dc.contributor.author | Omid, Y | |
dc.contributor.author | Hosseini, SMR | |
dc.contributor.author | Shahabi, SMM | |
dc.contributor.author | Shikh-Bahaei, M | |
dc.contributor.author | Nallanathan, A | |
dc.date.accessioned | 2021-07-08T13:13:27Z | |
dc.date.available | 2021-07-08T13:13:27Z | |
dc.date.issued | 2021-06-01 | |
dc.identifier.issn | 1089-7798 | |
dc.identifier.uri | https://qmro.qmul.ac.uk/xmlui/handle/123456789/72953 | |
dc.description.abstract | In this paper, 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. | en_US |
dc.format.extent | 1 - 1 | |
dc.publisher | IEEE | en_US |
dc.relation.ispartof | IEEE Communications Letters | |
dc.title | AoA-Based Pilot Assignment in Massive MIMO Systems Using Deep Reinforcement Learning | en_US |
dc.type | Article | en_US |
dc.rights.holder | © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | |
dc.identifier.doi | 10.1109/lcomm.2021.3089234 | |
pubs.issue | 99 | en_US |
pubs.notes | Not known | en_US |
pubs.volume | PP | en_US |
rioxxterms.funder | Default funder | en_US |
rioxxterms.identifier.project | Default project | en_US |