2021/12/13 by J. Guo, Guo, J., L. Wang +6
Computer Science · Engineering · Mathematics · #Advanced MIMO Systems Optimization #Antenna Design and Optimization #FOS: Computer and information sciences #FOS: Electrical engineering #Full-Duplex Wireless Communications #Information Theory (cs.IT) #Machine Learning (cs.LG) #Signal Processing (eess.SP) #cs.IT #cs.LG #eess.SP #electronic engineering #information engineering #math.IT
paper · pdf · doi:10.48550/arxiv.2112.06405
6 pages, 4 figures
arxiv created 2021/12/13 · openalex publication_date 2021/12/13 · arxiv updated 2021/12/14 · openalex created_date 2022/05/05 · openalex updated_date 2026/07/28
In order to achieve reliable communication with a high data rate of massive multiple-input multiple-output (MIMO) systems in frequency division duplex (FDD) mode, the estimated channel state information (CSI) at the receiver needs to be fed back to the transmitter. However, the feedback overhead becomes exorbitant with the increasing number of antennas. In this paper, a two stages low rank (TSLR) CSI feedback scheme for millimeter wave (mmWave) massive MIMO systems is proposed to reduce the feedback overhead based on model-driven deep learning. Besides, we design a deep iterative neural network, named FISTA-Net, by unfolding the fast iterative shrinkage thresholding algorithm (FISTA) to achieve more efficient CSI feedback. Moreover, a shrinkage thresholding network (ST-Net) is designed in FISTA-Net based on the attention mechanism, which can choose the threshold adaptively. Simulation results show that the proposed TSLR CSI feedback scheme and FISTA-Net outperform the existing algorithms in various scenarios.