采用压缩感知的贝叶斯信道估计算法

Compressed sensing-based Bayesian channel estimation algorithm

高阶多输入多输出系统能有效提高能量效率和传输可靠性,但由于天线数量巨大,信道参数估计任务艰巨.虽然支持不可知的贝叶斯匹配追踪算法估计准确,但复杂度过高.为了解决这个问题,提出了一种期望修剪匹配追踪算法.在信道每一个稀疏度下,把与当前残差信号内积较大原子(测量矩阵列矢量)的所在位置添加到支撑集中,组成扩大支撑集;然后对扩大支撑集进行筛选,剔除可能选错的位置,并确定最佳支撑集;计算各个稀疏度最佳支撑集对应信道的估计值和相对发生概率,由此计算信道的数学期望,并作为最终的信道估计值.仿真结果表明,文中算法与支持不可知的贝叶斯匹配追踪算法相比,具有更低复杂度的期望修剪匹配追踪算法能保证信道估计精度和系统误比特率性能.

The high-order multiple-input multiple-output system can improve the energy efficiency and transmission reliability.However,it is difficult to perform channel estimation because of the large number of antennas.Although the SABMP(Support Agnostic Bayesian Matching Pursuit)algorithm can estimate the channel accurately,the complexity is too high.To address this issue,an EPMP(Expectation Prune Matching Pursuit)algorithm is proposed in the paper.At each sparsity level of the channel,an expanded support set is given by adding some positions corresponding to the atoms that have a larger inner product value with the current residual signal.Then the best support set is obtained by removing the wrong positions in the expanded support set.The estimated channel and the relative probability of the best support set at each sparse level arc calculated.Finally,the expectation of the channel is calculated and regarded as the estimation of the channel.Compared with the S八BMP algorithm,the KPMP algorithm can reduce the computational complexity while maintaining the estimation accuracy.The effectiveness of the KPMP algorithm is validated by simulation results.