. Under this load-coupled SINR model, to study the feasibility of a given rate demand vector for users, we formulate an optimization problem of minimizing the total load of the BSs on the RBs. Then, for two different scenarios of feasible and infeasible demand vectors, respectively, we study the load management problem (i.e., minimizing the total load of the BSs on the RBs) and admission control problem (i.e., finding the sub-set of users with maximum cardinality whose demands can be concurrently satisfied), respectively. Our theoretical investigations, which provide guidelines for designing radio resource management methods for load-coupled OFDMA networks, are complemented through Monte Carlo simulations. [ABSTRACT FROM PUBLISHER]

In this paper, we propose a general framework to study the tradeoff between energy efficiency (EE) and spectral efficiency (SE) in massive multiple-input-multiple-output-enabled heterogenous networks while ensuring proportional rate fairness among users and taking into account the backhaul capacity constraint. We aim at jointly optimizing user association, spectrum allocation, power coordination, and the number of activated antennas, which is formulated as a multi-objective optimization problem maximizing EE and SE simultaneously. With the help of weighted Tchebycheff method, it is then transformed into a single-objective optimization problem, which is a mixed-integer non-convex problem and requires unaffordable computational complexity to find the optimum. Hence, a low-complexity effective algorithm is developed based on primal decomposition, where we solve the power coordination and number of antenna optimization problem and the user association and spectrum allocation problem separately. Both theoretical analysis and numerical results demonstrate that our proposed algorithm can fast converge within several iterations and significantly improve both the EE-SE tradeoff performance and rate fairness among users compared with other algorithms. [ABSTRACT FROM AUTHOR]

The exponential growth in data traffic and dramatic capacity demand in fifth generation (5G) have inspired the move from traditional single-tier cellular networks toward heterogeneous cellular networks (HCNs). To face the coming trend in 5G, the high availability requirement in new applications needs to be satisfied to achieve low latency service. Usually, these applications require an availability of six nines or even higher. In this paper, we present a tractable multitier multiband availability model for spectrum aggregation-based HCNs. We first derive a closed-form expression for the availability of spectrum aggregation-based HCNs using the signal-to-interference-plus-noise model. By doing so, we formulate two optimization problems, one is to maximize the average availability, and the other one is to minimize the average power consumption. These two optimization problems are both nonconvex problems, which are challenging to solve. To cope with them, we propose to apply genetic algorithm for the joint user equipment (UE) association, subcarrier assignment, and power allocation problem. Our results show that the average availability in spectrum aggregation-based HCNs improves with decreasing number of UEs, as well as increasing power budget ratio. We also show that increasing the maximum number of aggregated subcarriers decreases the average power consumption, but cannot guarantee the substantial improvement of average availability. [ABSTRACT FROM PUBLISHER]

This paper introduces a fair and energy-efficient resource allocation framework in spectrum-sharing wireless networks with quality-of-service guarantees. Consider the tradeoff between energy efficiency and spectral efficiency, the multiobjective problem of spectral efficiency and energy efficiency is transformed into a problem that minimizes the total power consumption and maximizes the achievable utility, subject to power constraints and rate outage probability constraints. We then analyze the complexity of the considered problem; particularly, the optimization problem is NP-Hard when $0 < \alpha < 1$ and $\alpha =0$ and is convex for other values of the fairness index $\alpha$. After that, we adopt the successive convex approximation (SCA) approach to approximate and transform the NP-hard nonconvex optimization problem into a sequence of convex programs and propose two iterative SCA-based resource allocation algorithms. Extensive simulation results are presented to demonstrate the effectiveness and outperformance of the proposed algorithms over existing frameworks. [ABSTRACT FROM PUBLISHER]

