Deep Learning-enhanced Massive Channel Estimation for Reconfigurable Intelligent Surface-aided Massive Machine-Type Communication
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摘要: 大规模机器类通信 (mMTC) 是第5代移动通信系统的重要应用场景之一，可实现每平方公里近百万级设备的连接。考虑到mMTC传播环境的复杂性，该文引入可重构智能超表面 (RIS) 进行上行免授权的传输，由此级联形成用户与RIS、RIS与基站 (BS) 之间的信道链路，从而有效控制无线信号传输的质量。在此基础上，建立Turbo译码消息传递思想下的降噪学习系统，通过大量的训练数据，以学习RIS辅助的级联信道状态信息，并对其进行估计。此外，该文对RIS辅助的mMTC信道估计结果进行了统计分析，以验证所提方案的准确性。数值仿真结果和理论分析结果表明，该文方法优于其他压缩感知类的方法。Abstract: Massive Machine-Type Communication (mMTC) is one of the typical scenarios of the fifth-generation mobile communications systems, and nearly one million devices per square kilometer can be connected under this circumstance. The Reconfigurable Intelligent Surface (RIS) is applied for the grant-free uplink transmission due to the complexity of the propagation environment in the scenario of massive connectivity. Then, the cascaded channel, i.e., the channel link between devices and the RIS, as well as the channel link between the RIS and the Base Station (BS), is formed. Consequently, the quality of the wireless signal transmission can be controlled effectively. On this basis, a denoising learning system is designed using the principle of turbo decoding message passing. The RIS-aided cascaded CSI is learned and estimated through a large number of training data. In addition, the statistical analysis of the RIS-assisted mMTC channel estimation is performed to verify the accuracy of the proposed scheme. Numerical simulation results and theoretical analyses show that the proposed technique is superior to other compressed-sensing-type methods.
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