This letter focuses on the problem of uplink channel estimation with the reduced fronthaul overhead in cell-free massive multiple-input multiple-output (mMIMO) systems. First, we propose a sub-sampling scheme to reduce the dimension of fronthaul. Then, we exploit the inherent channel sparsity and model the underdetermined channel estimation problem as an off-grid sparse signal recovery problem. Finally, an enhanced sparse Bayesian learning (ESBL) channel estimation algorithm is proposed to refine the sampled grid points and recover the sparse channel iteratively. Simulation results demonstrate that the proposed algorithm achieves a significant reduction on the fronthaul overhead and offers a better channel estimation performance.

中文翻译：

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无小区大规模 MIMO 系统中减少前传开销的上行链路信道估计


这封信重点讨论了无小区大规模多输入多输出 (mMIMO) 系统中的上行链路信道估计问题以及减少的前传开销。首先，我们提出了一种子采样方案来减少前传的维度。然后，我们利用固有的信道稀疏性，并将欠定信道估计问题建模为离网稀疏信号恢复问题。最后，提出了一种增强稀疏贝叶斯学习（ESBL）信道估计算法来细化采样网格点并迭代恢复稀疏信道。仿真结果表明，该算法显着降低了前传开销，并提供了更好的信道估计性能。