With macro and micro base stations (BSs) of different transmission and processing capabilities jointly deployed, heterogeneous networks effectively extend the coverage and capacity of wireless networks and have been adopted as one of the key technologies in Fourth-Generation-Long-Term Evolution (4G-LTE). To fully exploit the benefits of heterogeneous networks, proper mobile association, interference management, and radio resource management schemes need to be developed. In this paper, we investigate radio resource allocation for heterogeneous networks with cooperative relays, where the relay nodes (RNs) with in-band backhaul act as micro BSs and are able to serve user equipment (UE) either independently or cooperatively with the BSs. In such a network, radio resource-allocation schemes need to decide for each resource block: 1) whether a UE is served by a BS or an RN, and 2) whether a UE should be cooperatively served or not. In making these decisions, radio resource consumption on RN's in-band backhaul links and fairness among the UEs should be taken into consideration, which further complicates the resource-allocation problem. In this paper, we propose a radio resource-allocation framework and derive a resource-allocation strategy that is asymptotically optimal on the proportional fairness metric. The derived resource-allocation scheme gives insights on the optimal radio resource allocation for the heterogeneous networks with cooperative RNs using in-band backhauls. [ABSTRACT FROM PUBLISHER]

Traditional cellular networks are moving toward heterogeneous cellular networks (HetNets) to satisfy the stringent demand for data rates and capacity. To enable the new applications in 5G, such as haptic communications, we face new challenges of achieving high availability with low latency in HetNets. In this paper, we introduce coordinated multi-point (CoMP) and carrier aggregation (CA) techniques in HetNets to guarantee the availability of all user equipment (UE), where the CoMP improves the single-path availability, and the CA enhances availability via multi-carrier gain combining. To characterize the availability, we first derive an exact closed-form expression for the availability of a random UE in a CoMP and CA-enabled HetNets. To achieve the maximum UE availability, we formulate a max-min optimization problem. To solve it, we then propose a two-step optimization algorithm (TSOA) and a joint (JTOA). The TSOA is based on heuristic algorithm for the optimal subcarrier assignment and UE association, and based on the Lagrangian dual method for the power allocation. The JTOA is based on genetic algorithm to achieve the interaction between the first step and the second step. Our results showcase the effective of our proposed JTOA, and the effective of the CoMP in availability improvement in HetNets. [ABSTRACT FROM PUBLISHER]

In this paper, we consider the power-allocation problem in orthogonal frequency-division multiplexing (OFDM)-based bidirectional cognitive radio (CR) relay networks. A joint optimization under the amplify-and-forward (AF) relaying protocol is formulated to maximize the throughput of secondary users (SUs) via power loading over different subcarriers at the three (two transceivers and a relay) nodes. For compatibility with practical systems, separate power constraints are considered at each transmitting node. Furthermore, to provide high-level protection to primary users (PUs) in underlay CR transmission, per-subcarrier-based interference constraints are assumed. For tractability of the solution, we adopt a decomposition framework where the joint power optimization (JPO) is split into two subproblems. Later, each subproblem is solved by exploiting the convex optimization techniques. Moreover, a low-complexity suboptimal solution of the joint power allocation at all nodes is also proposed. Simulation results are presented to validate the effectiveness of the proposed schemes. [ABSTRACT FROM AUTHOR]

In this paper, the problem of optimal power allocation over flat fading additive white Gaussian noise channels is considered for maximizing the average detection probability of a signal emitted from a power constrained transmitter in the Neyman–Pearson framework. It is assumed that the transmitter can perform power adaptation under peak and average power constraints based on the channel state information fed back by the receiver. Using results from measure theory and convex analysis, it is shown that this optimization problem, which is in general nonconvex, has an equivalent Lagrangian dual that admits no duality gap and can be solved using dual decomposition. Efficient numerical algorithms are proposed to determine the optimal power allocation scheme under peak and average power constraints. Furthermore, the continuity and monotonicity properties of the corresponding optimal power allocation scheme are characterized with respect to the signal-to-noise ratio for any given value of the false alarm probability. Simulation examples are presented to corroborate the theoretical results and illustrate the performance improvements due to the proposed optimal power allocation strategy. [ABSTRACT FROM PUBLISHER]

