An energy-efficient power allocation scheme for millimeter-wave MIMO-NOMA systems

There are many challenges in fifth generation (5G) telecommunication systems, due to the increasing demands and applications. The most important of which are need to have higher energy efficiency (EE) and spectral efficiency (SE). They are critical in the practical multiple-input multiple-output (MIMO) telecommunication systems. Non-orthogonal multiple access (NOMA) methods and millimeter-waves can be used in conjunction with MIMO systems to improve their EE and SE performance. In this paper, we investigate the application of NOMA and mm-Wave transmission in the downlink of MIMO systems. Then, we formulate the optimization problem for users in MIMO-NOMA systems to maximize the EE that is subject to minimum data rate to satisfy required quality of service (QoS) and maximum transmission power. To achieve the optimal power allocation for users, we reach a problem for the EE maximization that is non-convex and solution of the optimization problem is not trivial. We exploit a lower bound of the data rate and the Lagrange dual function to convert it to a convex and unconstrained problem, which is easy to solve. In the next step, we derive a relation for determining the optimal power allocation of users. In addition, a numerical algorithm is presented that can be used to solve the problem. According to the simulation results of the proposed algorithm, our method performs better and provides higher EE than both orthogonal multiple access and equal power allocation schemes.     

大规模MIMO系统中基于能效最大化的资源联合优化算法

针对信道条件未知的多小区大规模多输入多输出（MIMO）系统,提出一种对导频序列长度、导频符号功率以及数据符号功率进行联合优化的资源分配算法。采用最大比合并（MRC）接收,考虑电功率和导频污染的影响,并对最大传输功率进行约束从而建立起以能效（EE）最大化为目标的非凸函数模型。根据分数规划的性质,首先将分数形式转化成减式形式,进而分解成一系列凸函数之差（DC）的问题,最后采用交替优化算法联合调整 3 个变量从而达到能效最大化的目标。仿真结果表明,随着最大符号传输功率的增加,所提方案仍然能保持良好系统能效性能。     

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.     

Optimised fusion rule in cluster-based energy-efficient CSS for cognitive radio networks

Cooperative spectrum sensing (CSS) can solve the problem of the hidden terminal in cognitive radio (CR). Energy consumption and sensing time are high in CSS due to a large number of users. In CSS, the parameters that affect the energy efficiency (EE) of the system are fusion rule, sensing time, transmission power and number of users. In this paper, four fusion rules OR-OR, AND-OR, OR-AND and AND-AND are proposed for cluster-based CSS and optimum fusion rule is determined. It is found that in terms of EE metric, AND-OR rule outperforms the other fusion rules. An iterative algorithm has been proposed which finds the optimum number of CR users in a cluster and total number of clusters that maximises the EE. Quantitatively at SNR = ?20 dB, EE is maximum when number of clusters equals to 4 and number of users in the cluster equals to 3 for 12 fixed number of users. The maximum EE is 3.69 Mbits/Hz/joule at sensing time 1.5 ms.     

基于天线选择的MIMO系统能效功率分配优化

对MIMO系统在毫米波衰落信道下的能效功率分配进行研究,以期实现绿色传输。考虑发送端采用天线选择技术,接收端采用最大比合并技术,给出系统能量效率公式,并构建系统基于能效最大化的目标优化函数。利用拉格朗日乘子法,获得迭代功率分配方案。利用排序法及朗伯函数给出简单的闭式功率分配方案。利用Matlab软件验证所提功率分配方案的有效性。仿真结果表明,文章提出的功率分配方案较穷举搜索法EE性能得到了明显提升,是一种复杂度低的优化算法,能快速收敛到EE最大值。     

基于速率约束的OFDM中继链路能效最优资源分配策略

该文研究解码转发(DF)模式的OFDM中继链路的能效最大化资源分配问题。与现有典型的固定速率最小化发射功率或无约束最大化能效算法不同,该文考虑电路功率消耗的前提下,将问题建模为以最大化系统能效为目标,同时考虑用户最小速率需求、源节点S和中继节点R各自总发射功率约束下的联合子载波配对和最优功率分配问题。证明了速率和功率联合约束条件下中继链路全局能效最优解的唯一性,在此基础上提出一种低复杂度联合最优资源分配策略。仿真结果表明,该文所提方案能够在最小速率和S/R节点最大发射功率约束下自适应分配功率资源,实现系统能效最优,并能够降低链路的中断概率。     

