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。     

基于MIMO系统的球形译码算法的改进

在最大似然检测中,球形译码算法是一种有效的快速算法。提出一种基于MIMO系统的新的快速球形译码算法,它的复杂度比传统的算法要小的多。在提出的方法中,初始半径的选择并不重要。这种改进算法的译码性能和复杂度由两个参数来控制。因此,该方法存在着译码性能和复杂度的均衡。通过计算机仿真,可以看到,提出改进算法的译码性能得到了较大的提高。     

MIMO-OFDM中基于KSDA和SQRD的联合检测算法

球形译码算法的检测性能最接近最大似然检测算法,但其计算复杂度仍然较高。为了在计算复杂度和系统性能之间取得良好折中,在研究标准球形译码的基础上,提出一种新的球形译码改进算法。新算法由快速球形译码与基于MMSE准则的SQRD算法构成。该算法在高信噪比时采用SQRD算法,低信噪比时采用KSDA算法。仿真结果表明,该算法在降低球形译码算法复杂度的同时获得了较好的系统性能。     

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.     

面向大规模MIMO的分块空间调制技术研究

广义空间调制（Generalized Spatial Modulation,GSM）是一种基于多输入多输出（Multiple-Input Multiple-Output, MIMO）系统的高效的数字调制技术,在传统调制符号之外同时通过激活天线模式组合发送信息比特。针对大规模MIMO系统中发送天线数量过多导致的激活天线模式组合的数量过多的问题,提出了一种分块空间调制算法,将每个发送天线子块的激活模式组合构成完整的激活天线模式组合,可有效降低大规模MIMO系统中发送端的调制复杂度。在接收端使用球形译码方法实现逐个子块的天线激活模式组合的解调,可以大幅降低接收端的计算复杂度。仿真分析表明,提出的球形译码算法可以在大幅降低计算复杂度的情况下,实现接近最大似然（Maximum Likelihood,ML）接收机算法的误比特率（Bit Error Rate,BER）性能,且可实现接收端检测性能和计算复杂度之间的最佳折衷。     

多天线系统中逼近最大似然的快速检测算法

该文针对无编码的多输入多输出无线通信系统中的最大似然检测接收机在发端天线数较多、调制阶数较高时计算复杂度过高的问题,提出了一种低复杂度的球形译码算法。该算法首先利用信道信息对待检测的发送信号矢量进行分组,然后对各组内的信号矢量采用球形译码进行最大似然检测,并在组间做干扰消除。理论分析和仿真表明,该算法不仅复杂度低,而且能够逼近最大似然检测的性能。     

一种基于统计裁剪的球形译码算法

球形译码的半径对译码的复杂度有很大的影响。文章在卡方分布特性的基础上设计了一种基于统计裁剪的改进球形译码算法,该算法通过减去估算出来的未检测层的半径的大小,达到对当前检测层的半径进行缩减的目的;同时综合考虑了信道的影响,对均方误差较大的列进行ML检测,剩余信号进行树裁剪的SD算法。仿真表明,该算法大大降低了球形译码的算法复杂度。     

一种低复杂度的球形检测算法

文章研究了一种多输入多输出（MIMO）无线系统的球形检测算法,首先介绍了MIMO系统模型和传统的球形检测算法的原理,然后分析了改进算法的原理及其优点。仿真结果显示,和传统的初始半径确定方法相比,改进算法大大减少了访问节点数量,有效地降低了计算复杂度。     

基于修正扩展信道矩阵的球形解码算法

球形解码算法是多输入多输出(MIMO)系统一种性能较好的信号检测算法,只是其计算复杂度较高,为此,有学者提出运用最小均方误差(MMSE)和压缩系数控制的初始半径来降低复杂度,然而对于16QAM调制的情形,该算法虽能降低复杂度却也损失了部分误码率。针对这一问题,文章提出一种基于修正扩展信道矩阵的球形解码算法,仿真结果表明,改进的算法在16QAM调制时不仅可以改善信号的误码率,而且基本不增加算法的计算复杂度。     

PSK调制下空频块码的低复杂度复数球形译码

对于PSK调制下的空频块码,复数球形译码相对实数球形译码有较低的复杂度.当复数球形译码的初始半径趋向无穷大时,排序的复杂度高.本文针对PSK符号提出每层符号以排序中心点为中心,在极坐标角度维按照之字(Zigzag)排序的方法.通过查表可以快速获得排序后的符号序列,查表排序球形译码算法相对于通用复数球形译码算法在16-PSK调制14dB平均比特信噪比下节省约61%的复杂度.     

