aalto1 untyped-item.component.html
User Throughput Optimization via Deep Reinforcement Learning for Beam Switching in mmWave Radio Access Networks
Loading...
Access rights
openAccess
acceptedVersion
URL
Journal Title
Journal ISSN
Volume Title
A4 Artikkeli konferenssijulkaisussa
This publication is imported from Aalto University research portal.
View publication in the Research portal (opens in new window)
View/Open full text file from the Research portal (opens in new window)
View publication in the Research portal (opens in new window)
View/Open full text file from the Research portal (opens in new window)
Authors
Hashemi, Ramin
Ranasinghe, Vismika
Veijalainen, Teemu
Kela, Petteri
Wichman, Risto
Date
Major/Subject
Mcode
Degree programme
Language
en
Pages
6
Series
2024 IEEE 35th International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2024, IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC
Abstract
In 5G, the analog beamforming architecture, typically used in millimeter wave (mmWave) bands, operates on a pre-defined beam set relying on grid-of-beams (GoB) approach. Therefore, efficient beam selection considering dynamic wireless environments incorporating the available measurements from the user equipment (UE) to enhance the overall throughput is of paramount importance. In typical beam selection scheme selecting a beam out of candidate beams would be to choose the beam that has the strongest reference signal received power (RSRP). This approach may not yield the optimal outcome. For example, selected beams might be reserved for UEs that cannot utilize all the available resource blocks (RBs) efficiently. To this end, a deep reinforcement learning (DRL)-based beam selection framework is introduced for maximizing the throughput of the users. We first formulate the proposed beam selection framework by utilizing the RSRP measurements, and time domain activation information of the beams to group the UEs in a way that the time and frequency resources are utilized more efficiently resulting in better observed throughput. The formulated problem is complex as the optimized policy for beam selection is to find a balance between signal quality and RB optimization simultaneously. Therefore, we employ recent advances in DRL to solve the formulated problem with an optimized policy. Specifically, proximal policy optimization (PPO) method incorporating recurrent neural networks (RNNs) for capturing the temporal properties of the wireless channel is exploited for training. Simulations demonstrate that up to 10% throughput gains can be achieved compared with baseline approaches.
Description
Publisher Copyright: © 2024 IEEE.
Other note
Citation
Hashemi, R, Ranasinghe, V, Veijalainen, T, Kela, P & Wichman, R 2025, User Throughput Optimization via Deep Reinforcement Learning for Beam Switching in mmWave Radio Access Networks. in 2024 IEEE 35th International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2024. IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC, IEEE, IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, Valencia, Spain, 02/09/2024. https://doi.org/10.1109/PIMRC59610.2024.10817344