Joint Optimization of Transmit Power-Time and Bit Energy Efficiency in CDMA Wireless Sensor Networks

In this paper, we address the problem of minimizing energy consumption in a CDMA-based wireless sensor network (WSN). A comprehensive energy consumption model is proposed, which accounts for both the transmit and circuit energies. Energy consumption is minimized by jointly optimizing the transmit power and transmission time for each active node in the network. The problem is formulated as a non-convex optimization. Numerical as well as closed-form approximate solutions are provided. For the numerical solution, we show that the formulation can be transformed into a convex geometric programming (GP), for which fast algorithms, such as interior point method, can be applied. For the closed-form solution, we prove that the joint power/time optimization can be decoupled into two sequential sub-problems: optimization of transmit power with transmission time serving as a parameter, and then optimization of the transmission time. We show that the first sub-problem is a linear program while the second one can be well approximated as a convex programming problem. Taking advantage of these analytical results, we further derive the per-bit energy efficiency. Our results are verified through numerical examples and simulations     

Distributed Beamforming for Relay Networks Based on Second-Order Statistics of the Channel State Information

In this paper, the problem of distributed beamforming is considered for a wireless network which consists of a transmitter, a receiver, and $r$ relay nodes. For such a network, assuming that the second-order statistics of the channel coefficients are available, we study two different beamforming design approaches. As the first approach, we design the beamformer through minimization of the total transmit power subject to the receiver quality of service constraint. We show that this approach yields a closed-form solution. In the second approach, the beamforming weights are obtained through maximizing the receiver signal-to-noise ratio (SNR) subject to two different types of power constraints, namely the total transmit power constraint and individual relay power constraints. We show that the total power constraint leads to a closed-form solution while the individual relay power constraints result in a quadratic programming optimization problem. The later optimization problem does not have a closed-form solution. However, it is shown that using semidefinite relaxation, this problem can be turned into a convex feasibility semidefinite programming (SDP), and therefore, can be efficiently solved using interior point methods. Furthermore, we develop a simplified, thus suboptimal, technique which is computationally more efficient than the SDP approach. In fact, the simplified algorithm provides the beamforming weight vector in a closed form. Our numerical examples show that as the uncertainty in the channel state information is increased, satisfying the quality of service constraint becomes harder, i.e., it takes more power to satisfy these constraints. Also our simulation results show that when compared to the SDP-based method, our simplified technique suffers a 2-dB loss in SNR for low to moderate values of transmit power.      

Joint scheduling and power control supporting multicasting in wireless ad hoc networks

This paper addresses the problem of power control in a multihop wireless network supporting multicast traffic. We face the problem of forwarding packet traffic to multicast group members while meeting constraints on the signal-to-interference-plus-noise ratio (SINR) at the intended receivers. First, we present a distributed algorithm which, given the set of multicast senders and their corresponding receivers, provides an optimal solution when it exists, which minimizes the total transmit power. When no optimal solution can be found for the given set of multicast senders and receivers, we introduce a distributed, joint scheduling and power control algorithm which eliminates the weak connections and tries to maximize the number of successful multicast transmissions. The algorithm allows the other senders to solve the power control problem and minimize the total transmit power. We show that our distributed algorithm converges to the optimal solution when it exists, and performs close to centralized, heuristic algorithms that have been proposed to address the joint scheduling and power control problem.     

基于IRS辅助的SWIPT物联网系统安全波束成形设计

为满足绿色万物互联的智能信号处理部署和物理层安全的新要求,针对基于智能反射面辅助的无线携能通信物联网系统中可持续能量供应紧缺问题,提出了一种安全波束成形设计方法.考虑保密速率、发射功率和IRS反射相移约束,以最大化能量采集器采集功率为目标,联合优化基站发射波束成形矩阵和干扰机协方差矩阵以及IRS相移,将优化问题建模为具...     