一种低复杂度的差分酉空时调制多符号球形译码算法

该文提出了一种瑞利衰落信道下差分酉空时调制系统中多符号差分球形译码的改进算法。该算法在执行球形译码的最大似然度量搜索时,仅对具有较小最大似然度量的部分测试符号进行搜索,从而大大减少了搜索的次数,同时提出了一种逐项进行的最大似然度量计算方法,可以尽早发现超过搜索范围的测试符号并终止计算,在避免无谓的运算负担的同时得到所需的具有较小最大似然度量的部分测试符号。仿真表明,在适中的信噪比范围内,该算法在牺牲少量系统性能的基础上降低了超过50%的运算量。     

On Multiple Symbol Detection for Diagonal DUSTM Over Ricean Channels

This letter considers multiple symbol differential detection for multiple-antenna systems over flat Ricean-fading channels when partial channel state information (CSI) is available at the transmitter. Using the maximum likelihood (ML) principle, and assuming perfect knowledge of the channel mean, we derive the optimal multiple symbol detection (MSD) rule for diagonal differential unitary space-time modulation (DUSTM). This rule is used to develop a sphere decoding bound intersection detector (SD-BID) with low complexity. A suboptimal MSD based decision feedback DD (DF-DD) algorithm is also derived. The simulation results show that our proposed MSD algorithms reduce the error floor of conventional differential detection and that the computational complexity of these new algorithms is reasonably low.     

Reduced Complexity K-Best Sphere Decoder Design for MIMO Systems

The sphere decoding algorithm is widely used for maximum likelihood (ML) detection in multiple-input multiple-output (MIMO) systems, wherein the K-best sphere decoding algorithm is well appreciated for its fixed complexity and throughput. However, to achieve near-ML performance, K needs to be sufficiently large, which leads to large computational complexity in path expansion, sorting, and path updating. In this paper, we propose two efficient decoding schemes that can significantly reduce the average K value and thus the total computational complexity without sacrificing performance. We further present a novel sorting architecture that can save about 70% of the sorting operations compared with the bubble sort method. It is estimated that, for the example 4×4 64 QAM MIMO system, 68% of the total computational complexity can be saved over the conventional design by applying all the proposed techniques. This work was supported by the National Science Foundation under Grant CCF-0514816 and the National Aeronautics and Space Administration under Grant NNX06AC23G.     

一种重叠Alamouti码的最大似然检测优化算法

针对准正交重叠Alamouti码(Overlapped Alamouti Code,OAC)采用最大似然(maximum likelihood,ML)接收机检测时译码复杂度高的缺点,提出了一种ML译码优化算法。利用等价信道矩阵具有部分正交性的特点,将等价信道矩阵拆分成2个相互正交的子等价信道矩阵,使得在检测每个符号分组时可分别检测2个较短分组,从而降低了ML接收机的译码复杂度。改进算法保持了与ML译码相近的误码率性能,而运算量大幅度降低,从而克服了ML译码复杂度高的缺点。     

一种低复杂度的空时分组码检测算法

空时分组码能够提供分集和编码增益,但所用的ML译码算法计算量太大,不能满足高速实时通信系统的要求。为了有效地降低译码复杂度,提出了一种新的检测算法,该算法通过对系统模型进行变换,利用等效的信道响应矩阵的QR分解来进行译码,从而避免了对所有的调制信号进行穷举搜索的过程,使计算复杂度有了很大降低,而且新算法的误码率性能与ML算法近乎相同。最后,通过仿真验证了所提算法的有效性和实用性。     