In this paper, we consider the relay selection and power allocation (RSPA) problem in amplify-and-forward (AF) relaying networks. The objective is to minimize the outage probability at the receiver, assuming that only the mean channel gain information is available. By taking advantage of its special structure, we propose to decompose the RSPA problem into two tractable subproblems, which can be implemented in a semidistributed manner. For the relay selection (RS) subproblem, a relay-ordering-based scheme is designed to incrementally select a subset of relays from the candidate nodes according to a specific metric in a centralized manner, which is dominated by the mean channel gain of each individual nodes. For the power allocation (PA) subproblem, a source-driven iterative algorithm is proposed for each relay to decide its optimal transmit power locally by exploiting the convexity of this subproblem. Simulation results are provided to evaluate the outage and signal-to-noise-ratio (SNR) performance of the proposed scheme under different channel conditions. It is shown that the proposed scheme can dynamically select relays and adjust power allocation according to the SNR and channel conditions. It outperforms the other two benchmark schemes, and the performance is close to the optimal scheme with exhaustive search, but the computational complexity is significantly reduced. [ABSTRACT FROM AUTHOR]

Growing attention has been paid to renewable- or hybrid-energy-powered heterogeneous networks (HetNets). In this paper, focusing on backhaul-aware joint user association and resource allocation for this type of HetNets, we formulate an online optimization problem to maximize the network utility reflecting proportional fairness. Since user association and resource allocation are tightly coupled not only on resource consumption of the base stations (BSs) but in the constraints of their available energy and backhaul as well, the closed-form solution is quite difficult to obtain. Thus, we solve the problem distributively by employing certain decomposition methods. Specifically, at first, by adopting the primal decomposition method, we decompose the original problem into a lower level resource-allocation problem for each BS and a higher level user-association problem. For the optimal resource allocation, we prove that a BS either assigns equal normalized resources or provides an equal long-term service rate to its served users. Then, the user-association problem is solved by the Lagrange dual decomposition method, and a completely distributed algorithm is developed. Moreover, applying results of the subgradient method, we demonstrate the convergence of the proposed distributed algorithm. Furthermore, to efficiently and reliably apply the proposed algorithm to the future wireless networks with an extremely dense BS deployment, we design a virtual user association and resource allocation scheme based on the software-defined networking architecture. Finally, numerical results validate the convergence of the proposed algorithm and the significant improvement on network utility, load balancing, and user fairness. [ABSTRACT FROM PUBLISHER]

Heterogeneous network deployment is considered to be among the most promising approaches to meet the demand for increasing communication capacity. The concept is that of increasing the number of cells while reducing their size to provide different layers of coverage. This paper proposes a coordinated method to face the interlayer and intralayer interference caused by the overlapping of heterogeneous cells. The aim is to exploit the benefits of both enhanced intercell interference coordination (eICIC) and coordinated multipoint (CoMP) approaches and to limit, at the same time, their drawbacks by means of their optimized joint use. The idea devised here is to build an ad hoc CoMP system on top of a basic eICIC mechanism. Therefore, the analysis focuses on the dynamic selection of users and cells involved in CoMP operations, with the goal of minimizing the unfulfilled data rate requests (UDRRs). Due to the computational complexity of the optimal solution, we propose a heuristic procedure and validate its accuracy by providing a comparison with the optimal numerical solution. In particular, our results show that the proposed method outperforms other benchmark solutions in terms of UDRRs and signaling overhead. [ABSTRACT FROM PUBLISHER]

In this paper, the energy efficiency (EE) of multihop relaying over Nakagami- $m$ fading channels is investigated. The “consumption factor” (CF), which is adopted as a metric to evaluate the EE, is derived for both amplify-and-forward and decode-and-forward relaying strategies. Then, based on the obtained expressions, we propose a power-allocation (PA) strategy maximizing the CF. In addition, a suboptimal low-complexity PA algorithm is proposed and analyzed, and the obtained PA scheme is compared in terms of EE with other PA schemes from the literature. Analytical and simulation results confirm the accuracy of our derivations and assess the performance gains of the proposed approach. [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]

