基于能效最优准则的双跳中继网络功率分配算法

针对单天线多跳系统中的资源分配策略进行了研究,重点研究了基于能效最优的功率分配算法。所提算法以系统能效最大化为设计目标,以满足指定的系统服务质量(QoS, quality of service)为约束条件,建立了以源节点和中继节点发射功率为设计变量的数学优化模型。通过大信噪比区间近似等效,将原始的非凸优化问题转化为凸优化问题。再利用拉格朗日对偶函数凸优化算法,并借助于Lambert 函数,最终得到一种功率分配方案的闭合形式解,避免了采用交替迭代方法来求解最优化问题。相比传统以系统频谱效率最大化为目标的算法,所提算法能更好地提升系统整体能效,同时降低了功率分配算法的复杂度。     

异构蜂窝网络中基于能效的非正交多址接入下行功率分配算法

该文针对应用非正交多址接入(NOMA)技术的异构蜂窝网络,在考虑层间层内干扰的情况下,提出一种能效最大化的功率分配算法。该算法主要包括两部分,一部分为子信道内用户功率分配因子的求解,主要利用差分优化的方法,迭代求解。另一部分为子信道间的功率分配,主要利用凹凸程序法将原有的非凸问题简化为可解的凸问题,最后利用拉格朗日求解法得出功率最优解。仿真结果表明该算法有良好的迭代性,且新算法表明利用NOMA技术得到的系统能效较利用正交技术得到的系统能效提高了至少44%以上。     

下行链路广播信道能效优化鲁棒波束成形算法

针对多天线广播下行链路通信系统,研究了一种鲁棒能效联合波束成形和功率分配算法。首先,鲁棒能效优化问题描述为满足一定功率约束的系统和速率与系统消耗之比的最大化优化问题。其次,利用分数规划理论及用户速率与最小均方误差之间的关系,把所描述的分数规划优化问题转化成参数化多项式优化问题。然后,利用拉格朗日对偶及单调优化理论,提出了一种有效的鲁棒能效优化算法。数值仿真结果表明,相对于传统的非鲁棒能效优化算法,所提鲁棒能效优化算法可获得明显的能效性能增益。      

一种分布式天线系统的最大化能源效率传输优化算法

为实现绿色通信,考虑在每个远端射频头的发送功率受限、前端链路容量受限和保证用户目标速率的情况下,研究分布式天线系统的最大化能源效率传输优化算法。受分布式前端链路容量表达式及能源效率分式形式的约束,所考虑优化问题是非凸优化问题,难以获得其最优解。针对此优化问题,通过分式规划和凸近似方法将原始非凸问题转化为凸优化问题,提出了有效的二层迭代优化算法,并理论分析证明了所提算法的收敛性。仿真结果表明,所提算法优于传统算法,且随着远端射频头调度的用户数减少,能效压缩预编码策略的能源效率增大。      

放大转发双向中继系统中降低总功率的波束成形设计

本文研究了多天线放大转发双向中继系统中在满足源节点信噪比要求条件下最小化系统总功率的波束设计问题。该问题是非凸优化问题,为了有效求解该问题,采用分层优化方法将原问题分解成发送波束成形向量优化、接收波束成形向量优化和中继波束成形矩阵优化三类子问题。发送/接收波束成形向量通过求解Rayleigh商最小化问题来获得。中继波束成形矩阵优化问题通过半正定松弛方法转化成半正定优化问题来求解。在求解这三类优化问题的基础上,提出了一种迭代波束成形算法,并采用单调有界序列定理证明了所提算法的收敛性。计算机仿真表明:所提算法经过若干次迭代即可收敛到稳定点;相比于已有算法,本文算法能显著降低系统总功率。      

面向安全通信的智能反射面网络能效优化算法

为了提高智能反射面网络传输安全性和能效,提出了一种面向安全通信的智能反射面网络能效最大资源分配算法。针对多输入单输出的智能反射面辅助蜂窝通信系统,考虑用户的安全速率约束、基站的最大发射功率约束以及连续相移约束,建立了非线性、多变量耦合的能效最大化资源分配模型。利用Dinkelbach方法将目标函数转化为辅助变量相减的形式,采用交替迭代算法求解原变量耦合的非凸优化问题。仿真结果表明,所提算法与传统算法对比,能效提升了8.3%,中断概率下降了77%。     

