Deep Learning for Hybrid Beamforming with Finite Feedback in GSM Aided mmWave MIMO Systems

Zhilin Lu, Xudong Zhang, Rui Zeng, Jintao Wang
2/15/2023
cs.ITcs.AIeess.SP

No official code found. Best implementation: Kylin9511/GsmEFBNet (8 stars, last commit 2023-02-16).

Abstract

Hybrid beamforming is widely recognized as an important technique for millimeter wave (mmWave) multiple input multiple output (MIMO) systems. Generalized spatial modulation (GSM) is further introduced to improve the spectrum efficiency. However, most of the existing works on beamforming assume the perfect channel state information (CSI), which is unrealistic in practical systems. In this paper, joint optimization of downlink pilot training, channel estimation, CSI feedback, and hybrid beamforming is considered in GSM aided frequency division duplexing (FDD) mmWave MIMO systems. With the help of deep learning, the GSM hybrid beamformers are designed via unsupervised learning in an end-to-end way. Experiments show that the proposed multi-resolution network named GsmEFBNet can reach a better achievable rate with fewer feedback bits compared with the conventional algorithm.

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Code Implementations(1)

Kylin9511/GsmEFBNetCommunity100%

This is the open-source library for the inference verification of the GsmEFBNet

83Python3 years ago

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Cite this paper

@article{lu2023deep,
  title  = {Deep Learning for Hybrid Beamforming with Finite Feedback in GSM Aided mmWave MIMO Systems},
  author = {Zhilin Lu and Xudong Zhang and Rui Zeng and Jintao Wang},
  year   = {2023},
  eprint = {2302.07601},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2302.07601v1}
}

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