On the Ergodic Capacity and Energy Efficiency for Fading MIMO NOMA Systems

Non-orthogonal multiple access (NOMA) is expected to be a promising multiple access techniques for 5G networks due to its superior spectral efficiency (SE). Previous research mainly focus on the design to improve the SE performance with instantaneous channel state information (CSI). In this paper, we consider the fading MIMO channels with only statistical CSI at the transmitter, and explore the potential gains of MIMO NOMA scheme in terms of both ergodic capacity and energy efficiency (EE). The ergodic capacity maximization problem is first studied for the fading multiple-input multiple-output (MIMO) NOMA systems. We derive the optimal input covariance structure and propose both optimal and low complexity suboptimal power allocation schemes to maximize the ergodic capacity of MIMO NOMA system. For the EE maximization, the optimization problem is formulated to maximize the system EE (defined by ergodic capacity under unit power consumption) under the total transmit power constraint and the minimum rate constraint of the weak user. By transforming the EE maximization problem into an equivalent one-dimensional optimization problem, the optimal power allocation for EE design is proposed. To further reduce the computation complexity, a near-optimal solution based on golden section search and suboptimal closed form solution are proposed as well. Numerical results show that the proposed NOMA schemes significantly outperform the traditional orthogonal multiple access scheme with traditional orthogonal multiple access transmission in terms of both SE and EE.     

Optimal resource allocation for energy efficiency in coordinated multicell OFDMA networks

Because energy efficiency (EE) is inevitable in future wireless cellular networks, in this paper, we focus on improving the number of bits delivered to users for each unit energy consumption in the downlink of orthogonal frequency‐division multiple access cellular networks with base stations (BSs) coordination. Specifically, each BS shares the channel qualities of users with others and jointly choose the set of co‐channel users and the transmit power allocated to maximize the EE of the system subject to the transmit power ceiling of each BS and minimum required data rate. We formulate the problem as a nonlinear fractional optimization problem, using nonlinear fractional programming, the original hard‐to‐solve problem is transferred to a new one that has the same optimal solution and is easier to solve, this enables two iterative algorithms that achieve nearly the same maximum EE. Numerical results are provided to show the convergence and superiority of the two proposed. Copyright © 2014 John Wiley & Sons, Ltd.     

Energy efficiency comparison between distributed and co‐located MIMO systems

Energy efficiency (EE) is becoming more and more important in future wireless communications because of limited battery power in mobile terminals. In this paper, we compare EE of the distributed MIMO (D‐MIMO) and co‐located MIMO (C‐MIMO) in uplink systems. Taking into account both circuit and transmit power, we derive an analytical expression for EE of D‐MIMO and C‐MIMO systems in a composite Rayleigh‐lognormal channel. What is more, an optimization algorithm is proposed to get the optimal EE values while satisfying given spectral efficiency requirement for both D‐MIMO and C‐MIMO systems. Simulation results show that D‐MIMO systems are more energy effective than C‐MIMO systems when considering the realistic systems, and the optimal EE can be obtained by the proposed algorithm while satisfying given spectral efficiency requirement. Copyright © 2012 John Wiley & Sons, Ltd.     

Massive MIMO异构网的高能效资源分配

为满足第五代移动通信系统高频谱效率和高能量效率的需求,提出一种工作在不同频段下行两层异构网中的高能量效率资源分配方法,考虑用户数据率需求和基站最大发射功率。天线和传输带宽是影响系统能量效率的关键因素。通过研究宏基站和小基站的天线资源和带宽分配发现:当系统天线数很大时,发射功耗的影响可以忽略不计;给定带宽分配因子时,达到宏基站或微基站最大发射功率的天线分配因子几乎可以达到最高能效;给定天线分配因子时,系统平均总功耗是关于带宽分配因子的下凸函数,存在全局最优带宽分配因子使能效最高。仿真结果表明,与给定带宽和天线资源的异构网和小小区网络相比,所提出的异构网可以显著提高系统能量效率,而且在大量用户、高数据率需求时能效提升更明显。      

