多天线系统中的天线选择技术

多天线系统可以改进无线通信的容量,提高可靠性。然而,使用多天线。就意味着多个射频链路会增加天线的体积、功率、硬件,从而增加成本。天线选择是一种低成本、低复杂度的有效方法。可以利用多天线系统的多数优点。通过对MIMO系统中的天线选择技术进行概述,指出了这个领域内的一些还未解决的问题。     

一种快速的接收天线选择算法

天线选择是MIMO系统中一项重要的技术,它能从MIMO系统的多个发射天线和多个接收天线中选择出性能最好的一个或几个天线,从而以很小的性能损失换取成本的大幅降低,极大地提高了MIMO系统的性能价格比。最优算法具有较高的复杂度而限制了它的应用,文中从次优的递增递减算法入手,提出了一种具有更低复杂度的递增递减接收天线选择算法。仿真结果表明,该算法以很小的系统容量损失为代价换取了复杂度的降低。     

MIMO系统中的天线选择技术

MIMO系统是无线通信领域的研究热点,他能够极大地提高通信系统的容量和频谱利用率。然而使用多个射频的MIMO系统增加了天线的体积、功率和硬件,从而增加了成本。因此寻找具有MIMO天线优点且低价格、低复杂度的最优天线选择极具吸引力。总结了天线选择的方案、介绍了两类关键实现算法和最新研究进展,并在性能上进行分析比较,最后指出了该技术的实际应用问题。     

基于MIMO系统的天线选择

多天线MIMO（Multiple Input Multiple Output）系统利用多个收、发天线有效地改善无线通信系统性能，提高系统容量，增强系统可靠性。然而，由于使用多天线同时收发，这要求发射机和接收机使用与天线一样多的射频链路，增加了系统成本和复杂度。使用天线选择技术可以降低系统成本和复杂度，同时保留MIMO系统的优越性能。文中首先介绍了MIMO系统的实现方式，然后讨论天线选择的方法及性能，最后提出天线选择技术还存在的问题，并得出相关的结论。     

天线选择对分布式多入多出系统的容量影响研究

通过天线选择可以提高多输入多输出(MIMO)系统的容量,并能有效地降低MIMO系统的复杂度和射频成本.基于连续选择使MIMO系统容量增加最大的天线的方法,用矩阵及行列式运算导出了一种新的接收天线选择算法.将该算法用于分布式MIMO系统的容量研究,通过计算机仿真,结合Rice因子K及不同视距传播条件等因素对分布式MIMO系统上行信道容量的影响进行了研究.仿真结果在分布式无线通信组网及网络优化中具有指导意义.     

基于天线选择的低秩信道MIMO系统容量研究

最近的研究表明,在衰落信道多天线MIMO系统的容量随发射天线数线性增加.而对于低散射环境,即使信号经历独立的衰落,信道秩的降低也会严重影响系统容量的增加.对于这种低秩环境,选择天线发射或接收可以有效地降低系统的成本.本文研究了天线选择对低秩信道MIMO系统容量的影响.仿真结果表明,对不同的信道条件,适当选择天线发射不仅可以增加信道容量,而且可以降低系统的复杂度和射频成本.适当选择天线接收,在不严重降低系统容量的前提下,也达到了降低系统成本的目的.     

一种用于MIMO系统的快速天线选择算法

在多径衰落环境中, MIMO系统的信道容量随天线数的增加呈线性增加,发射/接收天线选择方法能以很小的性能损失换取射频成本的大幅度降低,使MIMO系统不完全受射频成本的限制。为快速选择出使系统容量最优的发射/接收天线子集,该文提出一种快速天线选择算法的改进算法。该算法通过实时更新优化参数,大大降低计算复杂度。仿真结果表明,该算法在不影响系统容量的情况下大大减少了计算时间。     

天线选择技术在MIMO中的应用

MIMO系统是当今无线通信领域的重要技术,但是它存在一个严重的缺陷:随着天线数量增多,系统的复杂度和成本大大增加。天线选择技术被认为是降低MIMO系统复杂度的有力方案。本文详细阐述了天线选择技术在MIMO系统中的应用,它能在保证系统传输速率的同时降低复杂度和误码率。     

多天线系统中的天线选择技术

近年来，多天线系统(也称为MIMO系统)引起了人们很大的研究兴趣，它可以增加系统的容量，改进误比特率(BER)。然而，获得这些增益的代价是硬件的复杂度提高，无线系统前端的复杂度、体积和价格随着天线数目的增加而增加。使用天线选择技术，就可以在获得MIMO系统优势的同时降低成本。     

大规模MIMO系统中收发联合阈值天线选择算法

在大规模多输入多输出(massive MIMO)系统中使用天线选择算法可提高能效和系统吞吐量,然而适用于传统MIMO系统的天线选择算法具有高复杂度,很难用于massive MIMO系统。为优化天线选择算法,以算法复杂度和系统容量为优化目标,提出了收发联合阈值天线选择算法。该算法在发射端使用最大范数双向天线选择算法进行天线选择,在接收端使用分组maxvol算法并通过仿真实验结果的预设阈值进行天线选择。仿真实验表明,收发联合阈值天线选择算法在降低复杂度的同时可以提高系统容量,与递增天线选择算法相比,系统容量最多可提高52.2 bit/s/Hz。提出的天线选择算法可以满足不同天线相关度和信噪比的传输环境。     

