分布式MIMO系统一种基于容量最大化准则的快速天线选择算法研究

由于分布式MIMO系统中的穷举天线选择算法复杂度较高,难以实现的缺点,因此本文提出了一种低复杂度的基于容量最大化准则的快速天线选择算法（Fast Antenna Selection Algorithm based on Maximum Capacity Criteria,简称FASAMCC）。该算法以容量最大化为依据进行端口的动态选择,并采用快速天线选择算法来进行天线的选择。仿真表明FASAMCC不仅复杂度低,而且其性能接近穷举算法。     

基于特征值估计的发射天线选择算法

现有的大多数发射天线选择算法都是假设信道是独立衰落的,这与实际的传播环境不相符。文章在相关信道的前提下研究MIMO(多输入多输出)系统,提出一种新的基于特征值估计的发射天线选择算法。算法通过特征值下界估计的方法,每次迭代中选择使信道矩阵最小特征值下界最大的列,来提高最小特征值并降低小天线间的相关性,使得容量最大化并使误码率最小化。理论分析结果表明,该算法在所选天线数目较多场合下具有较低的复杂度,同时其容量和误码性能优于随机选择算法,接近最优选择算法。     

MIMO系统中的接收天线选择算法研究

多输入多输出(MIMO)系统能极大地提高通信系统的容量和频谱利用率,然而多个射频链路的使用增加硬件成本和信号处理负担。最优的选择算法(穷举算法)和次优算法(递增或者递减算法)均能减少系统的成本和复杂度。但在天线规模较大时,选择天线时的计算量仍较大。提出一种基于信道矩阵特征空间的范数算法,仿真表明:相对于最优算法(穷举算法)和次优算法(递增或者递减算法),该算法能在损失较少容量的同时,极大地减少系统天线选择的计算时间。     

分布式MIMO系统中的一种可控式天线选择

基于信道容量最大化准则,提出了一种专门针对分布式多入多出（MIMO）系统的可控式天线选择算法。该算法在射频链路数（Lt）固定的前提下分两步执行,第一步进行动态的端口选择,第二步利用穷举搜索法在已选定的端口间选出Lt个最优天线,通过门限值的设定控制天线选择的复杂度和性能的平衡。仿真结果表明,在合适的门限值范围内,该算法具有很好的跟踪特性,具有接近于最优的容量性能,同时大大降低了天线选择复杂度。     

基于天线选择和功率分配的联合迭代优化算法

当使用所有天线进行无线数据传输时，大规模多输入多输出（Multiple-Input multiple-Output,MIMO）系统中的基站需要使用与天线数相同的射频链路，导致系统的实现复杂度增加，降低了系统的能效。针对能效降低的问题，提出了一种天线选择和功率分配的联合迭代优化算法。该算法在给定初始发送功率的条件下，随机生成一个天线集合作为内循环的初始值，内循环每次从余下的天线集合中选择一根具有最大能效的天线进行替换，得出最优天线集合，求出相应的最优发送功率，并以此作为下次外循环发送功率的初始值。仿真结果表明，所提算法在降低计算复杂度的前提下，几乎可以达到近似于最优穷举搜索算法的能效性能。     

GMD V-BLAST系统天线选择

基于最大容量和最小差错率准则,研究了在GMD V-BLAST系统中的天线选择问题。选用以信道矩阵非零奇异值的几何均值最大化为目标函数,可以避免容量与差错率性能之间的矛盾。在所有可用天线中进行选择的全搜索算法虽有最佳性能,但复杂度太高。基于贪婪算法,对发射天线采用快速的逐增选择策略,对接收天线采用快速的逐减选择策略,可以显著降低计算的复杂性。计算机仿真结果表明,所采用的快速天线选择算法可以较低的复杂度获得接近全搜索法的容量和分集增益。     

一种基于相异度的接收天线选择算法

对于多输入多输出系统天线选择算法而言,穷尽搜索算法能够达到最优的性能,但包含较多矩阵运算,计算复杂度较高。而传统基于相关度和相似度的天线选择算法虽具有较低计算量,但损失了较大的容量性能。针对这一问题,以容量性能为目标,提出了基于相异度的接收天线选择算法,分析了不同相异度下所提选择准则对系统性能的影响。与传统相关度和相似度天线选择算法相比,所提算法有效降低了计算复杂度,改善了系统的容量性能,仿真结果表明:所提算法具有较好的系统性能,适用于实时通信系统。     

基于正交空分复用的多用户调度策略

为了消除多用户MIMO下行系统的多用户间干扰以及改进系统的误码率性能,研究了块对角化预编码与几何均值分解的联合方案(BD-GMD).针对BD-GMD系统的资源分配和用户调度,对比了等功率分配和注水算法对系统的影响,并基于系统容量最大化,提出了一种根据用户信道的子空间特性的低复杂度的用户调度算法.此外,对比分析了穷举搜索算法和传统的贪婪算法.数值仿真结果表明,文中所提出的基于正交投影的多用户调度算法充在保证系统容量的同时降低了算法复杂度.     

