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

研究成果: Article同行評審

摘要

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.

原文English
頁(從 - 到)183-187
頁數5
期刊IEEE Wireless Communications Letters
11
發行號1
DOIs
出版狀態Published - 2022 一月 1

All Science Journal Classification (ASJC) codes

  • 控制與系統工程
  • 電氣與電子工程

指紋

深入研究「Deep Learning-Based Transmit Power Control for Device Activity Detection and Channel Estimation in Massive Access」主題。共同形成了獨特的指紋。

引用此