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Traffic Prediction and Random Access Control Optimization: Learning and Non-Learning-Based Approaches

Research output: Contribution to journalArticlepeer-review

Original languageEnglish
Article number9422330
Pages (from-to)16-22
Number of pages7
Issue number3
PublishedMar 2021

Bibliographical note

Funding Information: This work was supported by the Engineering and Physical Sciences Research Council (EPSRC), U.K., under Grant EP/R006466/1. Publisher Copyright: © 1979-2012 IEEE. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.

King's Authors


Random access channel (RACH) procedures in modern wireless communications are generally based on multi-channel slotted-ALOHA, which can be optimized by flexibly organizing devices' transmission and retransmission. However, due to the lack of information about the traffic generation statistics and the occurrence of random collision, optimizing RACH in an exact manner is generally challenging. In this article, we first summarize the general structure of optimization for different RACH schemes, and then review existing RACH optimization methods based on machine learning (ML) and non-ML techniques. We demonstrate that the ML-based methods can better optimize RACH schemes compared to non-ML-based methods due to their capability in solving high-complexity long-term optimization problems. To further improve the RACH performance, we introduce a decoupled learning strategy (DLS) for access control optimization, which individually executes two sub-tasks: traffic prediction and access control configuration. In detail, the traffic prediction relies on an online supervised learning method adopting recurrent neural networks that can accurately capture traffic statistics over consecutive frames, while the access control configuration uses either a non-ML-based controller or a cooperatively trained deep reinforcement learning (DRL)-based controller selected depending on the complexity of different random access schemes. Numerical results show that the DLS optimizer considerably outperforms conventional DRL optimizers in terms of higher training efficiency and better access performance.

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