60GHz 室内信道下一种快速天线选择算法

传统的基于信道容量最大化准则的天线选择算法虽然使信道容量达到了最大化,但是计算复杂度很高。针对计算复杂度高的问题, 提出了一种基于Doolittle-QR 分解的低复杂度天线选择算法。该算法基于Doolittle-QR 分解,可以快速选择出使系统容量最大化的天线。与传统的天线选择算法相比,该算法的计算复杂度不仅有效地降低了, 而且容量性能相近。在60GHz 室内信道下,仿真实验结果表明, 该算法具有良好的容量性能,优于随机天线选择算法,接近最优天线选择算法。     

一种改进的麻雀搜索天线选择算法

为解决麻雀搜索算法在多输入多输出(Multiple-Input Multiple-Output, MIMO)系统中进行天线选择时,存在过早收敛导致系统容量非最优的问题,提出了一种改进的麻雀搜索天线选择算法。该算法首先结合0-1规划推导出信道容量函数,将其作为适应度函数。在天线选择子集寻优过程中,通过引入自适应变异,有效增加了个体变化的多样性。最后将禁忌搜索算法融入到算法中,并加速算法收敛。仿真结果表明,改进的麻雀搜索天线选择算法能够有效降低MIMO系统进行天线选择时的运算复杂度,在保证收敛速度的同时,最终得到的系统信道容量趋近于最优算法。     

Symbol detection in spatial multiplexing system using particle swarm optimization meta-heuristics

Symbol detection in multi-input multi-output (MIMO) communication systems using different particle swarm optimization (PSO) algorithms is presented. This approach is particularly attractive as particle swarm intelligence is well suited for real-time applications, where low complexity and fast convergence is of absolute importance. While an optimal maximum likelihood (ML) detection using an exhaustive search method is prohibitively complex, PSO-assisted MIMO detection algorithms give near-optimal bit error rate (BER) performance with a significant reduction in ML complexity. The simulation results show that the proposed detectors give an acceptable BER performance and computational complexity trade-off in comparison with ML detection. These detection techniques show promising results for MIMO systems using high-order modulation schemes and more transmitting antennas where conventional ML detector becomes computationally non-practical to use. Hence, the proposed detectors are best suited for high-speed multi-antenna wireless communication systems. Copyright © 2008 John Wiley & Sons, Ltd.     

一种MIMO系统中的快速广义复球形解码算法

该文提出了一种广义复球形解码算法。它能处理多输入多输出系统(MIMO)中发送天线多于接收天线的情形,并能同时检测具有格型结构和不具有格型结构的二维空间星座信号。该算法对信号矢量的超定部分进行优化搜索,从而避免了穷尽搜索的高复杂度。仿真结果表明该广义复球形解码算法的复杂度明显低于采用穷尽搜索策略的复杂度。     

一种新型低复杂度双层分组天线选择算法

针对平坦相关瑞利衰落信道环境下的端到端大规模MIMO系统复杂度过高的问题,提出一种基于离散布谷鸟搜索的低复杂度双层分组天线选择算法。该算法首先基于天线信道相关性对大规模天线阵列进行分组处理,进而利用新型双层算法对分组的天线集合进行优化天线选择。其中,新型双层算法的第一层是每小组天线基于离散布谷鸟搜索的内部选择,第二层是对第一层选择的所有天线利用离散布谷鸟搜索进行最终的选择。提出的新型天线选择算法可有效降低大规模MIMO系统复杂度。仿真结果验证了在平坦相关瑞利衰落信道环境下,提出的天线选择算法能够以较低选择复杂度获得接近最优选择方法的容量性能和较优的BER性能。     

Receive antenna selection for MIMO systems over correlated fading channels

In this letter, we propose a novel receive antenna selection algorithm based on cross entropy optimization to maximize the capacity over spatially correlated channels in multiple-input multiple-output (MIMO) wireless systems. The performance of the proposed algorithm is investigated and compared with the existing schemes. Simulation results show that our low complexity algorithm can achieve near-optimal results that converge to within 99% of the optimal results obtained by exhaustive search. In addition, the proposed algorithm achieves near-optimal results irrespective of the mutual relationship between the number of transmit and receive antennas, the statistical properties of the channel and the operating signal-to-noise ratio.     

