Two-Timescale End-to-End Learning for Channel Acquisition and Hybrid Precoding

Qiyu Hu, Yunlong Cai, Kai Kang, Guanding Yu, Jakob Hoydis, Yonina C. Eldar

Research output: Contribution to journalArticlepeer-review

Abstract

In this paper, we propose an end-to-end deep learning-based joint transceiver design algorithm for millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems, which consists of deep neural network (DNN)-aided pilot training, channel feedback, and hybrid analog-digital (HAD) precoding. Specifically, we develop a DNN architecture that maps the received pilots into feedback bits at the receiver, and then further maps the feedback bits into the hybrid precoder at the transmitter. To reduce the signaling overhead and channel state information (CSI) mismatch caused by the transmission delay, a two-timescale DNN composed of a long-term DNN and a short-term DNN is developed. The analog precoders are designed by the long-term DNN based on the CSI statistics and updated once in a frame consisting of a number of time slots. In contrast, the digital precoders are optimized by the short-term DNN at each time slot based on the estimated low-dimensional equivalent CSI matrices. A two-timescale training method is also developed for the proposed DNN with a binary layer. We then analyze the generalization ability and signaling overhead for the proposed DNN based algorithm. Simulation results show that our proposed technique significantly outperforms conventional schemes in terms of bit-error rate performance with reduced signaling overhead and shorter pilot sequences.

Original languageEnglish
Pages (from-to)163-181
Number of pages19
JournalIEEE Journal on Selected Areas in Communications
Volume40
Issue number1
DOIs
StatePublished - 1 Jan 2022

Keywords

  • Channel acquisition
  • Deep learning
  • Hybrid precoding
  • Massive multiple-input multiple-output (MIMO)
  • Millimeter wave
  • Two-timescale

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Electrical and Electronic Engineering

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