车联网中基于移动边缘计算的内容感知分类卸载算法研究

随着智能交通的快速发展,车辆终端产生大量需要实时处理的数据消息,而在有限资源上的竞争将会增加消息处理的时延,且对终端设备造成很大的能量消耗。针对时延和能量损耗的均衡关系,该文提出一种基于移动边缘计算(MEC)的内容感知分类卸载算法。首先根据层次分析法对安全消息进行优先级划分,然后建立时延和能量损耗的最优任务卸载模型,通过给时延和能量损耗赋予不同的权重系数构造关系模型,并利用拉格朗日松弛法将非凸问题转化为凸问题,从而结合次梯度投影法和贪婪算法得到问题的可行解。性能评估结果表明,该算法在一定程度上改善了消息处理时延和能量损耗。     

无线供能移动边缘网络中计算时延最小化资源分配方法研究

针对无线供能移动边缘计算(MEC)网络,该文将计算时延定义为数据卸载与计算所消耗的时间,并提出一种节点计算时延之和最小化的多维资源分配方法。首先,在节点能量因果约束下,通过联合优化专用能量站工作时长、任务分割系数、节点计算频率和发射功率来建立一个计算时延之和最小化的多维资源分配问题。由于存在优化变量耦合与max-max函数,所建问题非凸且无法采用凸优化工具获取最优解。为此,通过引入一系列松弛变量和辅助变量来进行优化问题简化以及优化变量解耦,并在此基础上,通过深入分析简化问题的结构特性,提出一种基于二分法的迭代算法来求解原问题的最优解。最后,计算机仿真验证了所提迭代算法的正确性以及所提资源分配方法在计算时延方面的优越性。     

基于IRS辅助的异构网络中超可靠低时延通信波束成形算法设计

为了增强微小区内超可靠低时延通信(URLLC)业务在异构网络场景下的传输性能,该文提出一种基于智能超表面(IRS)辅助通信网络下最大化用户和速率的波束成形算法。异构网络中微小区采用短包通信技术,在保证宏小区用户通信质量的前提下,使用IRS提高微小区用户在一定解码错误概率下的短数据包传输性能,建立一个联合优化波束向量和IRS相移向量的微小区用户和速率最大化问题模型。通过交替固定优化变量的方式,将该非凸优化问题拆分为两个子问题,利用逐次凸逼近(SCA)的方法将原问题转换成凸优化问题,并利用交替优化算法对该问题进行求解。仿真结果表明,该算法通过部署IRS可以有效减弱异构场景下对于微小区用户的干扰,同时由于IRS的部署能够有效优化波束成形向量进而提高微小区用户的短包传输性能,并且IRS的通信增强效果与微小区用户的解码错误概率以及IRS反射单元的数量有直接关系。     

基于输入队列的光分组交换短包抢先调度

光分组交换的输出队头阻塞引起分组的平均排队时延增加.分析了可变长分组的特点,提出了基于抢先方式的短包抢先调度(PSPP)算法,以减少分组在输入排队中的平均等待时间.在PSPP算法中,短包可以抢占长包的传输时间,获得优先的服务.分析和仿真结果表明,当到达业务负载为中或较低时,短包优先调度算法使短包的平均排队时延接近零,所有分组的平均等待时延减小,该算法还保证具有实时特性的TCP业务获得较低的平均等待时延.     

基于智能反射面辅助的无线供电通信网络鲁棒能效最大化算法

为了解决能量收集效率易受到障碍物阻挡和信道不确定性影响的问题,该文提出一种基于智能反射面(IRS)辅助的无线供电通信网络鲁棒能效(EE)最大化算法。首先,考虑最小收集能量、IRS相移、最小吞吐量等约束,基于有界信道不确定性,建立一个联合优化能量波束、相移、传输时间的多变量耦合非线性资源分配模型。然后,利用最坏准则、变量替换和S-Procedure等方法,将原非凸问题转换为确定性凸优化问题,同时,提出一种基于迭代的鲁棒能效最大化算法进行求解。仿真结果表明,与现有算法比较,该文算法具有较好的能效和鲁棒性。     

基于时空多样性编码的低轮值无线传感器网络可靠传输算法

提出了适用于低轮值无线传感器网络的可靠传输算法,主要特点是发掘并利用低轮值无线传感器网络特有的时空相关性,结合编码机制以获得较高的能量效率和较低的传输时延。针对编码块在多条路径上的最优分配问题进行了建模。由于求解最优策略是NP难题,给出了近似算法,并通过仿真分析了不同参数对近似算法的影响。仿真结果表明,基于时空多样性编码的分配算法能够在较低的能量开销条件下,实现低时延的可靠传输。     

WRAN系统中基于自适应内插的信道估计算法

提出了一种基于自适应复系数内插的信道估计算法,改善了无线区域网络（WRAN）系统对抗动态多径时延的能力。WRAN是第一个采用认知无线电技术改善频谱效率的宽带接入标准,系统在下行链路中采用了正交频分复用（OFDM）调制技术,而信道估计技术对于采用相干解调的OFDM系统十分重要。传统的OFDM信道频域响应（CFR）估计算法通常采用实系数频域内插的方式,在对抗WRAN系统长多径时延信道时,不能有效地工作。该文在研究实系数FIR内插变换域响应的基础上,提出了一种复系数内插算法。为了同时适用于短时延信道,提出了一种低复杂度、自适应匹配信道最大多径时延的算法。通过仿真,验证了该算法能够对抗更大的多径时延,提高信道估计的精度,改善系统误码性能。      

