MIMO系统中天线选择的关键技术及其应用

多天线MIMO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.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.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.文中考虑了天线选择系统的一些实际应用问题,RF预处理,训练序列等的实际应用.最后,给出了一些天线选择的结论.     

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。     

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。     

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。     

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。     

多天线系统的一种改进软判决译码算法

推导了采用垂直贝尔实验室分层空时(V-BLAST,Vertical-Bell Laborato-ries Layered Space-Time)结构的多天线(MIMO,Multiple Input and Multiple Out-put)系统中一种改进的软判决球译码(SD,Sphere-decoding)算法.结合LDPC码,仿真验证了该算法的性能与复杂度并与一种经典算法的性能及复杂度进行了比较.结果表明,合理选择参数,改进算法的计算复杂度可低于经典算法的计算复杂度,而其性能接近经典算法的性能.     

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。     

TD-LTE新技术之MIMO

1MIMO简介目前多天线技术(MIMO,Multiple Input Multiple Output)已经成为了3G/4G系统的关键技术之一。为了满足LTE在高数据率、高容量方面的需求,LTE系统支持应用MIMO技术。下行MIMO技术包括空间复用、波束赋形和传输分集,目前MIMO技术下行基本天线配置为2*2,即2天线发送和2天线接收,最大支持4天线进行下行方向四层传输。上行MIMO技     

天线选择策略在MIMO中继系统中的应用及改进

为了降低MIMO系统的射频链路成本同时保证其性能增益,该文在两跳MIMO中继系统中分析了理想和几种优化的天线选择策略,并分别在放大转发和译码转发两种中继方式下得出了系统中断概率表达式。最后,通过对不同天线选择策略下系统中断概率的数值仿真对比其系统性能,得出了适用于实际通信系统的低复杂度的最佳策略。     

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.     

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     

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     

Antenna selection in MIMO systems

Multiple-antenna systems, also known as multiple-input multiple-output radio, can improve the capacity and reliability of radio communication. However, the multiple RF chains associated with multiple antennas are costly in terms of size, power, and hardware. Antenna selection is a low-cost low-complexity alternative to capture many of the advantages of MIMO systems. This article reviews classic results on selection diversity, followed by a discussion of antenna selection algorithms at the transmit and receive sides. Extensions of classical results to antenna subset selection are presented. Finally, several open problems in this area are pointed out.     

MIMO systems with full and reduced hardware complexity

MIMO (multiple-input-multiple-output) systems propose enormous gains in the capacity of wireless systems without requiring more spectral resources. This paper first gives an overview of the use of MIMO for diversity and spatial multiplexing, and the use of channel state information in MIMO systems. It then explores the use of antenna selection as a means for the reduction of the hardware complexity. It is shown that the performance in a spatial-multiplexing application is almost as good as that of full-complexity systems as long as the number of RF chains is at least as large as the number of data streams.     

Hybrid Beamforming for Massive MIMO Using Rectangular Antenna Array Model in 5G Wireless Networks

Fifth and future generation (5G and B5G) wireless networks aim to serve users with higher data rates and lower latency. Data traffic due to the rapid growth in communication has motivated the study of Multiple Input Multiple Output (MIMO) systems. They utilize multiple antennas in both transmitter and receiver sides. It is necessary to improve the existing technology to achieve fast and reliable communication. In this research work, a rectangular array antenna based hybrid beamforming in a massive MIMO model has been proposed to improve the spectral efficiency of the system. Thus channel capacity with small RF chains is used. To achieve the high signal strength in the main lobe, Chebyshev tapering has been used to suppress the side lobes signals. In this manner, the proposed Hybrid Beamforming for Massive Output MIMO has been realized with a small complexity and higher spectral efficiency. In this research work, the spectral efficiency of both proposed Hybrid and fully-digital beamforming with a different number of RF chains for a various number of antennas at the transmitter, the receiver side has been analyzed. From the simulation results, it has been observed that the proposed rectangular array antenna based Hybrid beamforming in a massive MIMO system reduces the computational complexity up to 99% as compared with conventional fully digital beamforming to achieve the same spectral efficiencies, which is a productive model for 5G wireless networks.

     

Optimal antenna selection based on capacity maximization for MIMO systems in correlated channels

Recent work has shown that multiple-input multiple-output (MIMO) systems with multiple antennas at both the transmitter and receiver are able to achieve great capacity improvement. In such systems, it is desirable to select a subset of the available antennas so as to reduce the number of radio frequency (RF) chains. This paper addresses the problem of antenna selection in correlated channels. We consider a narrowband communication system with M transmit and N receive antennas. We present the criterion for selecting the optimal L/sub t/ out of M transmit and L/sub r/ out of N receive antennas in terms of capacity maximization, assuming that only the long-term channel statistics, instead of the instantaneous channel-state information, are known. Simulations will be used to validate our theoretical analysis and demonstrate that the number of required RF chains can be significantly decreased using our proposed selection strategy, while achieving even better performance than the conventional MIMO system without antenna selection.