Sparsity-Based Channel Estimation Exploiting Deep Unrolling for Downlink Massive MIMO
Sparsity-Based Channel Estimation Exploiting Deep Unrolling for Downlink Massive MIMO
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DOI：
10.1109/globecom54140.2023.10437911
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发表时间：
2023-09
期刊：
影响因子：
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通讯作者：
An-Jen Chen;Wenbo Xu;Liyang Lu;Yue Wang
中科院分区：
文献类型：
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作者：
An-Jen Chen;Wenbo Xu;Liyang Lu;Yue Wang
大量的多输入多输出（MIMO）在5G无线通信系统中具有很大的优势，这是由于其频谱和能源效率。但是，数百个天线需要大量的飞行员开销，以保证FDD大型MIMO系统中的可靠通道估计。通过利用大规模MIMO通道的固有稀疏结构，但遭受高复杂性，已将压缩传感（CS）应用于通道估计。为了克服这一挑战，本文通过整合模型驱动的CS和数据驱动的深层展开技术来开发混合通道估计方案。所提出的方案包括一个粗糙的估计部分和一个精细的校正零件，以分别利用通道的框架间和框架内的稀疏性，以大大减少飞行员开销。提供理论结果以表明精细校正和粗估计网的收敛性。提供了仿真结果，以验证我们的方案可以估计较低的飞行员开销的MIMO通道，同时确保估计准确性相对较低。
Massive multiple-input multiple-output (MIMO) enjoys great advantage in 5G wireless communication systems owing to its spectrum and energy efficiency. However, hundreds of antennas require large volumes of pilot overhead to guarantee reliable channel estimation in FDD massive MIMO system. Compressive sensing (CS) has been applied for channel estimation by exploiting the inherent sparse structure of massive MIMO channel but suffer from high complexity. To overcome this challenge, this paper develops a hybrid channel estimation scheme by integrating the model-driven CS and data-driven deep unrolling technique. The proposed scheme consists of a coarse estimation part and a fine correction part to respectively exploit the inter- and intra-frame sparsities of channels to greatly reduce the pilot overhead. Theoretical result is provided to indicate the convergence of the fine correction and coarse estimation net. Simulation results are provided to verify that our scheme can estimate MIMO channels with low pilot overhead while guaranteeing estimation accuracy with relatively low complexity.