A simple low-complexity algorithm for generalized spatial modulation

Generalized spatial modulation (GSM) is an extension of spatial modulation which is significant for the next generation communication systems. Optimal detection process for the GSM is the maximum-likelihood (ML) detection which jointly detects the antenna combinations and transmitted symbols. However, the receiver is much more complicated than SM due to inter-antenna interference and/or increased number of combinations. Therefore, the computational complexity of the ML detection grows with the number of transmit antennas and the signal constellation size. In this letter, we introduce a novel and simple detection algorithm which uses sub-optimal method based on the least squares solution to detect likely antenna combinations. Once the antenna indices are detected, ML detection is utilized to identify the transmitted symbols. For obtaining near-ML performance while keeping lower complexity than ML detection, sphere decoding is applied. Our proposed algorithm reduces the search complexity while achieving a near optimum solution. Computer simulation results show that the proposed algorithm performs close to the optimal (ML) detection resulting in a significant reduction of computational complexity.     

On maximum-likelihood detection and the search for the closest lattice point

Maximum-likelihood (ML) decoding algorithms for Gaussian multiple-input multiple-output (MIMO) linear channels are considered. Linearity over the field of real numbers facilitates the design of ML decoders using number-theoretic tools for searching the closest lattice point. These decoders are collectively referred to as sphere decoders in the literature. In this paper, a fresh look at this class of decoding algorithms is taken. In particular, two novel algorithms are developed. The first algorithm is inspired by the Pohst enumeration strategy and is shown to offer a significant reduction in complexity compared to the Viterbo-Boutros sphere decoder. The connection between the proposed algorithm and the stack sequential decoding algorithm is then established. This connection is utilized to construct the second algorithm which can also be viewed as an application of the Schnorr-Euchner strategy to ML decoding. Aided with a detailed study of preprocessing algorithms, a variant of the second algorithm is developed and shown to offer significant reductions in the computational complexity compared to all previously proposed sphere decoders with a near-ML detection performance. This claim is supported by intuitive arguments and simulation results in many relevant scenarios.     

Speeding up the Sphere Decoder With H∞ and SDP Inspired Lower Bounds

It is well known that maximum-likelihood (ML) decoding in many digital communication schemes reduces to solving an integer least-squares problem, which is NP hard in the worst-case. On the other hand, it has recently been shown that, over a wide range of dimensions N and signal-to-noise ratios (SNRs), the sphere decoding algorithm can be used to find the exact ML solution with an expected complexity that is often less than N³. However, the computational complexity of sphere decoding becomes prohibitive if the SNR is too low and/or if the dimension of the problem is too large. In this paper, we target these two regimes and attempt to find faster algorithms by pruning the search tree beyond what is done in the standard sphere decoding algorithm. The search tree is pruned by computing lower bounds on the optimal value of the objective function as the algorithm proceeds to descend down the search tree. We observe a tradeoff between the computational complexity required to compute a lower bound and the size of the pruned tree: the more effort we spend in computing a tight lower bound, the more branches that can be eliminated in the tree. Using ideas from semidefinite program (SDP)-duality theory and H^infin estimation theory, we propose general frameworks for computing lower bounds on integer least-squares problems. We propose two families of algorithms, one that is appropriate for large problem dimensions and binary modulation, and the other that is appropriate for moderate-size dimensions yet high-order constellations. We then show how in each case these bounds can be efficiently incorporated in the sphere decoding algorithm, often resulting in significant improvement of the expected complexity of solving the ML decoding problem, while maintaining the exact ML-performance.     

基于节点子集和k阶信息截断的多元LDPC译码算法

本文提出一种低复杂度的多元LDPC译码算法,从以下两个方面降低复杂度：（1）提出一种节点截断策略,构造处理/非处理校验节点子集,使得一部分足够可靠的校验节点不进行信息更新,从而减少运算量;（2）针对处理校验节点子集,本文进而提出一种k阶信息截断准则,对Trellis图上的边和状态进行划分.在迭代递归过程中,只有"活"的状态/边才参与运算,可进一步降低校验节点的计算量.仿真表明,本文算法的译码性能与现有的几种EMS改进算法非常接近;在译码复杂度方面,不管对高阶域还是低阶域的多元LDPC码,本文算法的复杂度都是最小的.