云无线接入网络高能效功率分配和波束成形联合优化算法

针对云无线接入网络(C-RAN)的资源分配问题,该文采用max-min公平准则作为优化准则,以C-RAN用户的能量效率作为优化目标函数,在满足最大发射功率和最小传输速率约束条件下,通过最大化最差链路的能量效率来实现用户发射功率和无线远端射频单元(RRHs)波束成形向量的联合优化。上述优化问题属于非线性、分式规划问题,为了方便求解,首先将原优化问题转化为差分形式的优化问题,然后通过引入变量将差分形式的、非平滑优化问题转化为平滑优化问题。最终,提出一种双层迭代功率分配和波束成形算法。在仿真实验中,将该文算法与传统的非能效资源分配算法和能量效率最大化算法进行了比较,实验结果证明该文算法在改进C-RAN能量效率和提高资源分配公平性方面的有效性。     

An optimal spectrum‐balancing algorithm for digital subscriber lines based on particle swarm optimization

This paper presents a new algorithm for optimal spectrum balancing in modern digital subscriber line (DSL) systems using particle swarm optimization (PSO). In DSL, crosstalk is one of the major performance bottlenecks, therefore various dynamic spectrum management algorithms have been proposed to reduce excess crosstalks among users by dynamically optimizing transmission power spectra. In fact, the objective function in the spectrum optimization problem is always nonconcave. PSO is a new evolution algorithm based on the movement and intelligence of swarms looking for the most fertile feeding location, which can solve discontinuous, nonconvex and nonlinear problems efficiently. The proposed algorithm optimizes the weighted rate sum. These weights allow the system operator to place differing qualities of service or importance levels on each user, which makes it possible for the system to avoid the selfish‐optimum. We can show that the proposed algorithm converges to the global optimal solutions. Simulation results demonstrate that our algorithm can guarantee fast convergence within a few iterations and solve the nonconvex optimization problems efficiently. Copyright © 2008 John Wiley & Sons, Ltd.     

一种改进的大规模MIMO发射天线选择算法

为了降低天线选择算法在大规模多输入多输出（Multiple-Input Multiple-Output,MIMO）系统下的误码率和复杂度,以用户端接收的总功率为优化目标,提出一种最大化所有用户接收总功率的天线选择算法。该算法将优化目标函数转化为凸函数,并利用凸优化方法求得其有效解。仿真结果表明,所提天线选择算法与传统的最大和容量算法相比,具有较好的系统误码率性能,且运算复杂度低,但系统容量有所降低。     

Optimal Resource Allocation in Heterogeneous MIMO Cognitive Radio Networks

The optimal resource allocation in MIMO cognitive radio networks with heterogeneous secondary users, centralized and distributed users, is investigated in this work. The core aim of this work is to study the joint problems of transmission time and power allocation in a MIMO cognitive radio scenario. The optimization objective is to maximize the total capacity of the secondary users (SUs) with the constraint of fairness. At first, the joint problems of transmission time and power allocation for centralized SUs in uplink is optimized. Afterwards, for the heterogeneous case with both the centralized and distributed secondary users, the resource allocation problem is formulated and an iterative power water-filling scheme is proposed to achieve the optimal resource allocation for both kinds of SUs. A dynamic optimal joint transmission time and power allocation scheme for heterogeneous cognitive radio networks is proposed. The simulation results illustrate the performance of the proposed scheme and its superiority over other power control schemes.     

Resource allocation for sum-rate maximization in NOMA-based generalized spatial modulation

The Multiple-Input Multiple-Output (MIMO) Non-Orthogonal Multiple Access (NOMA) based on Spatial Modulation (SM-MIMO-NOMA) system has been proposed to achieve better spectral efficiency with reduced radio frequency chains comparing to the traditional MIMO-NOMA system. To improve the performance of SM-MIMO-NOMA systems, we extend them to generalized spatial modulation scenarios while maintaining moderate complexity and fairness. In this paper, system spectral efficiency and transmission quality improvements are proposed by investigating a sum-rate maximization resource allocation problem that is subject to the total transmitted power, user grouping, and resource block constraints. To solve this non-convex and difficult problem, a graph-based user grouping strategy is proposed initially to maximize the mutual gains of intragroup users. An auxiliary-variable approach is then adopted to transform the power allocation subproblem into a convex one. Simulation results demonstrate that the proposed algorithm has better performance in terms of bit error rate and sum rates.     

