空间相关信道下空时分组码的子空间修正解码

Revised Subspace-Based Decoding of Space-Time Block Codes over Spatially Correlated Channels

针对空时分组码子空间盲解码算法在空间相关信道下性能下降的问题,提出了一种修正解码算法.在分析空间相关性破坏信号子空间和统计噪声子空间正交性的基础上,利用接收端已知的信道相关矩阵,修正接收端用于搜索的码字矩阵集合,使相关信道下分解得到的统计噪声子空间正交于修正的发送信号子空间,提高了解码性能.与传统解码的算法不同,该算法无需在发送端进行信道相关矩阵的估计和预编码,降低了发送端的编码复杂度.Monte-Carlo仿真实验表明,空间强相关信道下空时分组码子空间盲解码性能下降比较剧烈,采用新算法修正后的解码性能明显改善,在误比特率为10-2、相关系数为0.8时,修正性能提高约2 dB.

Due to the performance degradation of subspace-based blind decoding of space-time block codes （STBC） over spatially correlated channels, a revised decoding algorithm was proposed. By analyzing that the channel correlation destroys the orthogonality between the signal subspace and the statistical noise subspace, the code matrix set used for searching was revised by the new algorithm with the known correlation matrix in the receiver. The decoding performance was improved because the revised subspace of sending signal would be orthogonal to the statistical noise subspace obtained from decomposing the correlated channel. Compared to the traditional decoding algorithms, the proposed scheme is no need to estimate the correlation matrix of the channel and pre-code at the transmitter, and its coding complexity is reduced. Monte-Carlo simulations demonstrate that subspace-based blind decoding of STBC degrades severely in the high correlated channels,while the revised decoding performance is increased by about 2 dB when BER is 10^-2 and correlation coefficient is 0. 8.