Generalized co-phasing for multiple transmit and receive antennas

We propose and study a class of transmit beamforming techniques for systems with multiple transmit and multiple receive antennas with a per-antenna transmit power constraint. The per-antenna transmit power constraint is more realistic than the widely used total (across all transmit antennas) power constraint, since in practice each transmit antenna is driven by a separate power amplifier with a maximum power rating. Under the per-antenna power constraint, from an implementation perspective, it becomes desirable to vary only the phases (as opposed to both power and phase variation) of the signals departing from the transmit antennas. We name this class of techniques generalized co-phasing and formulate an optimization problem to calculate the transmit antenna phases. Furthermore, we propose five heuristic algorithms to solve the optimization problem. All the proposed algorithms except one are optimal for the case of two transmit antennas and an arbitrary number of receive antennas. For an arbitrary number of transmit and receive antennas, simulations indicate that the proposed algorithms perform very close to the optimal solution calculated through an exhaustive search of all possible transmit phases.     

Computation energy efficiency maximization based resource allocation scheme in wireless powered mobile edge computing network

For wireless powered mobile edge computing (MEC) network,a system computation energy efficiency (CEE) maximization scheme by considering the limited computation capacity at the MEC server side was proposed.Specifically,a CEE maximization optimization problem was formulated by jointly optimizing the computing frequencies and execution time of the MEC server and the edge user(EU),the transmit power and offloading time of each EU,the energy harvesting time and the transmit power of the power beacon.Since the formulated optimization problem was a non-convex fractional optimization problem and hard to solve,the formulated problem was firstly transformed into a non-convex subtraction problem by means of the generalized fractional programming theory and then transform the subtraction problem into an equivalent convex problem by introducing a series of auxiliary variables.On this basis,an iterative algorithm to obtain the optimal solutions was proposed.Simulation results verify the fast convergence of the proposed algorithm and show that the proposed resource allocation scheme can achieve a higher CEE by comparing with other schemes.     

Optimal simultaneous wireless information and energy transfer in OFDMA decode-and-forward relay networks

In this paper, we consider simultaneous wireless information and energy transfer in an orthogonal-frequency-division-multiple-access decode-and-forward relay network, in which an energy-constrained relay node harvests energy from a source node and uses the harvested energy to forward information to multiple destination nodes. Our objective is to maximize the end-to-end sum rate by resource allocation, subject to transmit power constraint at the source and energy-harvesting (EH) constraint at the relay. A non-convex and mixed-integer programming (MIP) problem is formulated to optimize time-switching (TS) ratios of EH and information decoding at the relay, TS ratio of information transmission from relay to destinations, subcarrier allocation as well as power allocation (PA) over all subcarriers at source and relay. We propose to decouple this problem into a convex problem and an MIP problem in fractional form. To solve the MIP problem, we transform it into an equivalent optimization problem in subtractive form which has a tractable solution. As a result, we propose a novel scheme to achieve jointly optimal TS ratios, subcarrier allocation and PA. Simulation results verify the optimality of our proposed resource allocation scheme.     

Optimised power allocation for a power beacon-assisted SWIPT system with a power-splitting receiver

ABSTRACT

This article studies a power beacon (PB)-assisted simultaneous wireless information and power transfer (SWIPT) system, where a base station (BS) sends information, and a PB transfers radio frequency energy to a receiver equipped with a power-splitting (PS) structure to decode information and harvest energy simultaneously. First, we jointly design the transmit power of the PB and the PS ratio, to formulate the minimum transmit power at the PB problem under the constraints of minimum energy harvesting and the minimum signal-to-interference-plus-noise ratio. Unfortunately, the problem is not always feasible, and thus, we provide the conditions to guarantee the feasibility of the problem and derive the analytical optimal solution by the Lagrange method and Karush–Kuhn–Tucker optimality conditions. Second, we consider a receiver with the capability of cancelling the interference from the PB, and we obtain the optimal solution to the minimum PB transmit power problem. Next, we consider the transmission power at the BS as another optimisation variable and solve the minimisation problem with an objective function represented by the transmit power at the BS plus the transmit power at the PB. Finally, numerical results verify the optimality of the proposed approaches in comparison with three benchmark schemes.     

MIMO Transmit Beamforming Under Uniform Elemental Power Constraint

We consider multi-input multi-output (MIMO) transmit beamforming under the uniform elemental power constraint. This is a nonconvex optimization problem, and it is usually difficult to find the optimal transmit beamformer. First, we show that for the multi-input single-output (MISO) case, the optimal solution has a closed-form expression. Then we propose a cyclic algorithm for the MIMO case which uses the closed-form MISO optimal solution iteratively. The cyclic algorithm has a low computational complexity and is locally convergent under mild conditions. Moreover, we consider finite-rate feedback methods needed for transmit beamforming. We propose a simple scalar quantization method, as well as a novel vector quantization method. For the latter method, the codebook is constructed under the uniform elemental power constraint and the method is referred as VQ-UEP. We analyze VQ-UEP performance for the MISO case. Specifically, we obtain an approximate expression for the average degradation of the receive signal-to-noise ratio (SNR) caused by VQ-UEP. Numerical examples are provided to demonstrate the effectiveness of our proposed transmit beamformer designs and the finite-rate feedback techniques.     

