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AoA-Based Pilot Assignment in Massive MIMO Systems Using Deep Reinforcement Learning

Research output: Contribution to journalArticlepeer-review

Yasaman Omid, Seyed Mohammad Reza Hosseini, Seyyed Mohammad Mahdi Shahabi, Mohammad Shikh-Bahaei, Arumugam Nallanathan

Original languageEnglish
Article number9454483
Pages (from-to)2948-2952
Number of pages5
Issue number9
Accepted/In press2021
PublishedSep 2021

Bibliographical note

Publisher Copyright: IEEE Copyright: Copyright 2021 Elsevier B.V., All rights reserved.

King's Authors


In this letter, the problem of pilot contamination in a multi-cell massive multiple input multiple output (M-MIMO) system is addressed using deep reinforcement learning (DRL). To this end, a pilot assignment strategy is designed that adapts to the channel variations while maintaining a tolerable pilot contamination effect. Using the angle of arrival (AoA) information of the users, a cost function, portraying the reward, is presented, defining the pilot contamination effects in the system. Numerical results illustrate that the DRL-based scheme is able to track the changes in the environment, learn the near-optimal pilot assignment, and achieve a close performance to that of the optimum pilot assignment performed by exhaustive search, while maintaining a low computational complexity.

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