Paper Info
Title
Deep Learning Based Antenna Selection for Channel Extrapolation in FDD Massive MIMO
Abstract
In massive multiple-input multiple-output (MIMO) systems, the large number of antennas would bring a great challenge for the acquisition of the accurate channel state information, especially in the frequency division duplex mode. To overcome the bottleneck of the limited number of radio links in hybrid beamforming, we utilize the neural networks (NNs) to capture the inherent connection between the uplink and downlink channel data sets and extrapolate the downlink channels from a subset of the uplink channel state information. We study the antenna subset selection problem in order to achieve the best channel extrapolation and decrease the data size of NNs. The probabilistic sampling theory is utilized to approximate the discrete antenna selection as a continuous and differentiable function, which makes the back propagation of the deep learning feasible. Then, we design the proper off-line training strategy to optimize both the antenna selection pattern and the extrapolation NNs. Finally, numerical results are presented to verify the effectiveness of our proposed massive MIMO channel extrapolation algorithm.
Year
DOI
Venue
2020
10.1109/WCSP49889.2020.9299795
2020 International Conference on Wireless Communications and Signal Processing (WCSP)
Keywords
DocType
ISSN
Massive multiple-input multiple-output,antenna selection,channel extrapolation,deep learning,probabilistic sampling
Conference
2325-3746
ISBN
Citations 
PageRank 
978-1-7281-7237-8
1
0.35
References 
Authors
11
6
Name
Order
Citations
PageRank
Yindi Yang110.35
Shun Zhang222627.93
Feifei Gao33093212.03
Chao Xu410.35
Jianpeng Ma56310.36
Octavia A. Dobre62064181.08