The pilot overhead provides fundamental limits on the performance of massive multiple-input multiple-output (MIMO) systems. This is because the performance of such systems is based on the failure of the presentation of accurate channel state information (CSI). Based on the theory of compressive sensing, this paper presents a novel channel estimation technique as the mean of minimizing the problems associated with pilot overhead. The proposed technique is based on the combination of the compressive sampling matching and sparsity adaptive matching pursuit techniques. The sources of the signals in MIMO systems are sparsely distributed in terms of spatial correlations. This distribution pattern enables then use of compressive sampling techniques to solve the channel estimation problem in MIMO systems. Simulation results demonstrate that the proposed channel estimation outperforms the conventional compressive sensing (CS)-based channel estimation algorithms in terms of the normalized mean square error (NMSE) performance at high signal-to-noise ratios (SNRs). Furthermore, it reduces the computational complexity of the channel estimation compared to conventional methods. In addition to the achieved performance gain in terms of NMSE, the presented method significantly reduces pilot overhead compared to conventional channel estimation techniques.

导频开销对大规模多输入多输出（MIMO）系统的性能提供了基本限制。这是因为此类系统的性能基于准确的信道状态信息（CSI）表示失败。基于压缩感测理论，本文提出了一种新颖的信道估计技术，作为将与导频开销相关的问题最小化的手段。所提出的技术是基于压缩采样匹配和稀疏自适应匹配追踪技术的结合。就空间相关性而言，MIMO系统中的信号源稀疏分布。然后，这种分布模式可以使用压缩采样技术来解决MIMO系统中的信道估计问题。仿真结果表明，所提出的信道估计在高信噪比（SNR）下的归一化均方误差（NMSE）性能方面优于常规的基于压缩感测（CS）的信道估计算法。此外，与传统方法相比，它降低了信道估计的计算复杂度。除了在NMSE方面实现的性能提升外，与传统的信道估计技术相比，该方法还大大减少了导频开销。与传统方法相比，它降低了信道估计的计算复杂度。除了在NMSE方面实现的性能提升外，与传统的信道估计技术相比，该方法还大大减少了导频开销。与传统方法相比，它降低了信道估计的计算复杂度。除了在NMSE方面实现的性能提升外，与传统的信道估计技术相比，该方法还大大减少了导频开销。