Logo image
Deep Learning Approach for Outage-Constrained Non-Orthogonal Random Access
Journal article   Peer reviewed

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

Han Seung Jang, Hoon Lee and Tony Q. S. Quek
IEEE wireless communications letters, Vol.11(3), pp.645-649
01/03/2022

Abstract

deep learning IoT Non-orthogonal random access outage Performance evaluation Power system reliability Probability Reliability Throughput timing advance Training Uplink
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.

Metrics

1 Record Views

Details

Logo image