Deep Learning-Based Transmit Power Control for Device Activity Detection and Channel Estimation in Massive Access

Zhuo Sun, Nan Yang, Chunhui Li, Jinhong Yuan, Tony Q.S. Quek

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

We propose a transmit power control (TPC) scheme for grant-free multiple access, where each device is able to determine its transmit power based on a TPC function. For the proposed scheme, we design a novel deep learning framework to jointly design the TPC functions and the parametric Stein's unbiased risk estimate (SURE) approximate message passing (AMP) algorithm, which significantly improves the accuracy of active device detection and channel estimation, particularly for short pilot sequences. Simulations are conducted to demonstrate the advantages of our proposed deep learning framework on massive device activity detection and channel estimation compared to existing schemes.

Original languageEnglish
Pages (from-to)183-187
Number of pages5
JournalIEEE Wireless Communications Letters
Volume11
Issue number1
DOIs
Publication statusPublished - 2022 Jan 1

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Electrical and Electronic Engineering

Fingerprint

Dive into the research topics of 'Deep Learning-Based Transmit Power Control for Device Activity Detection and Channel Estimation in Massive Access'. Together they form a unique fingerprint.

Cite this