基于压缩感知的MIMO-OFDM系统自适应信道估计

Compressed Sensing Based Adaptive Channel Estimation of MIMO-OFDM System

针对超密集组网中导频复用将产生导频干扰,严重影响移动用户下行链路信道估计准确性的问题,提出了一种使用短导频的幂函数稀疏度自适应匹配追踪(Power Sparsity Adaptive Matching Pursuit,PSAMP)算法。该算法由稀疏度预估计和追踪重构两部分构成。首先通过幂函数试探得到一个略小于真实稀疏度的预估值,再通过压缩采样匹配追踪重构信号,改善估计结果;若不能成功重构,则逐渐增加信号原子数量。仿真结果表明,相较于传统自适应压缩感知重建算法,所提的PSAMP算法在高信噪比区域具有更好的信道估计性能。

The accuracy of downlink channel estimation in cellular systems can be severely affected by pilot signal interference resulted from pilot reuse in ultra-dense network(UDN).In light of this,a power function sparsity adaptive matching pursuit(PSAMP)algorithm for short signals composed of sparsity pre-estimation and pursuit reconstruction is proposed.At first,an estimated value slightly smaller than the real sparsity is obtained through a trial power function.On this basis,signals are reconstructed through compressive sampling matching pursuit to improve the estimated result.The number of signal atoms will be increased gradually if the reconstruction fails.The simulation results show that the proposed PSAMP algorithm presents more satisfactory channel estimation performance in the area of high signal-to-noise ratio than the traditional adaptive compressive sensing reconstruction algorithm.