Worst-Case Robust MIMO Transmission With Imperfect Channel Knowledge

In this paper, we consider robust transmit strategies, against the imperfectness of the channel state information at the transmitter (CSIT), for multi-input multi-output (MIMO) communication systems. Following a worst-case deterministic model, the actual channel is assumed to be inside an ellipsoid centered at a nominal channel. The objective is to maximize the worst-case received signal-to-noise ratio (SNR), or to minimize the worst-case Chernoff bound of the error probability, thus leading to a maximin problem. Moreover, we also consider the QoS problem, as a complement of the maximin design, which minimizes the transmit power consumption and meanwhile keeps the received SNR above a given threshold for any channel realization in the ellipsoid. It is shown that, for a general class of power constraints, both the maximin and QoS problems can be equivalently transformed into convex problems, or even further into semidefinite programs (SDPs), thus efficiently solvable by the numerical methods. The most interesting result is that the optimal transmit directions, i.e., the eigenvectors of the transmit covariance, are just the right singular vectors of the nominal channel under some mild conditions. This result leads to a channel-diagonalizing structure, as in the cases of perfect CSIT and statistical CSIT with mean or covariance feedback, and reduces the complicated matrix-valued problem to a scalar power allocation problem. Then we provide the closed-form solution to the resulting power allocation problem.     

无线供能反向散射通信中能耗最小化资源分配方法

针对无线供能反向散射通信网络,提出了一种满足传感设备通信需求及能量因果的专用能量站能耗最小化资源分配方法。在考虑非线性能量收集模型及不完美串行干扰消除基础上,通过联合优化专用能量站发射功率、传感设备反向散射通信时间、反向散射系数及其能量收集时间,构建了一个专用能量站能耗最小化的非凸多维资源分配问题。首先,构建辅助变量对反向散射系数与时间进行解耦,再利用目标函数是关于专用能量站发射功率的单调递减函数这一特性来设计一种基于二分法的迭代算法来获取原问题的最优解。仿真验证了所提算法的快速收敛性,同时,与同类算法相比,所提方法可为专用能量站节约更多的能量。     

Sum-rate maximization for UAV-enabled two-way relay systems

In this paper, an Unmanned Aerial Vehicle (UAV)-enabled two-way relay system with Physical-layer Network Coding (PNC) protocol is considered. A rotary-wing UAV is applied as a mobile relay to assist two ground terminals for information interaction. Our goal is to maximize the sum-rate of the two-way relay system subject to mobility constraints, propulsion power consumption constraints, and transmit power constraints. The formulated problem is not easy to solve directly because it is a mixed integer non-convex optimization problem. Therefore, we decompose it into three sub-problems, and use the mutation arithmetic of the Genetic Algorithm (GA) and Successive Convex Approximation (SCA) to dispose. Besides, a high-efficiency iterative algorithm is proposed to obtain a locally optimal solution by jointly optimizing the time slot pairing, the transmit power allocation, and the UAV trajectory design. Numerical results demonstrate that the proposed design achieves significant gains over the benchmark designs.     

Load-balance cell association for improving network capacity in heterogeneous cellular networks

Heterogeneous cellular networks improve the spectrum efficiency and coverage of wireless communication networks by deploying low power base station （BS） overlapping the conventional macro cell. But due to the disparity between the transmit powers of the macro BS and the low power BS, cell association strategy developed for the conventional homogeneous networks may lead to a highly unbalanced traffic loading with most of the traffic concentrated on the macro BS. In this paper, we propose a load-balance cell association scheme for heterogeneous cellular network aiming to maximize the network capacity. By relaxing the association constraints, we can get the upper bound of optimal solution and convert the primal problem into a convex optimization problem. Furthermore we propose a Lagrange multipliers based distributed algorithm by using Lagrange dual theory to solve the convex optimization, which converges to an optimal solution with a theoretical performance guarantee. With the proposed algorithm, mobile terminals （MTs） need to jointly consider their traffic type, received signal-to-interference-noise-ratios （SINRs） from BSs, and the load of BSs when they choose server BS. Simulation results show that the load balance between macro and pico BS is achieved and network capacity is improved significantly by our proposed cell association algorithm.     