The ultimate efficiency of a wireless network is achieved when various resources are allocated in a joint optimization process. This paper proposes the optimal solution for a joint power allocation and relay assignment (JPARA) problem subject to the sum-power constraint at the source node. This formulation is applicable to different system models. Specifically, we assume a network consisting of one source node, one destination node, and $N$ relays, where the source node uses orthogonal frequency-division multiplexing (OFDM) to transmit its information to the destination. Each relay should be assigned to one of the subcarriers, and the source node should distribute its power among the subcarriers. We propose an algorithm to find the JPARA solution based on the max-min criterion. The optimality of the proposed algorithm is analytically proved, and its complexity is calculated to be $O(N^4)$. We show that our proposed algorithm offers significant improvement in the performance of the worst end-to-end link compared with the separate optimization case. To further improve the performance, a new approach is proposed, in which each subcarrier is assigned a predetermined initial power, and the remaining power is distributed through the proposed JPARA algorithm. [ABSTRACT FROM AUTHOR]

This paper studies the information-theoretic metric of outage probability for a half-duplex asymmetric two-way amplify-and-forward (AF) relaying. Unlike current research activities, our main purpose is to examine the impact of traffic asymmetry on system outage probability. First, an exact but two-integral-form outage probability is derived over Rayleigh fading channels. To provide more insight, a closed-form asymptotic outage probability is then obtained. It is shown that the outage probability in the system is determined only by the one-way channel or related to the two-way links, depending on the asymmetric level of the two senders' target rates and the terminals' average transmission powers. Following this analysis, optimum power allocation and opportunistic relay selection are studied to optimize the overall performance of the considered AF scenario. Next, with the aid of traffic knowledge, novel power allocation and relay selection criteria are proposed, followed by an analysis of the outage probability with the relay selection. Simulation experiments are performed to validate the proposed studies in this paper. The results show that the proposed power allocation and relay selection methods can achieve significant performance gains in terms of outage probability, regardless of symmetric or asymmetric traffic and channels. [ABSTRACT FROM PUBLISHER]

This paper develops an improved amplify-and-forward (AF) orthogonal frequency-division multiplexing (OFDM) scheme, where an incremental policy is fully utilized to promote the spectrum efficiency of cooperative transmission, and presents several achievable-rate-maximizing resource allocation strategies. The high computational complexity for optimization necessitates our investigation of suboptimal approaches. However, the associated computational burdens are still intensive due to the exhaustive search nature. As a consequence, a low-complexity alternative evolves to achieve a better tradeoff between performance and complexity. Moreover, its average unconditional signal-to-noise ratio (SNR) outage probability is analytically derived. Numerical results are shown to illustrate the benefits of the proposals and verify the validity of the theoretical analysis. [ABSTRACT FROM AUTHOR]

Fifth-generation (5G) wireless networks are expected to achieve 1000 times higher capacity compared with fourth-generation wireless networks. Thus, improving the spectrum efficiency (SE) is a crucial problem, which must be considered. Cognitive radio (CR) is considered an effective approach to alleviate the spectrum scarcity problem. In this paper, based on the location information of the primary transmitter (PT) and the CR network, we estimate the distance between the PT and the secondary transmitter (ST) and then propose a joint spectrum sensing and power allocation (JSS-PA) scheme to improve the SE of the CR network. In the JSS-PA scheme, we focus on jointly optimizing the sensing parameters and the transmit power of the secondary user (SU) such that the SE is maximized, whereas the primary user (PU) outage constraint is satisfied. When cooperative spectrum sensing is employed to detect the PU's status, we analyze two cooperative strategies, i.e., soft information fusion (SIF) and hard information fusion (HIF). Under the SIF strategy, the optimization of sensing and power (S-OSP) algorithm is proposed to maximize the SE. Under the HIF strategy, the optimization of thresholds (H-OT) algorithm is proposed, and then, the optimization of sensing and power (H-OSP) algorithm is proposed to find the optimal duration of local sensing, the optimal transmit power of the SU, and the optimal final decision threshold. Finally, we present the simulation results to evaluate the performance of the proposed JSS-PA scheme and discuss the effects of the optimal parameters on different schemes under SIF and HIF strategies. [ABSTRACT FROM PUBLISHER]

