MIMO-OFDM系统中一种局域化最大似然检测算法

本文提出了MIMO-OFDM系统中一种低复杂度的局域化的最大似然检测算法,该方法通过对每个发送天线的导频训练序列进行信道估计,调整信号有效搜索域,获得低复杂度的系统误码率。实验表明,该信号检测算法明显降低了高阶QAM调制的计算复杂度,在QPSK和16QAM调制时,系统误码率接近已知信道频响情况。     

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。     

一种低复杂度的CPM信号最大似然块检测算法

为了降低连续相位调制(Continuous Phase Modulation,CPM)信号多符号非相干检测的运算复杂度,提出了一种低复杂度的最大似然块检测算法。该算法充分利用已判决输出的符号对检测过程中的符号向量取值进行约束,有效减少判决统计量计算时的运算量,进而降低算法复杂度。另外,该算法引入判决长度变量,通过调整单次检测时判决符号数使算法能够在检测性能与运算量之间灵活折中。仿真结果表明,提出的低复杂度检测算法能够适用于全响应和部分响应CPM信号,相比原最大似然块检测算法能够在不损失检测性能的前提下降低算法运算量至少50〖WT《Times New Roman》〗%〖WTBZ〗,并且能够通过选择不同的判决长度提高算法应用的灵活性。     

广义空间调制系统的低复杂度检测算法

针对广义空间调制( GSM)系统接收端最大似然( ML)检测算法计算复杂度极高的缺点,提出了一种基于压缩感知( CS)信号重构理论的低复杂度信号检测算法。首先,在多输入多输出( MI-MO)信道模型下,通过改进正交匹配追踪( OMP)算法,得到一个激活天线索引备选集;然后,利用ML算法在该备选集中进行遍历搜索,检测出激活天线索引和星座调制符号。仿真结果表明所提算法的检测性能接近于ML算法,且复杂度约为ML算法的2%。因此,所提算法在保证检测性能的同时也大大降低了计算复杂度,实现了检测性能与复杂度之间的平衡。     

高阶调制的GLST系统中的检测算法

多天线无线通信系统的下行链路常采用GLST (Group Layered Space-Time architecture) 来增加接收分集度。该文基于GLST系统的等效多输入多输出模型,分析了该系统在符号传输上的正交性,并针对该系统在使用高阶调制时,传统的符号检测算法因计算复杂度太高而不可用的问题,提出了两种基于球形译码的低复杂度检测算法。仿真结果表明,两种算法均能获得逼近最大似然检测器的性能。     

基于检测残差消除的CP-OTFS系统信号检测算法

正交时频空（OTFS）调制技术因其高抗频率色散的性能，有望成为6G中高移动性场景下通信的关键技术。通过在OTFS系统发送端加入循环前缀（CP），可克服信道多径效应导致的码间干扰。针对当前CP-OTFS系统信号检测算法计算复杂度高的问题，提出一种基于检测残差消除的信号检测算法。采用时频域低复杂度初始检测后，结合系统时延-时间域的输入-输出关系计算初始检测残差。以检测残差为迭代变量，通过计算反馈值更新检测信号，并在时延-多普勒域进行逐符号最大似然检测。在迭代计算过程中逐步消除检测残差，并给出了算法停止准则。实验结果表明，该算法在信噪比为15 dB时误码性能比线性最小均方差算法提升了2.79 dB，比消息传递算法提升了1.76 dB，并且具有更好的收敛性和更低的计算复杂度。     

基于K均值聚类的SPPM分步分类检测算法

针对空间脉冲位置调制系统中采用最大似然检测算法时存在计算复杂度高的问题,依据空间脉冲位置调制信号矩阵的特点,提出了一种基于K均值聚类的分步分类检测算法.首先,采用基于信号向量检测算法完成训练样本中光源索引号的检测,利用K均值聚类算法对训练样本进行离线训练得到其质心与调制符号间的映射关系.然后,以该映射关系为准则完成在线...     

大规模MIMO系统基于分布式压缩感知的信号检测算法研究

随着通信技术的不断发展,人们对通信速率的要求越来越高,大规模MIMO(Multiple-Input Multiple-Output)技术因其能够大大提高系统的频谱效率,成为通信技术领域的研究热点.在大规模空间调制MIMO中,原本最佳的检测方法——最大似然(Maximum likelihood,ML)检测算法由于算法复杂度过高,不再适用.而适用于小规模空间调制MIMO系统的低复杂度的检测算法在大规模系统中性能会很差.本文利用空间调制信号的结构化稀疏性,提出了基于分布式压缩感知(Distributed Compressed Sensing,DCS)的信号检测算法,同时参照已有文献,利用分组传输和信号交织来进一步提高信号检测性能.最后我们通过仿真验证了此方案能够较好地逼近最大似然检测算法性能.     

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.     

