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。      

基于上下行对偶性的多用户MIMO下行链路鲁棒收发机设计

针对多用户多天线(MIMO)下行链路信道状态信息(CSI)非理想的条件,该文研究SINR平衡问题及功率最小化问题的收发机设计。在信道误差受限情况下,该文建立worst-case SINR上下行链路对偶性,并基于该对偶性,为两种鲁棒收发机提出新的设计策略。该策略避免了凸优化内点迭代算法,从而大大降低复杂度。仿真结果显示,所提算法与现有的凸优化算法性能一致。     

合作认知无线网络中能效最优资源分配

针对能量受限的合作认知网络,该文研究在保证主用户服务质量要求下,认知用户能量效率最大化问题。认知用户利用信能同传技术接收主用户信号,并采用解码转发协议协助主用户通信。基于分式规划和引入辅助变量将原始非凸问题转换为凸优化问题进行求解,并提出一种迭代的资源分配算法。仿真结果表明,所提算法能够快速收敛于最优解。与能量合作方案相比,该文所采用方案能量效率显著提高,同时能更好地保证主用户服务质量要求。     

可重构智能反射面辅助的车联网资源分配算法研究

面对车辆通信对通信质量和频谱效率的更高要求，提出了一种可重构智能反射面（RIS）辅助的车联网资源分配算法。联合考虑RIS反射系数矩阵、功率分配和频谱共享方案，在确保V2V链路的可靠性条件下，建立一个V2I链路总容量最大化问题。由于该问题是一个变量之间高度耦合的非凸优化问题，难以直接求解。因此，在引入解析表达式近似V2V链路的中断概率后，利用块坐标下降（BCD）法将此问题分解为3个子问题，并通过引入辅助变量、逐次凸逼近（SCA）、匈牙利算法等对子问题进行求解，进而得到问题的近似次优解。仿真结果表明，所提算法具有良好的收敛性能，并能够有效地提高V2I链路总容量。     

基于能量效率的分布式星群下行链路功率分配方法研究

针对多波束卫星通信系统星上资源稀缺和能量利用效率不高的问题,本文提出了分布式星群网络下行链路中兼顾系统功耗和数据速率的功率分配方法,通过合理的资源分配来优化系统的能量效率.首先建立分布式星群功率分配模型,将复杂的分式问题转化为易于求解的减法形式问题,然后基于凸优化理论,提出功耗-数据速率权衡功率分配算法,并讨论了能量效率（energy efficiency）与频谱效率（spectral efficiency）之间的权衡关系.仿真结果验证了提出算法的有效性和EE-SE权衡关系,并分析了电路功耗对系统性能的影响.     

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。     

基于容量最大化的非迭代干扰对齐优化算法

现有分布式迭代干扰对齐算法没有考虑基站与用户终端处理能力的差异,使得基站和用户端都具有较高的计算复杂度和系统开销。针对下行链路和上行链路分别提出一种基于容量最大化准则的非迭代干扰对齐优化算法。所提算法在基站端使用基于容量最大化准则的优化方法求解发送预编码矩阵或接收抑制矩阵,在用户端使用迫零准则生成接收抑制矩阵或随机生成发送预编码矩阵。分析和仿真结果表明,所提的2种算法基站端的计算复杂度和系统开销与现有算法相当,但用户端的计算复杂度和系统开销大大降低,并且可以获得与最大信干噪比(Max-SINR)算法相当的系统性能。     

一种基于多小区OFDMA系统用户公平性约束的分布式资源分配算法

针对多小区OFDMA系统下行链路,研究了用户公平性约束下的资源分配问题,提出了一种多基站协作的迭代优化的分布式资源分配算法。每个小区根据干扰状况及用户公平性,迭代地进行子载波和功率的资源优化;而每次迭代中,根据用户公平性准则分配子载波,并将非凸的小区功率优化问题转化为其下界的凸问题,通过一个分布式算法来求解。通过仿真验证了算法的有效性;仿真结果表明,与传统网络的固定功率分配的情形相比,所提算法保证了用户之间的公平性并显著提高了系统吞吐量。     

