Due to the high complexity of the pairwise decoding algorithm and the poor performance of zero forcing（ ZF） /minimum mean square error（ MMSE） decoding algorithm, two low-complexity suboptimal decoding algorithms, called pairwisequasi-ZF and pairwise-quasi-MMSE decoders, are proposed. First,two transmit signals are detected by the quasi-ZF or the quasiMMSE algorithm at the receiver. Then, the two detected signals as the decoding results are substituted into the two pairwise decoding algorithm expressions to detect the other two transmit signals. The bit error rate（ BER） performance of the proposed algorithms is compared with that of the current known decoding algorithms.Also, the number of calculations of ZF, MMSE, quasi-ZF and quasi-MMSE algorithms is compared with each other. Simulation results showthat the BER performance of the proposed algorithms is substantially improved in comparison to the quasi-ZF and quasiMMSE algorithms. The BER performance of the pairwise-quasiZF（ pairwise-quasi-MMSE） decoder is equivalent to the pairwiseZF（ pairwise-MMSE） decoder, while the computational complexity is significantly reduced.
为了提高大规模MIMO系统的分集增益、降低译码复杂度,构建了一种码率为1的满分集贝尔实验室垂直分层空时码,并采用最大比合并算法(MRC)检测接收信号.分别计算了MRC算法的平均输出信干噪比(SINR)和传统迫零算法(ZF)的平均信噪比(SNR),分析了性能相等时应满足的条件,并且比较了2种算法的计算复杂度和BER性能.结果表明,当BER=10-5,收发天线数为400和40、调制方式分别为BPSK和QPSK时,最大比合并算法的BER性能较迫零算法分别存在0.4和0.3 d B的增益.采用所提算法对接收信号进行检测,不但能够降低系统的计算复杂度,而且能保证系统的误比特率性能.
基于协作AF(Amplify and Forward)通信模型,提出了一种基于分布式空时分组码的自适应能量分配方案。该方案首先以最小化中断概率为准则,在源节点与中继节点之间根据信道状态信息情况,决定是否放大转发中继节点接收信号。然后,在中继节点采用最优功率分配策略,实现整个系统的最优化传输。仿真显示了所提出的两种自适应方案与传统自适应方案、非自适应方案,以及在不同调制制度下的性能比较。仿真结果表明,与传统非自适应方案相比,所提方案1的误码率性能提高了约5 d B。