基于非正交多址接入异构携能网络稳健能效资源分配算法

为了解决5G移动通信系统功耗较大、频谱短缺、覆盖盲区等问题,针对用户终端带能量收集的两层异构非正交多址接入网络,提出了一种基于能效最大的稳健资源分配算法。考虑每个微蜂窝最小速率约束、微蜂窝基站最大功率约束、跨层干扰约束和时间切换系数约束,基于有界信道不确定性建立联合稳健功率分配和时间切换的混合资源分配模型。基于Dinkelbach方法和Worst-case方法,将原NP-hard问题转换为确定性优化问题。利用连续凸近似方法将确定性稳健优化问题转换为凸优化问题,并基于拉格朗日对偶分解方法提出一种双层迭代算法实现功率分配和最优时间切换。仿真结果表明,所提算法在能效和稳健性方面优于传统非稳健算法和非携能通信算法。     

多小区大规模协同功率分配及波束成形算法

针对大规模多输入单输出的多点协作下行系统,本文主要研究协同波束成形和功率控制,以达到最大化最差用户信干噪比的目的。为了求解原始下行的非凸优化问题,首先将原始优化问题转化成等价的上行优化问题进行求解。尽管在有限系统里可通过迭代算法获得波束矢量和发射功率,但是该算法依赖于瞬时信道信息,功率也需要瞬时更新。为了减少功率更新计算复杂度,本文进一步利用随机矩阵理论,提出了只需要依赖统计信道信息的算法来获得发射功率。数值仿真验证了单基站功率约束下所提算法的有效性以及相对于最大比发送算法的优越性。      

多标签无线供电反向散射通信网络能效优化算法

为了提高物联网(IoT)节点的运行周期和能量利用率,该文提出一种多标签无线供电反向散射通信网络能效最大化资源分配算法。考虑传输速率约束、能量收集约束以及发射功率约束,建立了基于系统能效最大化的资源分配模型。利用Dinkelbach理论、2次变换以及变量替换法,将原分式非凸问题转化为可求解的凸优化问题。通过拉格朗日对偶理论求得优化问题的全局最优解。仿真结果表明,该算法具有较好的收敛性和能效。     

面向窃听用户的RIS-MISO系统鲁棒资源分配算法

针对信道不确定性影响、用户信息泄露和能效提升等问题,该文提出一种基于不完美信道状态信息的可重构智能反射面(RIS)多输入单输出系统鲁棒资源分配算法。首先,考虑能量收集最小接收功率约束、合法用户最小保密速率约束、基站最大发射功率约束及RIS相移约束,基于有界信道不确定性,建立一个联合优化基站主动波束、能量波束、RIS相移矩阵的多变量耦合非线性资源分配问题。然后,利用Dinkelbach,S-procedure和交替优化方法,将原非凸问题转换成确定性凸优化问题,并提出一种基于连续凸近似的交替优化算法。仿真结果表明,与传统非鲁棒算法对比,所提算法具有较低的中断概率。     

多小区下行链路系统的资源效率最大化协同波束成形算法

本文研究了基于资源效率优化的多小区多用户协同波束成形算法设计。为了权衡系统频谱效率与能源效率,考虑在单基站发射功率约束以及达到用户服务质量需求条件下最大化系统资源效率,即系统能源效率与归一化系统频谱效率的加权和。由于优化变量之间的耦合性以及约束条件的非凸特性,该优化问题是一种非凸优化问题并且难以直接获得最优解。为了求解所考虑的优化问题,本文联合利用凸近似方法和分数规化理论,提出一种多小区下行链路系统中最大化资源效率的交替优化算法。所提算法的收敛性可以由凸近似方法和单调有界理论保证。同时,数值仿真验证了所提算法的有效性。      

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.     

Design of Optimal Linear Precoder and Decoder for MIMO Channels with Per Antenna Power Constraint and Imperfect CSI

This paper addresses the problem of designing joint optimum precoder and decoder for multiple-input multiple-output communication system. Conventionally, most of the joint precoder and decoder designs are based on the sum power constraint (SPC) at the transmitter and perfect channel state information (CSI). However, in practice, per-antenna power constraint is more realistic as the power at each transmit antenna is limited individually by the linearity of the power amplifier. Further, the estimate of CSI cannot be obtained perfectly by any methods. Under imperfect CSI, the aim is to design jointly optimum precoder and decoder subject to a power constraint that jointly meets both per-antenna and SPCs. The objective function is formulated into an optimization problem that minimizes the mean square error in estimating the transmitted signal. The simulation results show that the proposed scheme has a near-optimum performance under practical constraints.     

Distributed energy efficient beamforming algorithm for multicell multiuser downlink system

A distributed energy efficient beamforming algorithm was studied for multicell multiuser downlink communication system.Firstly,the energy efficient optimization problem was first considered as the maximization of the ratio between the system sum rate and the system power consumption.The fractional programming optimization target was then transformed into a subtractive form via using the fractional programming theorem.Secondly,the problem was decomposed into some subproblems that can be solved respectively by introducing the concept of the interference temperature in cognitive radio networks.Finally,an effective distributed energy efficient beamforming algorithm was proposed by exploiting the Lagrangian duality theorem and optimization theorem.Compared to the classical energy efficient optimization algorithm,the proposed algorithm can achieve an obvious energy efficiency performance gain.     

