基于贪心策略的大规模MIMO系统信号检测算法

针对传统球形译码性能和计算复杂度受到初始半径及搜索策略制约的问题,提出了一种新的基于M算法的贪心策略球形译码检测算法,对树搜索的方法进行了改进,先将该层信号集合中的距离增量进行排序,然后选择距离增量最小的M个点为信号点,这样每一次选取的信号点相对该层都是局部最优的.仿真结果表明,相比于传统球形译码检测算法,当M为1时,该算法可以降低约30％的计算复杂度.使球形译码算法的效率得到了很大的提高,可以运用于大规模MIMO系统中.     

降低高条件数信道下的球形译码算法复杂度的方法

MIMO系统中,球形译码可以在保证接近ML检测性能的前提下大大降低检测复杂度。但当信道矩阵条件数很高时,球形译码的复杂度仍然会很高。在分析了这一现象的原因后,本文提出了在高层对权值进行合并的一种球形译码算法,因为其减小了译码搜索过程中对树的高层节点的访问的概率,由此降低了搜索复杂度。仿真结果表明,这种算法在低信噪比、高条件数时可以节约20％的浮点运算操作次数。     

MIMO系统中球形译码算法的研究

针对多输入多输出（MIMO）通信系统中球形译码检测算法在较低信噪比和较高的调制阶数时复杂度仍然很高的问题,提出一种不影响检测性能同时又能大大降低其复杂度的方案。首先,对传统的球形译码算法进行研究;其次,介绍改进的球形译码算法;最后,通过仿真结果对其进行验证。     

编码MIMO系统中一种低复杂度次优软检测算法

提出了一种低复杂度次优编码MIMO系统软检测算法.在编码MIMO系统的迭代译码中,信道译码需要MIMO检测输出每一比特的软信息,而软信息的计算需要巨大的计算量.本文在不同的迭代次数中分别利用球形译码的硬判决信息和编码比特先验信息得到发射向量的估计值.在这个估计值的基础上计算MIMO检测中每一比特的软信息,从而避免了常规的穷尽搜索检测算法,减少了复杂度.通过分析和仿真,本算法在有限性能损失的前提下使复杂度得到了极大减少.在相同没置下,本算法的一帧数据仿真时间不到原算法的1/20,并且对于不同的调制方式复杂度基本不变,达到了性能和复杂度的较好折中.     

LTE-A系统一种降低复杂度的软球形检测算法

软球形译码算法虽然能接近ML(最大似然)算法的误码性能,但其计算复杂度很高。文章提出了一种降低计算复杂度的SSD(软球形译码)算法,该算法在QR(正交三角)分解算法的反向迭代上三角矩阵R中引入减弱噪声部分以缩小初始搜索半径,然后利用最小距离准则对搜索树进行有效地删减,缩小树搜索空间。MATLAB仿真结果表明,该算法在获得接近传统SSD性能的条件下,能够很大程度地降低系统的计算复杂度。     

一种低复杂度的MIMO预处理球形译码算法

提出了一种新的低复杂度MIMO预处理球形译码算法.球形译码算法是MIMO译码方案达到最大似然性能的低复杂度译码算法,但当信噪比较低或调制阶数较高时,该算法的复杂度仍然很大,甚至接近穷尽搜索方法的复杂度.本文利用迫零译码的初始估值作为软信息,对信道矩阵各列进行重排列,从而改变解向量中各维信号的求解顺序,因此大大减少了低信噪比或较高调制阶数的球形译码计算复杂度.仿真结果表明,当Eb/No为0～3时,16QAM调制方式下的V-BLAST译码复杂度减少了近1/3.     