This paper considers the problem of optimizing the downlink capacity in a heterogeneous home area network (HAN) with a beamforming technique at the smart meter for smart grid applications. We model the communication scenario between the smart meter and in-home appliances as a heterogeneous multiuser network. The optimal power-allocation algorithm is developed under the constraints that 1) each user satisfies individual signal-to-interference-plus-noise ratio (SINR) requirement for successful heterogeneous communication, 2) the sum of transmit power allocated to each user is equal to the permissible total transmit power at the smart meter, and 3) the allocated transmit power to each user is feasible. The optimization problem is mathematically shown to be convex, and the optimal power allocation is thus derived. By employing the theorem that the sum of independent exponentially distributed random variables follows Erlang distribution, the probability distribution function (pdf) of the smallest allocated transmit power is mathematically obtained from the properties of downlink indoor Saleh–Valenzuela (S–V) channels. It is analytically shown that the allocated transmit power has a lower limit, which is determined by the SINR threshold and the total number of active users in the HAN. Furthermore, numerical results verify the capacity performance improvements of the proposed optimal power-allocation scheme. It is also shown that beamforming technique contributes to the optimal power-allocation scheme. [ABSTRACT FROM PUBLISHER]

This paper tackles the problem of minimum-energy cooperative transmission in wireless networks under the assumption of limited channel state information at the transmitters. It is assumed that only the average statistics of the fading channels are known to the transmitters. The objective here is to jointly optimize the set of relays and the transmission powers for both the broadcasting and cooperative transmission phases while satisfying probabilistic signal-to-noise ratio (SNR) constraints at the relays and at the destination node. Increasing the broadcasting power expands the set of potential relays and decreases the required power for cooperative transmission. Hence, there is a compromise in the selection of the power values, which is addressed in this work using a chance-constrained optimization framework. A closed-form approximate solution is also presented, which provides a low-complexity transmission scheme for energy-harvesting wireless networks. Simulations are presented to demonstrate the efficacy of the proposed approach and the approximate solution. [ABSTRACT FROM AUTHOR]

Full-duplex transmission is a promising technique to enhance the capacity of relay communications. In this paper, we investigate the power and location optimization for full-duplex decode-and-forward (DF) relaying systems. The outage probability is adopted as the optimization criterion and a two-dimensional (2D) optimization problem is formulated for systems with and without the direct source–destination link. Analytical and numerical results are provided to demonstrate advantages of the power and location optimization. More interestingly, the effect of residual self-interference (RSI) introduced by full-duplex transmission is investigated in terms of the optimal power allocation and relay location deployment and the outage probability performance improvement. Furthermore, we show that the minimal outage probability can be achieved via joint power-location optimization. The effect of power optimization on the outage performance is also compared between full-duplex and half-duplex relaying. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the impact of outdated channel estimates on the overall performance of a two-way multirelay system that employs a three-phase (3P) analog network coding (ANC)-based opportunistic relay selection (ORS) scheme under Rayleigh fading. Specifically, considering the imperfect estimation and the time variation of the fading channels, we formulate the 3P ANC-based ORS strategy for the best relay among multiple relays in terms of both outdated and imperfect channel state information (CSI). With such a practical CSI modeling, we derive a tight lower bound expression for the overall outage probability (OOP) and an upper bound expression for the ergodic sum-rate (ESR) of the considered scheme. In addition, by deriving an asymptotic outage behavior in the high signal-to-noise ratio (SNR) regime, we highlight the impact of outdated and imperfect CSI on the achievable diversity order. Moreover, based on the asymptotic analysis, we investigate three optimization problems, namely, optimal relay power allocation for fixed relay location, optimal relay location for fixed power allocation, and jointly optimal relay power allocation and location to minimize the OOP and to maximize the ESR of the considered system. Our results reveal that the optimal relay power allocation has a more significant impact on the performance when the CSI is imperfect, whereas the optimal relay location has a more noticeable impact when the CSI is outdated and unbalanced over the two hops. Furthermore, we show that the imperfect CSI can degrade system performance more severely than the outdated CSI, and it can even reduce the system diversity order to zero. The numerical and simulation results corroborate our theoretical analysis and findings and demonstrate that the 3P ANC-based ORS scheme can achieve better performance than its two-phase counterpart, depending upon the quality of the direct link, particularly in the medium-to-high-SNR regime. [ABSTRACT FROM PUBLISHER]