Penalty function-based precoding for downlink multiuser MIMO systems

In this paper, a novel linear precoding scheme is proposed for downlink multiuser multiple-input multiple-output (MIMO) systems. The new algorithm uses the penalty function method to mitigate the co-channel interference and is formulated as a convex problem with general linear constraints. The constraints can be sum power, per-antenna power or per-antenna-group power constraints, hence the new algorithm is general and can be used in both single-cell and fully cooperative multi-cell scenarios. Moreover, the famous block diagonalization (BD) precoding can be considered as a special case of our method when a very large penalty factor is used. We study the optimal solution of this convex problem and propose an iterative algorithm to obtain the optimum based on the Lagrange dual method. Simulation results show that the proposed method significantly outperforms the BD method at low and moderate SNR values in terms of the weighted sum rate.     

Robust beamforming for multicell downlink TDD massive MIMO system based on QoS

In view of multicell downlink time division multiplexing (TDD) massive multiple-input multiple-output (MIMO) systems which had imperfect channel state information (CSI),the beamforming problem that minimized the total transmit power and signal leakage power based on quality of service (QoS) was studied.First,the objective problem was approximated as a standard convex optimization problem.Then,by using the duality of uplink and downlink,an inner and outer layer iterative algorithm was proposed.Numerical results show that,comparing with other typical downlink multicell massive MIMO beamforming algorithms,the proposed algorithm has obvious advantages in terms of complexity and energy efficiency.     

Energy‐Efficiency Power Allocation for Cognitive Radio MIMO‐OFDM Systems

This paper studies energy‐efficiency (EE) power allocation for cognitive radio MIMO‐OFDM systems. Our aim is to minimize energy efficiency, measured by “Joule per bit” metric, while maintaining the minimal rate requirement of a secondary user under a total power constraint and mutual interference power constraints. However, since the formulated EE problem in this paper is non‐convex, it is difficult to solve directly in general. To make it solvable, firstly we transform the original problem into an equivalent convex optimization problem via fractional programming. Then, the equivalent convex optimization problem is solved by a sequential quadratic programming algorithm. Finally, a new iterative energy‐ efficiency power allocation algorithm is presented. Numerical results show that the proposed method can obtain better EE performance than the maximizing capacity algorithm.     

Security enhancement with joint transmit antenna selection and transmit beamforming under energy harvesting constraints

This paper considers a multiuser multiple‐input single‐output (MISO) broadcasting system with simultaneous wireless information and power transfer (SWIPT), which consists of one information receiver (IR) and several energy harvesting receivers (ERs) which are capable of eavesdropping the legitimate signals. For reducing cost and hardware complexity, transmit antenna selection (TAS) is applied in the transmitter. We aim to maximize the achievable secrecy rate under the individual energy harvesting constraint at the ERs and the transmit power constraint at the transmitter by jointly optimizing TAS, transmit beamforming, and artificial noise (AN). The joint optimization problem is a non‐convex mixed integer programming problem. We apply variable replacements to decouple the variable couplings and relax and approach the binary constraint by the difference of two convex constraints. Afterwards, penalty method and constrained concave convex procedure (CCCP) are applied to transform the relaxed problem into a sequence of semi‐definiteness programming (SDP) problems. Simulation results shows that our proposed joint optimization scheme is superior over existing non‐joint optimization schemes. This paper studies the joint transmit antenna selection (TAS), transmit beamforming, and artificial noise (AN) optimization in a multiple‐input multiple‐output (MISO) wiretap system with simultaneous wireless information and power transfer (SWIPT). The joint optimization problem is nonconvex and we propose a penalty method based scheme to solve it. The simulation results show that our joint optimization scheme is superior to other non‐joint optimization schemes.     

多小区广播干扰信道的加权均方误差最小化收发机设计算法

未来的第五代无线网络中,由于干扰问题的存在,阻碍了网络性能的提升,尤其是对于小区边缘的用户,其通信质量的需求不能得到满足。本文研究了基于加权均方误差优化的多小区多用户广播干扰信道的收发机算法设计。针对所需求解的优化问题,首先利用分层预编码的方法提出了一种广义干扰对齐的方案,然后提出了一种最小化加权均方误差的交替优化算法。所提算法的收敛性可以由单调有界理论保证。仿真结果表明:与传统的方案相比,所提方案可以有效的消除干扰,并且有效的提升系统的和速率性能。