基于线性预编码的异步MIMOOFDM迭代检测算法

In asynchronous Multiple-Input-Multiple-Out-put Orthogonal Frequency Division Multiplexing(MIMO-OFDM) over the selective Rayleigh fading channel, the performance of the existing linear detection algorithms improves slowly as the Signal Noise Ratio (SNR) increases. To improve the performance of asynchronous MIMO-OFDM, a low complexity iterative detection algorithm based on linear precoding is proposed in this paper. At the transmitter, the transmitted signals are spread by precoding matrix to achieve the space-frequency diversity gain, and low complexity iterative Interference Cancellation(IC) algorithm is used at the receiver, which relieves the error propagation by the precoding matrix. The performance improvement is verified by simulations. Under the condition of 4 transmitting antennas and 4 receiving antennas at the BER of 10-4 , about 6 dB gain is obtained by using our proposed algorithm compared with traditional algorithm.     

A Low-Complexity Receive-Antenna-Selection Algorithm for MIMO–OFDM Wireless Systems

In this paper, a novel low-complexity antenna-selection algorithm based on a constrained adaptive Markov chain Monte Carlo (CAMCMC) optimization method is proposed to approach the maximum capacity or minimum bit error rate (BER) of receive-antenna-selection multiple-input multiple-output (MIMO)–orthogonal frequency division multiplexing (OFDM) systems. We analyze the performance of the proposed system as the control parameters are varied and show that both the channel capacity and the system BER achieved by the proposed CAMCMC selection algorithm are close to the optimal results obtained by the exhaustive search (ES) method. We further demonstrate that this performance can be achieved with less than 1% of the computational complexity of the ES rule and is independent of the antenna-selection criteria, outage rate requirements, antenna array configuration, and channel frequency selectivity. Similar to the existing antenna-selection algorithms, both channel capacity and system BER improvements achieved by the proposed CAMCMC method are reduced as the channel frequency selectivity increases. Therefore, we conclude that, whether it is designed to maximize the channel capacity or minimize the system BER, the CAMCMC-optimization-method-based antenna-selection technique is appropriate for a MIMO–OFDM system with low frequency selectivity.      

一种多用户MIMO-OFDM系统中的天线与子载波分配算法

天线与子载波分配是多用户MIMO-OFDM系统无线资源管理中的重要内容.在分析了多用户MIMO-OFDM系统下行链路中最优天线与子载波分配算法的基础上,提出了一种次优的低复杂度天线与子载波分配算法.该算法通过先选择天线再选择用户从而避免了最优算法中的遍历搜索.仿真结果表明,在运算量大大降低的情况下,所提算法获得的系统最大容量和最优算法所获得的最大容量相比相差不大,而且空间和频率联合优化的结果使其性能远远优于仅在MIMO环境下进行子载波分配的算法.     

Joint estimation of the channel and I/Q imbalance for two-way relay networks

In modern day communication systems, the massive MIMO architecture plays a pivotal role in enhancing the spatial multiplexing gain, but vice versa the system energy efficiency is compromised. Consequently, resource allocation in-terms of antenna selection becomes inevitable to increase energy efficiency without having any obvious effect or compromising the system spectral efficiency. Optimal antenna selection can be performed using exhaustive search. However, for a massive MIMO architecture, exhaustive search is not a feasible option due to the exponential growth in computational complexity with an increase in the number of antennas. We have proposed a computationally efficient and optimum algorithm based on the probability distribution learning for transmit antenna selection. An estimation of the distribution algorithm is a learning algorithm which learns from the probability distribution of best possible solutions. The proposed solution is computationally efficient and can obtain an optimum solution for the real time antenna selection problem. Since precoding and beamforming are also considered essential techniques to combat path loss incurred due to high frequency communications, so after antenna selection, successive interference cancellation algorithm is adopted for precoding with selected antennas. Simulation results verify that the proposed joint antenna selection and precoding solution is computationally efficient and near optimal in terms of spectral efficiency with respect to exhaustive search scheme. Furthermore, the energy efficiency of the system is also optimized by the proposed algorithm, resulting in performance enhancement of massive MIMO systems.

     

异步V-BLAST中基于功率扩展的迭代并行干扰消除

在平坦瑞利衰落信道下,异步V-BLAST系统中,现有检测算法随信噪比提高误码率性能改善缓慢。为此,该文提出一种基于预处理矩阵的迭代检测算法:在发射端,通过预处理矩阵将发射信号扩展到整个数据帧上,以获取空时分集度;在接收端,采用低复杂的迭代并行干扰消除方法,由于在迭代过程中干扰重建基于预处理矩阵,所以上次迭代的检测误差被扩展,降低了迭代过程中的误差传播。仿真验证了所提方法的有效性,在8发4收场景下,误码率为10-3时,与现有串行干扰消除方法相比,带来了约7 dB信噪比增益。