宽带MmWave大规模MIMO信道估计的AMP-SBL展开算法
AMP-SBL Unfolding for Wideband MmWave Massive MIMO Channel Estimation
摘要：
在宽带毫米波（mmWave）大规模多输入多输出（MIMO）系统中，由于混合模数架构，信道估计是一个具有挑战性的问题，它压缩了接收到的导频信号，使信道估计成为一个压缩感知（CS）问题。然而，现有的高性能CS算法通常遭受高复杂度。另一方面，大带宽和大量天线所引起的波束偏斜效应会使估计性能恶化。本文首次采用频率相关的角度字典来补偿光束斜视角。然后，从两个方面对基于期望最大化（EM）的稀疏贝叶斯学习（SBL）算法进行了改进，即在每一次迭代中，E步由近似消息传递（AMP）实现以降低复杂度，而M步由深度神经网络（DNN）实现以提高性能。仿真结果表明，基于AMP-SBL展开的信道估计器具有较低的复杂度和较好的性能。
英文摘要：
In wideband millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems, channel estimation is challenging due to the hybrid analog-digital architecture, which compresses the received pilot signal and makes channel estimation a compressive sensing (CS) problem. However, existing high-performance CS algorithms usually suffer from high complexity. On the other hand, the beam squint effect caused by huge bandwidth and massive antennas will deteriorate estimation performance. In this paper, frequency-dependent angular dictionaries are first adopted to compensate for beam squint. Then, the expectation-maximization (EM)-based sparse Bayesian learning (SBL) algorithm is enhanced in two aspects, where the E-step in each iteration is implemented by approximate message passing (AMP) to reduce complexity while the M-step is realized by a deep neural network (DNN) to improve performance. In simulation, the proposed AMP-SBL unfolding-based channel estimator achieves satisfactory performance with low complexity.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
