In this paper, we propose an untrained deep image prior-based compressive channel estimation (CE) algorithm in massive MIMO systems. We adopt deep convolutional generative network (DCGN) to learn the sparse prior of massive MIMO channels, whose weights can be optimized to match the compressed measurements. Unlike various supervised deep neural networks (DNN)-based CE algorithms, which require offline training over large simulated channel datasets, our DCGN does not require pre-training and can be learned online based on the real-time measurements to make it adaptable to the time-varying channels. Besides, DCGN can exploit the channel structure automatically without any prior knowledge. We further devise a learned regularization technique to improve the CE performance when the measurements are noisy and highly compressive. Simulations show that the proposed method can achieve more accurate and robust online CE performance than traditional compressive sensing (CS) and DNN-based methods.
Online Compressive Channel Learning Using Untrained Deep Generative Model
01.06.2022
548565 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
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