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

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

doi:10.1109/LWC.2021.3139608

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

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

Department of Computer Science and Information Engineering

Research output: Contribution to journal › Article › peer-review

Abstract

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.

Original language	English
Pages (from-to)	645-649
Number of pages	5
Journal	IEEE Wireless Communications Letters
Volume	11
Issue number	3
DOIs	https://doi.org/10.1109/LWC.2021.3139608
Publication status	Published - 2022 Mar 1

All Science Journal Classification (ASJC) codes

Control and Systems Engineering
Electrical and Electronic Engineering

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10.1109/LWC.2021.3139608

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title = "Deep Learning Approach for Outage-Constrained Non-Orthogonal Random Access",

abstract = "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.",

author = "Jang, {Han Seung} and Hoon Lee and Quek, {Tony Q.S.}",

note = "Funding Information: This work was supported in part by the National Research Foundation of Korea (NRF) Grant funded by the Korea Government Ministry of Science and ICT (MSIT) under Grant 2021R1F1A1058795, and in part by NRF under Grant 2021R1I1A3054575 Publisher Copyright: {\textcopyright} 2012 IEEE.",

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N2 - 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.

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

Abstract

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