In this paper, we study the secure multi-antenna transmission with artificial noise (AN) under slow fading channels coexisting with randomly located eavesdroppers. We provide a comprehensive secrecy performance analysis and system design/optimization under a stochastic geometry framework. Specifically, we first evaluate the secrecy outage performance, and derive a closed-form expression for the optimal power allocation ratio of the information signal power to the total transmit power that minimizes the secrecy outage probability (SOP). Subject to a SOP constraint, we then propose a dynamic parameter transmission scheme (DPTS) and a static parameter transmission scheme (SPTS) to maximize secrecy throughput, and provide explicit solutions on the optimal transmission parameters, including the wiretap code rates, the on-off transmission threshold and the power allocation ratio. Our results give new insight into secure transmission designs. For example, secrecy rate is a concave function of the power allocation ratio in DPTS, and AN plays a significant role under SOP constraints and in dense eavesdropper scenarios. In SPTS, transmission probability is a concave function of the power allocation ratio, and secrecy throughput is a quasi-concave function of the secrecy rate. Numerical results are demonstrated to validate our theoretical analysis. [ABSTRACT FROM AUTHOR]

This paper investigates advanced energy-efficient wireless systems in orthogonal frequency-division multiple access (OFDMA) downlink networks using coordinated multipoint (CoMP) transmissions between the base stations (BSs) in a heterogeneous network (HetNet), which is adopted by Third-Generation Partnership Project (3GPP) Long-Term Evolution (LTE)-Advanced to meet International Mobile Telecommunications-Advanced targets. HetNet CoMP has received significant attention as a way of achieving spectral efficiency (SE) and energy efficiency (EE). Usually, in the literature, the total network power consumption is restricted to the sum of the power consumption of all BSs. The significance of the power consumption of the backhaul links in wireless networks is normally omitted for its trivial effect with respect to that of the radio BSs. For SE and EE analysis of HetNet CoMP, the energy and bandwidth consumption of the backhaul is considered, without which, the investigation remains incomplete. However, SE and EE are design criteria in conflict with each other, and a careful study of their tradeoff is mandatory for designing future wireless communication systems. The EE is measured as “throughput (bits) per joule,” whereas the power consumption model includes RF transmit (radiated), circuit, and backhaul power. Furthermore, a nonideal backhaul model such as a microwave link is also investigated within intra-HetNet-CoMP (inside one cell), where an implementing fiber is not feasible. An intercell interference (ICI) coordination method is also studied to mitigate ICI. At the end, a novel resource allocation algorithm is proposed—modeled as an optimization problem—which takes into account the total power consumption, including radiated, circuit, and backhaul power, and the minimum required data rate to maximize EE. Given an SE requirement, the EE optimization problem is formulated as a constrained optimization problem. The considered optimization problem is transformed into a convex optimization problem by redefining the constraint using cubic inequality, which results in an efficient iterative resource allocation algorithm. In each iteration, the transformed problem is solved by using dual decomposition with a projected gradient method. Simulations results demonstrate how backhaul has a significant impact on total power consumption and the effectiveness of the studied schemes. In addition, the results demonstrate that the proposed iterative resource allocation algorithm converges within a small number of iterations and illustrate the fundamental tradeoffs between SE and EE. Our analytical results shed light on future “green” network planning in advanced OFDMA wireless systems like those envisioned for a fifth-generation (5G) system. [ABSTRACT FROM PUBLISHER]

Interference alignment (IA) is a promising technique for interference management in wireless networks. However, the sum rate may fall short of the theoretical maximum, particularly at low signal-to-noise ratio (SNR) levels since IA mainly concentrates on mitigating the interference, instead of improving the quality of desired signal. Moreover, most of the previous works focused on improving spectrum efficiency, but the energy efficiency (EE) aspect is largely ignored. In this paper, an adaptive energy-efficient IA algorithm is proposed through power allocation (PA) and transmission-mode adaptation for green IA-based wireless networks. The PA problem for IA is first analyzed; then, we propose a PA scheme that optimizes the EE of IA-based wireless networks. When the SNR is low, the transmitted power of some users may become zero. Thus, the users with low transmitted power are turned into the sleep mode in our scheme to save energy. The transmitted power and transmission mode of the remaining active users are adapted again to further improve the EE of the network. To guarantee the interests of all the users, fairness among users is also considered in the proposed scheme. Simulation results are presented to show the effectiveness of the proposed algorithm in improving the EE of IA-based wireless networks. [ABSTRACT FROM PUBLISHER]

