MIMO-OFDM系统天线和子载波联合分配问题研究

Research on Combined Antenna and Subcarrier Allocation for MIMO-OFDM System

作者: 专业:信号与信息处理 导师:李一兵 年度:2010 学位:硕士  院校: 哈尔滨工程大学

Keywords

MIMO-OFDM, Combined antenna and subcarrier Allocation, PSO, GA

        在未来的宽带无线通信系统中,存在两个最严峻的挑战:多径衰落信道和带宽效率。OFDM通过将频率选择性多径衰落信道在频域内转化成平坦信道,从而减小了多径衰落的影响。这种多载波传输技术,其多载波之间相互正交,可以高效地利用频谱资源。MIMO技术充分开发空间资源,利用多个天线实现多发多收,在空间中产生独立的并行信道同时传输多路数据流,在不需要增加频谱资源和天线发送功率的情况下,有效的增加了系统的传输速度,提高了信道容量。天线和子载波联合分配的意思是系统调度器能够根据不同用户的无线信道衰落特性自适应地为每个用户选择天线和子载波来进行信息的传输。天线和子载波联合分配可以充分利用独立多用户环境中固有的分集增益,即多用户分集,系统的传输效率和频谱利用率将会显著提高。本课题属于基础理论研究,旨在针对现有的MIMO-OFDM系统的天线和子载波联合分配算法存在的不足,即普通的遍历方法用时太长,基于遗传算法的MIMO-OFDM系统自适应天线和子载波分配容易陷入局部最优,提出一种基于粒子群算法的MIMO-OFDM系统自适应天线和子载波联合分配算法,实现了快速、准确找到天线和子载波的最优联合分配方式的效果,使系统能够充分利用独立多用户环境中固有的分集增益,提高信道容量。总结本文的工作主要有以下几个方面:1.归纳总结了MIMO-OFDM系统自适应天线和子载波联合分配算法、粒子群算法及遗传算法的国内外研究现状;分析了目前的天线和子载波联合分配算法的缺点。2.针对基于遗传算法天线和子载波联合分配算法易陷入局部最优的缺陷,提出了一种“基于粒子群算法的天线和子载波联合分配算法”,将生成的随机向量作为最初的天线和子载波分配方案,将系统的容量作为适应度函数,通过迭代寻求最优的天线和子载波联合分配方案。仿真结果表明,在发送数据速率和误码率一定的条件下,本文提出的改进算法能更有效的自适应对天线和子载波进行联合分配,增大整个系统的信道容量。3.针对基于粒子群算法的自适应天线和子载波联合分配算法用C语言编程处理后,8×8天线的最优分配方案确定时间只需0.017S,16×16天线的最优分配方案确定时间只需0.031S,能够满足时隙要求和实际的硬件运算速度,有实际实现的可能。
    The future broadband wireless communication system exists two most serious challenges:multi-path fading channel and bandwidth efficiency. OFDM converts frequency selective multipath fading channel into a flat channel, thus reducing the impact of multipath fading. The multi-carrier of this multi-carrier transmission technology orthogonal each other, can make full use of spectrum resources. The full development of space resources—MIMO technology, generated independent of parallel channels as multiple data streams in space through using multiple antennas,without the need of additional spectrum resources and transmit power, and this technology effectively increases the channel capacity of the system. Combined antenna and subcarrier allocation system scheduler means that the system can adaptively select the antenna and sub-carrier for different users according to wireless channel fading characteristics. Combined antenna and subcarrier allocation can take advantage of the inherent diversity gain in the independent multi-user environment, that is, multiuser diversity, and the system’s transmission efficiency and spectral efficiency will be improved significantly.This issue belongs to the basic theoretical research, and the purpose is to solve the existing shortcomings of antenna and subcarrier allocation in the MIMO-OFDM system with using intelligent optimization algorithm to find the optimal antenna and subcarrier joint distribution method as soon as possible, making full use of inherent diversity gain in the dependent multi-user environment, increasing channel capacity. In this paper, adaptive antennas and subcarrier allocation of MIMO-OFDM system based on particle swarm optimization algorithm is proposed. We conclude that the work mainly in the following areas:1. Sums up the MIMO-OFDM system, adaptive antennas and subcarrier allocation algorithm, particle swarm optimization and genetic algorithm research status; the shortcomings of current antenna and subcarrier allocation algorithm;2. For the defect of falling into local optimal with genetic algorithm, a Antenna and subcarrier allocation algorithm based on Particle Swarm Optimization is proposed.It generates a random vector as the original antenna and subcarrier allocation program,and uses capacity of the system as a fitness function, finding the best antenna and subcarrier allocation scheme by evolution. Simulation results show that this algorithm improves the channel capacity of the system under certain sending data rate and bit error rate;3. Adaptive antennas and subcarrier allocation based on Particle Swarm Optimization algorithm is programming with C language, and 8X8 antenna program will get the optimal allocation with just 0.017S,16X16 antenna 0.031S,which means that it can meet the hardware requirements.
        

MIMO-OFDM系统天线和子载波联合分配问题研究

摘要5-7
ABSTRACT7-8
第1章 绪论11-21
    1.1 论文的研究背景和意义11-12
    1.2 课题研究现状12-18
        1.2.1 多用户自适应OFDM子载波分配13-15
        1.2.2 多天线MIMO-OFDM子载波分配15-16
        1.2.3 粒子群算法国内外研究现状16-17
        1.2.4 遗传算法国内外研究现状17-18
    1.3 课题研究内容及论文安排18-21
第2章 MIMO-OFDM技术概述21-32
    2.1 引言21
    2.2 OFDM技术简介21-26
        2.2.1 OFDM系统模型21-24
        2.2.2 OFDM技术特点24-26
    2.3 MIMO原理介绍26-28
    2.4 MIMO-OFDM系统的原理28-31
        2.4.1 MIMO和OFDM系统组合的必要性28-29
        2.4.2 MIMO-OFDM系统结构29-30
        2.4.3 MIMO-OFDM的系统的关键设计30-31
    2.5 本章小结31-32
第3章 MIMO-OFDM自适应天线与子载波分配32-45
    3.1 MIMO-OFDM中的自适应技术介绍32-33
    3.2 自适应资源分配算法33-35
        3.2.1 贪婪算法33-34
        3.2.2 注水算法34-35
    3.3 多用户OFDM子载波分配35-37
    3.4 天线选择技术37-38
    3.5 自适应天线选择和子载波分配38-44
    3.6 本章小结44-45
第4章 基于优化算法的MIMO-OFDM系统天线和子载波联合分配45-57
    4.1 遗传算法的基本原理45-50
        4.1.1 选择算子46-48
        4.1.2 交叉算子48-49
        4.1.3 变异算子49-50
    4.2 基于遗传算法的天线和子载波联合分配的实现50-52
    4.3 粒子群算法基本原理52-54
    4.4 基于PSO算法的天线和子载波联合分配算法的实现54-56
    4.5 本章小结56-57
第5章 基于优化算法的MIMO-OFDM系统自适应天线和子载波联合分配仿真实验57-67
    5.1 实验条件57
    5.2 基于优化算法的自适应天线和子载波联合分配的实现57-65
        5.2.1 8X8天线的自适应天线和子载波联合分配的实现57-61
        5.2.2 16X16天线的自适应天线和子载波联合分配的实现61-65
    5.3 本章小结65-67
结论67-69
参考文献69-74
攻读硕士学位期间发表的论文和取得的科研成果74-75
致谢75
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