Multi-h CPM信号的减少状态多符号差分非相干检测

针对多指数连续相位调制(Multi-h CPM)载波恢复困难且实现复杂度高的难题,提出了一种适用于Multi-h CPM信号的减少状态多符号差分非相干检测算法。该算法利用多符号差分信息进行分支度量计算的同时引入参考相位信号,实现了具有残留载波频偏校正的非相干序列检测,并且结合逐幸存处理技术,利用幸存路径上的假设判决信息减少了网格状态数。通过计算和搜索接收信号所有可能调制指数序列所对应的分支度量信息实现了调制指数的估计与同步。计算机仿真结果表明,与相干最大似然序列检测相比,该算法能够以较小的性能损失获得其实现复杂度的降低,而且当存在残留载波频偏时,该算法的检测性能也较为稳健。     

An efficient approximate maximum likelihood signal detection for MIMO systems

This paper proposes an efficient approximate Maximum Likelihood （ML） detection method for Multiple-Input Multiple-Output （MIMO） systems, which searches local area instead of exhaustive search and Selects valid search points in each transmit antenna signal constellation instead of all hyperplane. Both of the selection and search complexity can be reduced significantly. The method performs the tradeoff between computational complexity and system performance by adjusting the neighborhood size to select the valid search points. Simulation results show that the performance is comparable to that of the ML detection while the complexity is only as the small fraction of ML.     

Hybrid SNR-Adaptive Multiuser Detectors for SDMA-OFDM Systems

Multiuser detection (MUD) and channel estimation techniques in space-division multiple-access aided orthogonal frequency-division multiplexing systems recently has received intensive interest in receiver design technologies. The maximum likelihood (ML) MUD that provides optimal performance has the cost of a dramatically increased computational complexity. The minimum mean-squared error (MMSE) MUD exhibits poor performance, although it achieves lower computational complexity. With almost the same complexity, an MMSE with successive interference cancellation (SIC) scheme achieves a better bit error rate performance than a linear MMSE multiuser detector. In this paper, hybrid ML-MMSE with SIC adaptive multiuser detection based on the joint channel estimation method is suggested for signal detection. The simulation results show that the proposed method achieves good performance close to the optimal ML performance at low SNR values and a low computational complexity at high SNR values.     

PDM-16QAM系统中几种相位估计算法的性能与复杂度比较以及改进

在偏分复用的高速相干光通信系统中,16进制正交幅度调制(PDM-16QAM)是最有前景的调制格式,且载波相位估计是补偿发射端与接收端激光器对信号造成的相位失真的重要技术。该领域最经常使用的算法为QPSK分区算法与盲相位搜索算法,但他们都有自己的缺点,如估计效果不够好,适用范围不够广,或者计算复杂度过高。本论文实验搭建了224Gb/s PDM-16QAM波分复用传输640km相干检测系统平台,首先利用几种基于前述两种算法的相位估计方法对接收到的信号进行了数据处理,分别给出了误码率曲线图、误差向量幅度曲线图、计算复杂度分析表。然后基于性能最优与计算复杂度的平衡,提出了针对16QAM信号星座图中间圈的点的省略两级处理算法。将该改进的算法用于实验系统相位估计单元进行数据处理,结果表明,改进的算法P1,3/ML其误码率性能和误差向量幅度性能基本保持不变,但是计算复杂度降低为原来的1/2。     

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.     

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.     

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.     

An Efficient DFE & ML Suboptimum Receiver for Data Transmission Over Dispersive Channels Using Two-Dimensional Signal Constellations

The DFE & ML receiver is presented for data transmission systems using QAM modulation. A substantial improvement in performance results as compared to other one- or two-stage receiver structures at relatively low computational complexity.     

一种低复杂度空间调制球形译码检测算法

现有的空间调制系统球形译码（Sphere-Decoding,SD）检测算法虽然能够较大地降低最大似然（Maximum-Likelihood,ML）检测算法的计算复杂度,但由于其更新半径比较松散、收敛较慢,计算复杂度降低的水平仍十分有限,尤其是在高阶调制系统下.针对上述问题,采用统计分布的思想对现有算法更新半径中的冗余项进行估计,提出了两种改进的球形译码检测算法.理论分析与仿真结果表明,改进算法在达到最优检测性能的同时,极大地降低了传统球形译码的计算复杂度,具有较好的理论和实际应用意义.     

一种基于准正交空时码的低复杂度MIMO差分检测方法

针对可实现全速率传输的准正交空时码,提出了一种低复杂度的准正交MIMO差分检测方法.该方法在发射端对数据比特进行联合星座映射,构造准正交空时码进行差分编码;接收端采用最大似然准则对两组星座符号对(symbol pair)并行差分检测.本文提出的星座集合及联合星座映射方法简化了接收端检测算法,降低了检测计算复杂度.     

双向中继信道中物理层网络编码的检测

对双向中继信道中物理层网络编码的检测进行了研究,最大似然检测性能好但是实现复杂度高。因此,在信源节点未知信道状态信息情况下,提出了2种检测方案:基于似然比函数的似然比检测和基于最大后验概率准则的最大后验概率检测。同时,针对信源节点已知信道状态信息的特殊情形,进行了同样的推导。分析和仿真结果表明,相比于分别检测出2个信源信息的最大似然检测,似然比检测的BER性能更优,但似然比检测需要知道额外的噪声方差信息,最大后验概率检测与最大似然检测等价,而且最大后验概率检测在实现复杂度上相对较低。