Deep Learning Approach for Outage-Constrained Non-Orthogonal Random Access

Han Seung Jang, Hoon Lee, Tony Q.S. Quek

研究成果: Article同行評審

摘要

This letter presents deep neural network (DNN) approaches for non-orthogonal random access (NORA) systems where several devices are allowed to occupy the identical preamble. We desire to improve the reliability of the packet transmission of NORA devices with a careful management of multi-user interference. A novel transmit power control (TPC) mechanism is proposed which minimizes the maximum transmit power under constraints on link outage probabilities. The nonconvexity and unavailable outage formulations are addressed through DNNs. It is trained to yield feasible TPC solutions for outage constraints based on timing advance values. The viability of the proposed DNN approach is demonstrated with system-level simulations.

原文English
頁(從 - 到)645-649
頁數5
期刊IEEE Wireless Communications Letters
11
發行號3
DOIs
出版狀態Published - 2022 3月 1

All Science Journal Classification (ASJC) codes

  • 控制與系統工程
  • 電氣與電子工程

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