In this paper, we provide a comprehensive study of secrecy transmission in decode-and-forward (DF) relay networks subjected to slow fading. With only channel distribution information (CDI) of the wiretap channels, we aim at maximizing secrecy throughput of the two-hop transmission under a secrecy outage constraint through optimizing transmission region, rate parameters of the wiretap codes and power allocation between the source and relay. We propose fixed transmission parameter scheme (FTPS) and variable transmission parameter scheme (VTPS), which are based on the CDI and instantaneous channel state information of the main channels, respectively. In both schemes, source and relay use the same codeword, and the eavesdropper can use maximum ratio combining (MRC) reception. To improve the secrecy throughput, we further propose VTPS-D1 and VTPS-D2 schemes, where the source and relay either use independent codewords with identical code rates, or different codebooks with different code rates so that the eavesdropper can only decode the two-hop signals individually rather than using MRC. We provide explicit results on the design for all proposed schemes. Numerical results and comparisons on the secrecy throughput of these schemes are presented to reveal their respective superiorities and give some insights into the choice of design scheme. [ABSTRACT FROM PUBLISHER]

This paper focuses on linear beamforming design and power allocation strategy for ergodic rate optimization in a two-user Multiple-Input Single-Output (MISO) system with statistical and delayed channel state information at the transmitter (CSIT). We propose a transmission strategy, denoted as Statistical Alternative MAT (SAMAT), which exploits both channel statistics and delayed CSIT. Firstly, with statistical CSIT only, we focus on statistical beamforming (SBF) design that maximizes a lower bound on the ergodic sum-rate. Secondly, relying on both statistical and delayed CSIT, an iterative algorithm is proposed to compute the precoding vectors of Alternative MAT (AMAT), originally proposed by Yang et al., which maximizes an approximation of the ergodic sum-rate with equal power allocation. Finally, via proper power allocation, the SAMAT framework is proposed to softly bridge between SBF and AMAT for an arbitrary number of transmit antennas and signal-to-noise ratio (SNR). A necessary condition for the power allocation optimization is identified from the Karush-Kuhn-Tucker (KKT) conditions. The optimum power allocation to maximize an ergodic sum-rate approximation is computed using Sequential Quadratic Programming (SQP). Simulation results show that the proposed SAMAT scheme yields a significant sum-rate enhancement over both SBF and AMAT. [ABSTRACT FROM PUBLISHER]

In this paper, we investigate multiuser scheduling with joint user pairing, resource allocation, and power control (PC) in Long-Term Evolution (LTE) uplink cellular networks. We first derive the received signal-to-interference-plus-noise ratio (SINR) in spatial multiuser single-carrier frequency-division multiple-access (SC-FDMA) systems with frequency-domain minimum mean square error (MMSE) equalization. Based on this, we formulate an optimization problem as joint multiuser pairing, resource allocation, and PC to maximize the weighted throughput of the system. We then divide it into a two-step optimization problem, which is suboptimal but significantly reduces the complexity. The two-step problem first performs optimal frequency resource allocation and user pairing for a given transmit power value and then adjusts the transmit power to maximize the weighted throughput when frequency resources are allocated and users are paired. With the help of high-interference indicators exchanged among multiple base stations (BSs), we develop an iterative distributed and low-complexity algorithm to further simplify the two-step problem. System-level simulation results show that the proposed algorithm can effectively mitigate interference and significantly outperforms the existing algorithms. [ABSTRACT FROM PUBLISHER]