Optimized Power Allocation for a Cooperative NOMA System with SWIPT and an Energy-Harvesting User

ABSTRACT

This paper investigates the solution to an optimisation problem to minimise the total transmission power at the transmitter in a cooperative non-orthogonal multiple access (NOMA) system with simultaneous wireless information and power transfer (SWIPT) and an energy-harvesting user. First, we formulate the optimisation problem to obtain the minimum transmission power at the transmitter under the constraints of minimum signal-to-interference-plus-noise ratio and minimum energy harvesting. Since the problem is not convex, we transform it into a bi-level optimisation problem. Then, conditions to guarantee the feasibility of the problem are provided, and we derive the analytical optimal solution via the Lagrange method meeting Karush–Kuhn–Tucker optimality conditions to solve the lower-level variables of the inner convex problem. Second, we use particle swarm optimisation to find the approximately optimal values of the upper-level variables. Next, we present two baseline schemes based on orthogonal multiple access (OMA) and equal power splitting for performance comparison with the proposed cooperative NOMA system with SWIPT. Finally, simulation results show that cooperative NOMA with SWIPT can reduce the transmit power at the transmitter, compared to two baseline schemes: OMA and EPS.     

Multiple Peer-to-Peer Communications Using a Network of Relays

We consider an ad hoc wireless network consisting of d source-destination pairs communicating, in a pairwise manner, via R relaying nodes. The relay nodes wish to cooperate, through a decentralized beamforming algorithm, in order to establish all the communication links from each source to its respective destination. Our communication strategy consists of two steps. In the first step, all sources transmit their signals simultaneously. As a result, each relay receives a noisy faded mixture of all source signals. In the second step, each relay transmits an amplitude- and phase-adjusted version of its received signal. That is each relay multiply its received signal by a complex coefficient and retransmits the so-obtained signal. Our goal is to obtain these complex coefficients (beamforming weights) through minimization of the total relay transmit power while the signal-to-interference-plus-noise ratio (SINR) at the destinations are guaranteed to be above certain predefined thresholds. Although such a power minimization problem is not convex, we use semidefinite relaxation to turn this problem into a semidefinite programming (SDP) problem. Therefore, we can efficiently solve the SDP problem using interior point methods. Our numerical examples reveal that for high network data rates, our space division multiplexing scheme requires significantly less total relay transmit power compared to other orthogonal multiplexing schemes, such as time-division multiple access schemes.     

Distributed Power Allocation With Rate Constraints in Gaussian Parallel Interference Channels

This paper considers the minimization of transmit power in Gaussian parallel interference channels, subject to a rate constraint for each user. To derive decentralized solutions that do not require any cooperation among the users, we formulate this power control problem as a (generalized) Nash equilibrium (NE) game. We obtain sufficient conditions that guarantee the existence and nonemptiness of the solution set to our problem. Then, to compute the solutions of the game, we propose two distributed algorithms based on the single user water-filling solution: The sequential and the simultaneous iterative water-filling algorithms, wherein the users update their own strategies sequentially and simultaneously, respectively. We derive a unified set of sufficient conditions that guarantee the uniqueness of the solution and global convergence of both algorithms. Our results are applicable to all practical distributed multipoint-to-multipoint interference systems, either wired or wireless, where a quality of service in (QoS) terms of information rate must be guaranteed for each link.     

Power-Minimizing Rate Allocation in Cooperative Uplink Systems

The rate-allocation problem, which has been aimed at minimizing the total transmit power in cooperative uplink systems, is investigated. Each user transmits over an orthogonal frequency band using cooperative broadcasting. The broadcasting nature of the wireless channel is exploited by allowing users to act as relays for one another. All users operate in the decode-and-forward mode. Depending on the number of relays that was selected by a user, we suggest two schemes: 1) the flow-optimized cooperative scheme (FCS) and 2) the single-relay cooperative scheme (SCS). We develop rate-allocation algorithms for them. In our simulation, we compare the outage performance of our schemes with two other schemes: 1) the orthogonal cooperative scheme and 2) direct transmission. Results indicate that our schemes achieve full diversity order and outperform other compared schemes. The algorithm for FCS achieves the optimality, whereas the algorithm for SCS is near optimal. In addition, our algorithms have fast convergence performance. SCS has a lower complexity than FCS, but it requires a higher total transmit power. However, the difference in total transmit power between FCS and SCS is not large under practical rate requirements. In addition to the total transmit power, we consider the improvement in the individual transmit powers of the users.      