低复杂度MIMO检测技术分析

为了克服多径传播对信号传输的影响,提出了多入多出(MIMO)检测的3种低复杂度算法,给出了各种算法的具体原理,特别对基于Chase原理的检测算法进行了介绍,论述了相对于最大似然算法可减低复杂的程度,研究了各种算法的误码率性能及复杂度。给出了2发2收MIMO系统和3发3收MIMO系统的仿真结果。结果表明,球形译码算法和基于Chase原理的MIMO检测算法能以比较低的复杂度,获得逼近最大似然检测算法的误码率性能。研究的检测算法为MIMO无线通信接收机的硬件实现提供了理论基础。     

一种低复杂度软入软出迭代检测方案

在MIMO系统中,以软输入软输出方式为基础的迭代检测方式,性能要明显优于基于硬判决的迭代方案。因此基于软信息传递的迭代检测理论具有广泛的应用。但是,其复杂度较高,特别是应用于MIMO系统时,复杂度的限制尤为明显。球形译码是一种近似最大似然检测的低复杂度方案,在这里,引入一种低复杂度的软入软出（SISO）球形译码迭代方案,并给出球形译码中搜索树的删减简化方案。     

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。     

基于切比雪夫-迹迭代的大规模MIMO系统软输出信号检测

在多用户大规模多输入多输出(MIMO)系统信号检测算法中,最小均方误差(MMSE)算法可取得近似最优性能,但MMSE算法中高维矩阵求逆的复杂度过高,导致在实际应用中难以快速有效地实现.同时,对于高阶正交幅度调制(HQAM),如果符号向比特的解映射采用硬判决,将会导致后续信道译码的性能明显下降.因此,该文针对采用格雷编码的HQAM的多用户大规模MIMO系统,提出一种基于切比雪夫-迹迭代(CTI)的低复杂度软输出信号检测算法.该算法不但有效地规避了信号检测所需的高维矩阵求逆,同时,利用格雷编码的调制信号的比特翻转特性和二叉树结构,给出了一种融合三叉链表搜索的比特对数似然比(LLR)简化计算方法.仿真结果表明,该文所提的软输出信号检测算法最多需要3次迭代就能收敛并可取得接近MMSE算法的性能,在复杂度和性能之间取得了很好的折中.     

An application of univariate marginal distribution algorithm in MIMO communication systems

The paper discusses a sequence detector based on univariate marginal distribution algorithm (UMDA) that jointly estimates the symbols transmitted in a multiple input multiple output (MIMO) communication system. While an optimal maximum likelihood detection using an exhaustive search method is prohibitively complex, it has been shown that sphere decoder (SD) achieves the optimal bit error rate (BER) performance with polynomial time complexity for smaller array sizes. However, the worst‐case complexity of SD is exponential in the problem dimensions, this brings in question its practical implementation for larger number of spatial layers and for higher‐order signal constellation. The proposed detector shows promising results for this overly difficult and complicated operating environment, confirmed through simulation results. A performance comparison of the UMDA detector with SD is presented for higher‐order complex MIMO architectures with limited average transmit power. The proposed detector achieves substantial performance gain for higher‐order systems attaining a near optimal BER performance with reduced computational complexity as compared with SD. Copyright © 2009 John Wiley & Sons, Ltd.     

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.

     

大规模MIMO下具有反馈的低复杂度检测算法

针对大规模MIMO系统,信号检测算法的复杂度往往呈指数级增长的问题,通过对传统的QRD-M检测算法和树搜索的深度研究,对其进行改进,得出一种具有反馈的低复杂度检测算法,该算法主要运用分支的权重和分支间的反馈对树进行修剪,然后运用树搜索的原理进行搜索输出.仿真结果表明,该改进算法以降低较少系统性能的代价,能够极大地降低计算复杂度,是一种折衷的高效检测算法,完全能运用于大规模MIMO系统中.     

Relaxed K -Best MIMO Signal Detector Design and VLSI Implementation

Signal detector is a key element in a multiple-input multiple-output (MIMO) wireless communication receiver. It has been well demonstrated that nonlinear tree search MIMO detectors can achieve near-optimum detection performance, nevertheless their efficient high-speed VLSI implementations are not trivial. For example, the hardware design of hard- or soft- output detectors for a 4 times 4 MIMO system with 64 quadrature amplitude modulation (QAM) still remains missing in the open literature. As an attempt to tackle this challenge, this paper presents an implementation-oriented breadth-first tree search MIMO detector design solution. The key is to appropriately modify the conventional breadth-first tree search detection algorithm in order to largely improve the suitability for efficient hardware implementation, while maintaining good detection performance. To demonstrate the effectiveness of the proposed design solution, using 0.13-mum CMOS standard cell and memory libraries, we designed a soft-output signal detector for 4 times 4 MIMO with 64-QAM. With the silicon area of about 31 mm², the detector can achieve above 100 Mb/s and realize the performance very close to that of the sphere decoding algorithm     

