MIMO协同中继系统中的最大似然合并解调算法

协同中继系统通过合并解调不同路径的信号副本，得到比非协同系统更优的误码性能。传统的合并解调算法将合并解调过程分开处理，性能较差。该文针对多输入多输出（MIMO）放大转发协同中继系统，基于最大似然（ML）准则，提出了在目的节点对来自源节点和中继节点的信号进行合并解调的新算法。该算法首先对来自源节点和中继节点的信号进行ML合并，然后采用传统的MIMO最大似然检测完成信号的解调。分析与仿真结果表明，与最大比合并（MRC）等算法相比，在不同调制方式和信道条件下，所提算法均获得了显著的性能增益，且高阶调制下的复杂度低。 

Combining Demodulation Algorithm Based on Maximum Likelihood Theory for MIMO Cooperative Relay System

By combining and demodulating signal copies from different paths, cooperative relay system achieves better bit error performance than non-cooperative system. Traditional schemes combine and demodulate signals separately. However it may lose some useful information in the separate processing procedures. So the system performance is bad. To overcome this problem, this paper presents a novel combining demodulation algorithm for multi-input multi-output (MIMO) amplify-and-forward (AF) cooperative relay system. The proposed algorithm is based on maximum likelihood (ML) rule. The process is, first, signals from source and cooperative relay are combined into traditional MIMO detected form, then ML detection is used to demodulate the combined signal. The analysis and simulation results demonstrate that compared with present combing demodulation algorithms, such as maximum ratio combing (MRC) demodulation algorithm, the ML combining demodulation algorithm has a significant performance gain for different modulations and channel conditions. The system complexity with this algorithm is lower than that with present algorithms for high-order modulation.