A comparison of frequency-division systems to code-division systems in overloaded channels

In this paper, a frequency-division counterpart of joint power control and sequence design problem for code- division multiple-access (CDMA) systems is solved. Total transmit and receive power minimizations are considered for frequency- division multiplexing (FDM) and frequency-division multiple- access (FDMA) communications over overloaded channels. After the definition of channel overloading for CDMA systems is extended to the frequency-division systems, the user admissibility is characterized by a necessary and sufficient condition for the existence of the optimal solution under unequal signal-to- interference-plus-noise ratio constraints at the output of linear receivers and asymmetric data transmission rate constraints among users. The optimal signal power, bandwidth, transmit waveform, and receive waveform are derived for each user as the decision parameters of the optimization problem. It is shown that, if this solution is applied for the uplink users to minimize the total receive power, the optimal FDMA system performs the same as the optimal CDMA system. It is also shown that, if this solution is applied for the downlink users to minimize the total transmit power, the optimal FDM system always outperforms the code-division system that minimizes the extended total squared correlation. Numerical results suggest that the optimal FDM system and the optimal downlink code-division system achieve the same performance when the total transmit power is minimized.     

Pricing and power control for energy‐efficient radio resource management in cognitive femtocell networks

Cognitive femtocell has been considered as a promising technique that can improve the capacity and the utilization of spectrum efficiency in wireless networks because of the short transmission distance and low transmit power. In this paper, we study the win–win solution of energy‐efficient radio resource management in cognitive femtocell networks, where the macrocell tries to maximize its revenue by adjusting spectrum utilization price while the femtocells try to maximize their revenues by dynamically adjusting the transmit power. When the spectrum utilization price is given by macrocell, we formulate the power control problem of standalone femtocells as an optimization problem and introduce a low‐complexity iteration algorithm based on gradient‐assisted binary search algorithm to solve it. Besides, non‐cooperative game is used to formulate the power control problem between collocated femtocells in a collocated femtocell set, and then low complexity and widely used gradient‐based iteration algorithm is applied to obtain the Nash‐equilibrium solution. Specially, asymptotic analysis is applied to find the approximate spectrum utilization price in macrocell, which can greatly reduce the computational complexity of the proposed energy‐efficient radio resource management scheme. Finally, extensive simulation results are presented to verify our theoretical analysis and demonstrate the performance of the proposed scheme. Copyright © 2013 John Wiley & Sons, Ltd.     

Energy-efficiency resource allocation of very large multi-user MIMO systems

With increasing demand in multimedia applications and high data rate services, energy consumption of wireless devices has become a problem. At the user equipment side, high-level energy consumption brings much inconvenience, especially for mobile terminals that cannot connect an external charger, due to an exponentially increasing gap between the available and required battery capacity. Motivated by this, in this paper we consider uplink energy-efficient resource allocation in very large multi-user MIMO systems. Specifically, both the number of antenna arrays at BS and the transmit data rate at the user are adjusted to maximize the energy efficiency, in which the power consumption accounts for both transmit power and circuit power. We proposed two algorithms. Algorithm1, we demonstrate the existence of a unique globally optimal data rate and the number of antenna arrays by exploiting the properties of objective function, then we develop an iterative algorithm to obtain this optimal solution. Algorithm2, we transform the considered nonconvex optimization problem into a convex optimization problem by exploiting the properties of fractional programming, then we develop an efficient iterative resource allocation algorithm to obtain this optimal solution. Our simulation results did not only show that the the proposed two algorithms converge to the solution within a small number of iterations, but demonstrated also the performances of the proposed two algorithms are close to the optimum. Meanwhile, it also shows that with a given number iterations the performance of proposed algorithm1 is superior to proposed algorithm2 under small p _C . On the contrary, the performance of proposed algorithm2 is superior to proposed algorithm1 under large p _C .