The newly emerging wireless powered communication networks (WPCNs) have recently drawn significant attention, where radio signals are used to power wireless terminals for information transmission. In this paper, we study a WPCN where one multi-antenna access point (AP) coordinates energy transfer and information transfer to/from a set of single-antenna users. A harvest-then-transmit protocol is assumed where the AP first broadcasts wireless power to all users via energy beamforming in the downlink (DL), and then, the users send their independent information to the AP simultaneously in the uplink (UL) using their harvested energy. To optimize the users' throughput and yet guarantee their rate fairness, we maximize the minimum throughput among all users by a joint design of the DL–UL time allocation, the DL energy beamforming, and the UL transmit power allocation, as well as receive beamforming. We solve this nonconvex problem optimally by two steps. First, we fix the DL–UL time allocation and obtain the optimal DL energy beamforming, UL power allocation, and receive beamforming to maximize the minimum signal-to-interference-plus-noise ratio of all users. This problem is shown to be still nonconvex; however, we convert it equivalently to a spectral radius minimization problem, which can be solved efficiently by applying the alternating optimization based on the nonnegative matrix theory. Then, the optimal time allocation is found by a one-dimensional search to maximize the minimum rate of all users. Furthermore, two suboptimal designs of lower complexity are also proposed, and their throughput performance is compared against that of the optimal solution. [ABSTRACT FROM AUTHOR]

In this paper, we study the problem of resource allocation in SC-FDMA systems. A sum-utility maximization is considered where the utility of each user may be neither additive nor super additive. Unlike OFDMA, in addition to the restriction of allocating a subchannel to at most one user, the multiple subchannels allocated to a user in SC-FDMA must be consecutive. This makes the resource allocation problem prohibitively difficult and challenging. We provide a fundamental complexity analysis of the optimization problem for general non-additive utility functions and show that not only the problem is NP-hard but also approximating it within a factor better than 2011\over 2012 is not possible unless P=NP. An efficient cutting plane algorithm is presented and five suboptimal heuristics are also presented that achieve near optimal solution in different scenarios. Computational results of the cutting plane and heuristic algorithms are reported and a comparison between these heuristics is provided as well. [ABSTRACT FROM PUBLISHER]

Recently, energy efficiency in wireless networks has become an important objective. Aside from the growing proliferation of smartphones and other high-end devices in conventional human-to-human (H2H) communication, the introduction of machine-to-machine (M2M) communication or machine-type communication into cellular networks is another contributing factor. In this paper, we investigate quality-of-service (QoS)-driven energy-efficient design for the uplink of long term evolution (LTE) networks in M2M/H2H co-existence scenarios. We formulate the resource allocation problem as a maximization of effective capacity-based bits-per-joule capacity under statistical QoS provisioning. The specific constraints of single carrier frequency division multiple access (uplink air interface in LTE networks) pertaining to power and resource block allocation not only complicate the resource allocation problem, but also render the standard Lagrangian duality techniques inapplicable. We overcome the analytical and computational intractability by first transforming the original problem into a mixed integer programming (MIP) problem and then formulating its dual problem using the canonical duality theory. The proposed energy-efficient design is compared with the spectral efficient design along with round robin (RR) and best channel quality indicator (BCQI) algorithms. Numerical results, which are obtained using the invasive weed optimization (IWO) algorithm, show that the proposed energy-efficient uplink design not only outperforms other algorithms in terms of energy efficiency while satisfying the QoS requirements, but also performs closer to the optimal design. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the performance of an analog network coding (ANC)-based two-way relay system that employs beamforming at the multi-antenna sources. Specifically, we analyze the overall system performance by deriving an exact expression for the overall symbol error rate (OSER) and an upper bound expression for the ergodic sum-rate (ESR) over independent and non-identically distributed Nakagami- $m$ fading channels. We also derive closed-form representations of these expressions in the asymptotic high signal-to-noise ratio (SNR) regime to provide useful insights into the system behavior, and the optimal power allocation and relay location. Moreover, we address the joint optimization problem of power allocation and relay location to minimize the OSER and to maximize the ESR. Our results highlight the influence of key system/channel parameters on the overall system performance. [ABSTRACT FROM PUBLISHER]