基于时延优化的蜂窝D2D通信联合用户关联及内容部署算法

针对蜂窝网络传输性能及基站(BS)缓存能力受限,多用户内容请求难以满足用户服务质量(QoS)需求等问题,该文提出一种蜂窝终端直通(D2D)通信联合用户关联及内容部署算法。考虑到位于特定区域的多用户可能对于相同内容存在内容请求,该文引入成簇思想,提出一种成簇及内容部署机制,通过为各簇头推送热点内容,而簇成员基于D2D通信模式关联簇头获取所需内容,可实现高效内容获取。综合考虑成簇数量、用户关联簇头、簇头缓存容量及传输速率等限制条件,建立基于用户总业务时延最小化的联合成簇及内容部署优化模型。该优化问题是一个非凸的混合整数优化问题,该文运用拉格朗日部分松弛法,将原优化问题等价转换为3个凸优化的子问题,并基于迭代算法及Kuhn-Munkres算法联合求解各子问题,从而得到联合成簇及内容部署优化策略。最后通过MATLAB仿真验证所提算法的有效性。     

分布式IRS辅助毫米波MU-MISO系统联合波束成形设计

为解决非视距场景下毫米波多用户多输入单输出(MU-MISO)系统下行链路的可靠性通信问题,提出一种分布式智能反射表面(IRS)辅助多用户通信的联合波束成形设计方案.考虑功率和恒模约束,以用户加权和速率最大为目标,将基站有源波束成形和多个IRS无源波束成形联合建模为非凸优化问题.利用闭式分式规划技术解耦该联合优化问题为易...     

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.     

云无线接入网络的下行链路能效的优化

在下行云接入网络(C-RANs)中,核心网络内基站处理单元(BBU)通过前向回传线路向远端无线电头端(RRH)传输数据,然后RRHs通过无线接入链路转发数据。目前,C-RANs的下行链路的能效是C-RANs的研究热点。为此,对C-RANs下行链路能效进行研究,其目的在于最大下行链路的能效。通过对联合用户分配(UA)、RRH激活、数据率分配和信号预编码约束,建立能效最大化表述式,再利用基于逐次凸二次规划的迭代算法求解混合整数非线性问题。仿真结果表明,提出的联合优化设计提高了C-RANs的能效。     

蜂窝物联网中短包域能量效率最大化波束成形优化与设计

为了满足未来蜂窝物联网(IoT)中超高可靠和超低时延的要求,该文提出一种适用于多小区多用户超高可靠极低时延网络短包域公平性能量效率最大化算法。首先,以最小用户传输速率、每个发射机最大功率等约束为限制,构建了一个关于波束成形矢量的非线性分式规划资源配置模型。随后,采用变量代换、连续凸近似等技术,将原始非凸优化问题转化为标准的凸问题,进而提出一种短包域迭代能量效率最优化方法进行求解。最后,数值仿真结果验证了所提算法在短包域具有良好的能量效率性能。     

Robust Energy Efficiency Power Allocation for Uplink OFDM–Based Cognitive Radio Networks

This paper studies the energy efficiency power allocation for cognitive radio networks based on uplink orthogonal frequency‐division multiplexing. The power allocation problem is intended to minimize the maximum energy efficiency measured by “Joule per bit” metric, under total power constraint and robust aggregate mutual interference power constraint. However, the above problem is non‐convex. To make it solvable, an equivalent convex optimization problem is derived that can be solved by general fractional programming. Then, a robust energy efficiency power allocation scheme is presented. Simulation results corroborate the effectiveness of the proposed methods.     