Energy efficiency of massive MIMO wireless communication systems with antenna selection

Massive multiple-input multiple-output (MIMO) requires a large number (tens or hundreds) of base station antennas serving for much smaller number of terminals, with large gains in energy efficiency and spectral efficiency compared with traditional MIMO technology. Large scale antennas mean large scale radio frequency (RF) chains. Considering the plenty of power consumption and high cost of RF chains, antenna selection is necessary for Massive MIMO wireless communication systems in both transmitting end and receiving end. An energy efficient antenna selection algorithm based on convex optimization was proposed for Massive MIMO wireless communication systems. On the condition that the channel capacity of the cell is larger than a certain threshold, the number of transmit antenna, the subset of transmit antenna and servable mobile terminals (MTs) were jointly optimized to maximize energy efficiency. The joint optimization problem was proved in detail. The proposed algorithm is verified by analysis and numerical simulations. Good performance gain of energy efficiency is obtained comparing with no antenna selection.     

无线通信系统的MIMO信道测量与建模

在多径信道中,使用多天线的M IMO(多输入多输出)无线系统能够比单天线系统提供更高的信道容量,而信道测量与建模是决定通信性能的一个重要因素。文中对目前国际范围内现有的M IMO信道测量和建模进行了研究,并进行了归纳和分类,同时分析了M IMO信道测量和建模的方法,指出了目前信道测量和建模中存在的问题,并给出了一些针对M IMO信道测量系统设计的建议。     

A Receive Antenna Subarray Formation Algorithm for MIMO Systems

Antenna selection techniques have been proposed to reduce the hardware complexity of MIMO wireless systems, while being efficient in terms of information rate achieved. Recently, an alternative method has been introduced to maximize the capacity of multiple antenna systems with reduced available RF chains, called antenna subarray formation. The method is based on the grouping of properly weighted antenna elements and initially was implemented with the use of an evolutionary optimization technique using the link capacity as a cost function. In this paper we propose an analytic algorithm for the subarray formation concept allowing for a more compact and tractable implementation     

Adaptive Antenna Subarray Formation for MIMO Systems

MIMO systems with reduced hardware complexity have attracted researchers' attention due to their high efficiency and low cost. Sub-optimum algorithms for antenna subset selection have been intensively studied in the literature. In this paper we present a new technique to maximize the capacity of multiple antenna wireless systems with reduced available RF chains. The technique is based on the adaptive formation of subarrays, i.e. the grouping of antenna elements and the application of appropriate element weights. The elements of each subarray and their weights are dynamically selected by an evolutionary optimization technique using the link capacity as a cost function     

Dynamics of spatial correlation and implications on MIMO systems

The use of multiple antennas has found various applications in the area of wireless communications. One such application has recently become very popular and is referred to as the multiple-input multiple-output (MIMO) antenna system. The main idea behind MIMO is to establish independent parallel channels between multiple transmit and receive antennas. Each channel uses the same frequency, and the transmissions occur simultaneously. In such a configuration, the amount of data transmitted increases linearly with the number of parallel channels, which is what makes MIMO so popular in the wireless world. The enormous capacity offered by MIMO systems is not realizable when the parallel channels are highly correlated. The goal of this article is to highlight the correlation concept and its impact on MIMO systems. Although correlation can be defined in many dimensions, here we focus on spatial correlation, and specifically consider antenna correlations in mobile units. We provide an overview of spatial correlation and present its underlying parameters in detail. Special attention is given to mutual coupling since it has signal decorrelation and antenna gain reduction effects. We then present how correlation in a MIMO system affects the amount of data that can be transmitted (MIMO capacity) and briefly review how power should be distributed with the knowledge of correlation. Analyses indicate that in real propagation environments, the high capacity gain of MIMO systems can be realized with improved antenna selection algorithms and power allocation strategies.     

Performance analysis of maximum likelihood detection in a MIMOantenna system

We provide an analysis of the performance of maximum likelihood detection (MLD) over flat fading channels in a wireless multiple input-multiple output (MIMO) antenna system. A tight union bound with an asymptotic form on the probability of symbol error rate (SER) for MIMO MLD systems with two-dimensional signal constellations (such as QAM and PSK) is introduced. Using this analytic bound, the performance of the MIMO antenna system is demonstrated quantitatively with respect to channel estimation, constellation size, and antenna configuration     

室内MIMO无线信道模型和信道容量研究

本文从工程的实际出发,首先提出一种修正的室内MIMO无线信道模型,该模型有效地修正先前室内MIMO无线信道模型的不足,具有明显吻合室内实际通信环境的特点,然后分析天线方向性以及天线单元间的互耦对室内MIMO无线信道容量的影响.数值模拟验证了这种影响,并得到在一定条件下互耦导致的天线方向图畸变产生角度分集,提高信道容量,互耦对空域相关性无影响的条件以及室内丰富的多径使天线方向性对信道容量的影响不明显等结论.最后,实验也证实理论分析.