Tree pruning for MIMO sphere detection based on MMSE detection

Tree pruning is an effective algorithm to reduce the complexity of sphere detection(SD) for multiple-input multiple-output(MIMO) communication systems.How to determine the tree pruning rule,as well as by what the tradeoff between the performance and the complexity can be achieved,is still an open problem.In this paper,a tree pruning algorithm is proposed based on minimum mean square error(MMSE) detection.The proposed algorithm first preforms MMSE detection since the complexity of MMSE detection is very low.Then the pruning constraints will be set according to the scaled path metrics of the MMSE solution.The choice of the scale factors and their influences on the complexity and performance are also discussed.Through analysis and simulations,it is shown that the complexity is reduced significantly with negligible performance degradation and additional computations.     

A simplified QRD-<Emphasis Type="Italic">M</Emphasis> signal detection algorithm for MIMO-OFDM systems

QR Decompositon with an M-algorithm (QRD-M) has good performance with low complexity, which is considered as a promising technique in Multiple-Input Multiple-Output (MIMO) detections. This paper presented a simplified QRD-M algorithm for MIMO Orthogonal Frequency Division Multiplexing (MIMO-OFDM) systems. In the proposed scheme, each surviving path is expanded only to partial branches in order to carry out a limited tree search. The nodes are expanded on demand and sorted in a distributed manner, based on the novel expansion scheme which can pre-determine the children’s ascending order by their local distances. Consequently, the proposed scheme can significantly decrease the complexity compared with conventional QRD-M algorithm. Hence, it is especially attractive to VLSI implementation of the high-throughput MIMO-OFDM systems. Simulation results prove that the proposed scheme can achieve a performance very close to the conventional QRD-M algorithm.     

Zero forcing based sphere decoder for generalized spatial modulation systems

To reduce the number of radio frequency (RF) chains in multiple input multiple output (MIMO) systems, generalized spatial modulation (GSM) techniques have been proposed in the literature. In this paper, we propose a zero‐forcing (ZF)‐based detector, which performs an initial pruning of the search tree that will be considered as the initial condition in a sphere decoding (SD) algorithm. The proposed method significantly reduces the computational complexity of GSM systems while achieving a near maximum likelihood (ML) performance. We analyze the performance of the proposed method and provide an analytic performance difference between the proposed method and the ML detector. Simulation results show that the performance of the proposed method is very close to that of the ML detector, while achieving a significant computational complexity reduction in comparison with the conventional SD method, in terms of the number of visited nodes. We also present some simulations to assess the accuracy of our theoretical results.     

大规模MIMO系统低复杂度混合迭代信号检测

在大规模MIMO系统上行链路信号检测算法中,最小均方误差(MMSE)算法能获得接近最优的线性检测性能.但是,传统的MMSE检测算法涉及高维矩阵求逆运算,由于复杂度过高而使其在实际应用中难以快速有效地实现.基于最速下降(steepest descent,SD)算法和高斯一赛德尔(Gauss-Seidel,GS)迭代的方法提出了一种低复杂度的混合迭代算法,利用SD算法为复杂度相对较低的GS迭代算法提供有效的搜索方向,以加快算法收敛的速度.同时,给出了一种用于信道译码的比特似然比(LLR)近似计算方法.仿真结果表明,通过几次迭代,给出的算法能够快速收敛并接近MMSE检测性能,并将算法复杂度降低一个数量级,保持在O(K2).     

Low complexity MIMO scheduling with channel decomposition using capacity upperbound

In multiuser MIMO systems, the base station schedules transmissions to a group of users simultaneously. Since the data transmitted to each user are different, in order to avoid the inter-user interference, a transmit preprocessing technique which decomposes the multiuser MIMO downlink channel into multiple parallel independent single-user MIMO channels can be used. When the number of users is larger than the maximum that the system can support simultaneously, the base station selects a subset of users who have the best instantaneous channel quality to maximize the system throughput. Since the exhaustive search for the optimal user set is computationally prohibitive, a low complexity scheduling algorithm which aims to maximize the capacity upper bound is proposed. Simulation results show that the proposed scheduling algorithm achieves comparable total throughput as the optimal algorithm with much lower complexity.