基于非正交多址接入的网络切片联合用户关联和功率分配算法

为了满足网络切片多样化需求,实现无线虚拟资源的动态分配,该文提出在C-RAN架构中基于非正交多址接入的联合用户关联和功率资源分配算法。首先,该算法考虑在不完美信道条件下,以切片和用户最小速率需求及时延QoS要求、系统中断概率、前传容量为约束,建立在C-RAN场景中最大化长时平均网络切片总吞吐量的联合用户关联和功率分配模型。其次,将概率混合优化问题转换为非概率优化问题,并利用Lyapunov优化理论设计一种基于当前时隙的联合用户调度和功率分配的算法。最后采用贪婪算法求得用户关联问题次优解;基于用户关联的策略,将功率分配的问题利用连续凸逼近方法将其转换为凸优化问题并采用拉格朗日对偶分解方法获得功率分配策略。仿真结果表明,该算法能满足各网络切片和用户需求的同时有效提升系统时间平均切片总吞吐量。     

Joint energy efficiency and spectral efficiency optimization algorithm for UDN under the restriction of interference threshold and backhaul capacity

Aiming at the scenarios which consider the constraint of backhaul capacity restriction and interference threshold in ultra-dense networks (UDN),an integer linear programming (ILP) and Lagrangian dual decomposition (LDD) based joint optimization algorithm of energy efficiency and spectrum efficiency was proposed.In the proposed algorithms,the user association problem with the constraint of limited backhaul capacity was modelled as an ILP problem and then finished the connection between the user and the base station of microcell by solving this problem with dynamic programming method.Therefor,Lagrangian dual decomposition (LDD) was applied in an iteration algorithm for spectrum resource allocation and power allocation.The simulation results show that compared with traditional schemes,the proposed algorithm can significantly improve the energy efficiency and spectrum efficiency of system and use the microcell’s load capacity more efficiently.     

Online Power Control and Optimization for Energy Harvesting Communication System Based on State of Charge

In this paper, the online power control problem for energy harvesting wireless communication system with a finite storage capacity battery is addressed, where the channel state and energy harvesting rate are both unknown. A low complexity algorithm based online convex optimization is proposed to guarantee energy availability of energy harvesting node and maximize average long-term throughput. The proposed algorithm restricts maximum transmission power with the information of state of charge, and allocates transmission power based on historical information. In addition, energy availability constraint is given by rigorous theoretical analysis to guarantee the optimization of average long-term throughput. Simulations have been conducted to demonstrate the effectiveness of the algorithm without considering probability distribution of energy arrival or channel coefficients. The proposed algorithm outperforms counterparts in different energy harvested rates.

     

云接入网络中基于容量最大化的射频拉远头选择算法设计

不同于传统的LTE-Advanced网络,云接入网络(C-RAN)通过前程链路将复杂数据处理从射频拉远头(RRH)转移至基带单元池(BBU Pool)。然而,由于C-RAN前程链路容量有限,系统总吞吐量并不一定能显著提升。在本文中,我们通过优化RRHs选择实现吞吐量的最大化。更准确地说,本文在前程容量约束条件下设计了一种选择活动RRHs集的优化算法,以实现系统容量的最大化。此外,本文分别推导了低信噪比（Low SNR）与高信噪比(High SNR)下,选择RRHs数目的渐进最优的闭合表达式。仿真结果表明,比起其他方案,本文所提出的RRH选择方案能够在有限的前程容量限制与任意给定RRHs数量下,有效实现吞吐量的提升。      

Energy-Spectral-Efficiency Tradeoff in Interference-Limited Wireless Networks

Spectral efficiency (SE) is an important metric in traditional wireless network design. However, as the development of high-data rate services and rapid increase of energy consumption, energy efficiency (EE) has received more and more attention. In this paper, we investigate the EE–SE tradeoff problem in interference-limited wireless networks. Different from previous researches, we try to optimize EE and SE simultaneously. Firstly, the problem is formulated as a multi-objective optimization problem (MOP), with the constraint of transmit power limit. Then, we convert the MOP to a single-objective optimization problem by the weighted linear sum method. We present an algorithm utilizing difference between two convex functions programming (DCP) to handle with SE optimization problem (SD). EE optimization problem can be solved by an algorithm (EFD) consists of fractional programming embedded with DCP. While for EE–SE tradeoff problem, a particle swarm optimization algorithm is proposed (ESTP) to deal with it. Simulation results validate that the proposed algorithm can efficiently balance EE and SE by adjusting the value of weighted coefficient, which could be used to design a flexible energy efficient network in the future.