Millimeter-wave massive multiple-input multiple-output (MIMO) can use a lens antenna array to considerably reduce the number of radio frequency (RF) chains, but channel estimation is challenging due to the number of RF chains is much smaller than that of antennas. By exploiting the sparsity of beamspace channels, the beamspace channel estimation can be formulated as a sparse signal recovery problem, which can be solved by the classical iterative algorithm named approximate message passing (AMP), and its corresponding version learned AMP (LAMP) realized by a deep neural network (DNN). However, these existing schemes cannot achieve satisfactory estimation accuracy. To improve the channel estimation performance, we propose a prior-aided Gaussian mixture LAMP (GM-LAMP) based beamspace channel estimation scheme in this paper. Specifically, based on the prior information that beamspace channel elements can be modeled by the Gaussian mixture distribution, we first derive a new shrinkage function to refine the AMP algorithm. Then, by replacing the original shrinkage function in the LAMP network with the derived Gaussian mixture shrinkage function, a prior-aided GM-LAMP network is developed to estimate the beamspace channel more accurately. Simulation results by using both the theoretical channel model and the ray-tracing based channel dataset show that, the proposed GM-LAMP network can achieve better channel estimation accuracy.

Xiuhong Wei

Chen Hu

Linglong Dai

IEEE Transactions on Communications

IEEE TRANSACTIONS ON COMMUNICATIONS 通过包括无线电在内的电磁传播发表高质量的手稿，介绍所有电信技术（包括电话、电报、传真和电视）的最新进展；金属丝;天线;地下、同轴和海底电缆；波导、通信卫星和激光器；在海洋、航空、空间和固定站服务方面；中继器、无线电中继、信号存储和再生；电信错误检测和纠正；多路复用和载波技术；通信交换系统；数据通信；和传播理论。

The IEEE Transactions on Communications publishes high-quality manuscripts that cover the latest advancements in electromagnetic propagation, including telecommunications technologies such as radio, telephone, telegraph, fax, and television. The content encompasses technologies like metallic wires, antennas, underground, coaxial, and submarine cables, waveguides, communication satellites, and lasers, as well as their applications in marine, aviation, space, and fixed station services. Additionally, the journal explores topics such as repeaters, radio relays, signal storage and regeneration, telecommunications error detection and correction, multiplexing and carrier technologies, communication switching systems, data communication, and propagation theory.

《IEEE Transactions on Communications》期刊发表高质量的手稿，涵盖电磁传播领域的最新进展，包括无线电、电话、电报、传真和电视等电信技术。内容涉及金属丝、天线、地下、同轴和海底电缆、波导、通信卫星和激光器等技术，以及在海洋、航空、空间和固定站服务中的应用。此外，期刊还探讨中继器、无线电中继、信号存储和再生、电信错误检测与纠正、多路复用和载波技术、通信交换系统、数据通信和传播理论等主题。

IEEE Trans. Commun.

Deep Learning for Beamspace Channel Estimation in Millimeter-Wave Massive MIMO Systems

A prior-aided Gaussian mixture LAMP (GM-LAMP) based beamspace channel estimation scheme based on a new shrinkage function to refine the AMP algorithm that can achieve better channel estimation accuracy than existing schemes.

Category	Quartile
计算机科学	2
计算机科学, 工程电子与电气	2
计算机科学, 电信学	2

Deep Learning for Beamspace Channel Estimation in Millimeter-Wave Massive MIMO Systems

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