基于NOMA的无线携能D2D通信鲁棒能效优化算法

针对频谱短缺、基站负荷过高、通信系统功耗较大等问题,考虑不完美的信道状态信息,该文提出一种基于非正交多址接入的无线携能(SWIPT)D2D网络鲁棒能效(EE)最大化资源分配算法(SREA).考虑用户的服务质量约束以及最大发射功率约束,基于随机信道不确定性建立鲁棒能效最大化资源分配模型.利用Dinkelbach和变量替换方法,将原NP-hard问题转换为确定性的凸优化问题,通过拉格朗日对偶理论求得解析解.仿真结果表明,所提算法在保证蜂窝用户通信质量的同时,能够有效提高D2D用户的能效性和鲁棒性能.     

Power allocation scheme for maximizing spectrum efficiency and energy efficiency tradeoff for multi-cluster NOMA system

In this paper, a power allocation to maximize tradeoff between spectrum efficiency (SE) and energy efficiency (EE) is considered for the downlink non-orthogonal multiple access (NOMA) system with arbitrarily clusters and arbitrarily users, where the subcarriers of clusters are mutually orthogonal to each other. Specifically, an optimization problem of maximizing SE-EE tradeoff is formulated by optimizing power allocation among users under the constraints of user rate requirements. Then, the optimization problem is decomposed into a group of sub-problems with the aim of maximizing SE-EE tradeoff for each cluster, which is solved by using bisection method and monotonicity of function. Finally, the power allocation optimization problem among users is transformed into that between clusters, and a two steps inter-cluster power allocation algorithm is developed to solve this problem. Simulation results show that SE-EE tradeoff of the proposed scheme is better than that of the existing schemes.     

Joint interference alignment precoding based on the optimization algorithm on the Grassmannian manifold

To solve the interference problem between users of the multiuser MIMO system, we first transform the system sum rate maximization problem into joint optimization of interference signal power and useful signal power. On this basis, we propose a weighted interference alignment objective function, causing the system to obtain a higher sum rate by adjusting the weight with different signal-to-noise ratios. Then, we model the transmit subspace and the interference subspace on the Grassmannian manifold and propose joint interference alignment precoding based on the Grassmannian conjugacy gradient algorithm (GCGA-JIAP algorithm). In contrast to conventional interference alignment algorithms, our proposed algorithm can reduce the computational cost by transforming the constrained optimization of the complex Euclidean space into unconstrained optimization with the degenerate dimension on the Grassmannian manifold. Computer simulation shows that the proposed algorithm improves the convergence of the iterative optimization of the transmitter precoding matrix and the receiver postprocessing matrix and also improves the sum rate performance of the multiuser MIMO interference system.     

An efficient resource-allocation scheme for spatial multiuser access in MIMO/OFDM systems

Fast adaptive transmission has been recently identified as a key technology for exploiting potential system diversity and improving power-spectral efficiency in wireless communication systems. An adaptive resource-allocation approach, which jointly adapts subcarrier allocation, power distribution, and bit distribution according to instantaneous channel conditions, is proposed for multiuser multiple-input multiple-output (MIMO)/orthogonal frequency-division multiplexing systems. The resultant scheme is able to: 1) optimize the power efficiency; 2) guarantee each user's quality of service requirements, including bit-error rate and data rate; 3) ensure fairness to all the active users; and 4) be applied to systems with various types of multiuser-detection schemes at the receiver. For practical implementation, a reduced-complexity allocation algorithm is developed. This algorithm decouples the complex multiuser joint resource-allocation problem into simple single-user optimization problems by controlling the subcarrier sharing according to the users' spatial separability. Numerical results show that significant power and diversity gains are achievable, compared with nonadaptive systems. It is also demonstrated that the MIMO system is able to multiplex several users without sacrificing antenna diversity by using the proposed algorithm.     

多无人机协作的认知通信网络中能/谱效折中优化

在无人机（Unmanned Aerial Vehicle,UAV）认知通信网络中,其能量受限和通信高吞吐量问题备受关注。然而,能量效率（Energy Efficiency,EE）的提升可能会导致频谱效率（Spectrum Efficiency,SE）的下降。针对此问题,对UAV协作认知通信网络中EE和SE的折中优化进行了研究。首先,进行了感知时间、UAV通信的发射功率和判决门限各自对SE与EE两者的优化;其次,通过二分法求解使得EE和SE最大化的感知时间值,并通过穷尽搜索法分别求解感知时间、UAV通信的发射功率和判决门限对EE和SE折中优化问题的最优参数值。在此基础上,提出一种联合参数迭代优化算法,求解EE和SE的折中优化问题。仿真实验表明,SE和EE之间存在折中的权衡,并验证了所提优化方案的有效性。