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。     

基于混合量化移相器的毫米波大规模MIMO预编码设计

针对毫米波大规模MIMO(Multiple-Input Multiple-Output)系统能量效率低下的问题,提出一种基于混合精度移相器量化的混合预编码设计方法.首先考虑到系统能量效率闭合表达式难以直接求解,通过Dinkelbach方法将能量效率优化问题转换为范数最小化的形式,然后将其进一步解耦为混合预编码矩阵和混合精度移相器量化配比问题,实现非凸能效低范数和的凸优化转化,最后通过引入酉矩阵松弛恒模约束求解混合预编码矩阵,并采用增量搜索算法求解混合量化移相器精度配比向量,从而实现系统能量效率最大化.仿真结果表明,所提出的混合精度量化算法的系统频谱效率优于传统单精度量化算法,且具有更高的系统能量效率.同时,当多数据流传输时,所提出混合精度量化结构的系统性能接近全数字结构.     

智能反射面辅助的无线携能通信系统优化问题研究

本文针对智能反射面辅助的无线携能通信系统，提出了一种面向能量效率的信号传输方案，该方案在基站处发射信息与能量的混合信号，通过联合优化基站的信息信号波束、能量信号波束和智能反射面处的相移来实现系统能量效率的提升。由于优化问题非凸且三个优化变量相互耦合，难以直接获取最优解。因此本文采用交替优化将原问题转换为两个非凸子问题进行交替迭代求解，并借助Dinclebach与半正定松弛等方法将非凸子问题转化为凸问题解决，最终获得原问题的次优解。数值仿真结果表明，本文所提方案在能量效率方面优于现有方案。     

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。      

Sling-shot spider optimization algorithm based packet length control in wireless sensor network and Internet of Things-based networks

Wireless sensor network (WSN) is a well-developed domain suitable in the optimal collection and processing of information needed for the present world. For processing and transmitting the correct data by the network, it is affected by many disrupting factors like interference, battery life, distance between the nodes, and redundant data. There have been many methods proposed in the past. Among all the disrupting factors, routing has been the most studied problem in the WSN literature, whereas packet length sizing has been the most untouched topic compared to routing. Sizing the packet lengths to transmit it through the network is important to get error-free data and run an energy-efficient network. In this paper, we propose a sling-shot spider optimization (S²SO) algorithm for packet length optimization in WSN and Internet of Things (IoT)-based networks. The S²SO algorithm is developed based on the spider's unique behavior of catching prey. The proposed algorithm is implemented in three types of networks: 1-hop, 2-hop, and multi-hop networks. A mathematical model for the communication channel, energy efficiency, and energy consumption is developed for all three types of networks. The proposed packet length optimization model is simulated in MATLAB software and compared with conventional bio-inspired algorithms. The results show that the proposed algorithm is very fast in finding the optimal results and transmits optimal packet size with low error rate of 0.2 p.u. and a high efficiency of 98%.     

单天线功率约束下的能效预编码优化设计

针对多小区多用户下行链路系统,研究了能效传输预编码设计问题。不同于传统设计中的和功率约束,由于实际系统中各天线的功率由其各自配备的功率放大器的线性度所限制,考虑更加实际的单天线功率约束。目标优化问题建模为最大化能效/谱效,同时满足各用户速率约束以及单天线功率约束。为了求解该优化问题,利用凸近似逼近的方法,将原始问题转化为凸优化问题,并提出了一种有效迭代优化算法求解该优化问题。MATLABAL数值仿真结果验证了所提算法的有效性。      

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.     

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。      

Link optimization for energy‐constrained wireless networks with packet retransmissions

With the objective to minimize the energy consumption for packet based communications in energy‐constrained wireless networks, this paper establishes a theoretical model for the joint optimization of the parameters at the physical layer and data link layer. Multilevel quadrature amplitude modulation (MQAM) and automatic repeat request (ARQ) techniques are considered in the system model. The optimization problem is formulated into a three dimensional nonlinear integer programming (NIP) problem with the modulation order, packet size, and retransmission limit as variables. For the retransmission limit, a simple search method is applied to degenerate the three dimensional problem into a two dimensional NIP problem, for which two optimization algorithms are proposed. One is the successive quadratic programming (SQP) algorithm, combining with the continuous relaxation based branch‐and‐bound method, which can obtain the global optimal solution since the continuous relaxation problem is proved to be hidden convex. The other is a low‐complexity sub‐optimal iterative algorithm, combining with the nearest‐neighboring method, which can be implemented with a polynomial complexity. Numerical examples are given to illustrate the optimization solution, which suggests that the joint optimization of the physical/data link layer parameters contributes noticeably to the energy saving in energy‐constrained wireless networks. Copyright © 2010 John Wiley & Sons, Ltd.     

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。     

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.

     

信号相关杂波背景中极化雷达发射波形优化

波形优化可有效抑制干扰,显著改善雷达探测性能.针对全极化雷达,考虑发射波形满足能量和相似性双重约束,以最大化信杂噪比为准则,对发射波形和接收滤波器进行联合优化.该文设计了一种波形和滤波器的迭代优化算法,该方法序贯提高输出信杂噪比.算法的每一次迭代需要分别解决一个凸问题和隐凸问题,整个算法的计算量与迭代次数和接收滤波器长度分别呈线性和多项式关系.最后,通过仿真实验分析了算法的收敛性、优化波形的模糊度函数方面的性质,与其他算法进行了对比,结果表明:与现有方法相比,该文方法可实现信杂噪比的有效提升.