Multichannel Relay assisted NOMA-ALOHA with Reinforcement Learning based Random Access

Haeyoung Lee; Sunyoung Lee; Youngwook Ko

doi:10.1109/VTC2023-Spring57618.2023.10200766

Multichannel Relay assisted NOMA-ALOHA with Reinforcement Learning based Random Access

Haeyoung Lee, Sunyoung Lee, Youngwook Ko

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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Abstract

We investigate multichannel relay assisted non-orthogonal multiple access (NOMA) in slotted ALOHA systems, where each user randomly accesses one of different channel slots and different transmit power for uplink transmissions over two-hop links, to and from the relay. By using multi-agent reinforcement learning, we propose greedy and non-greedy random access methods so that each user can learn its best strategies of random access over multiple relay slots. Random collisions and fading over the relay slots are both considered. The behaviors of relay-aided NOMA-ALOHA strategies are evaluated with the simulation. It is shown that the greedy method outperforms the non-greedy method in terms of average success rate. For deployment of relay, the greedy method benefits in improving transmission reliability under the symmetric relay channels (between the two-hop links) compared to asymmetric channels. Thus, it is interpreted that the proposed greedy method is more promising to the NOMA-ALOHA systems under a symmetric multichannel relay.

Original language	English
Title of host publication	2023 IEEE 97th Vehicular Technology Conference, VTC 2023-Spring - Proceedings
Place of Publication	Florence, Italy
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Number of pages	5
ISBN (Electronic)	9798350311143
DOIs	https://doi.org/10.1109/VTC2023-Spring57618.2023.10200766
Publication status	Published - 14 Aug 2023
Event	2023 IEEE 97th Vehicular Technology Conference (VTC2023-Spring) - Florence, Italy Duration: 20 Jun 2023 → 23 Jun 2023 Conference number: 97 https://ieeexplore.ieee.org/xpl/conhome/10199114/proceeding

Publication series

Name	IEEE Vehicular Technology Conference
Publisher	IEEE
Volume	2023-June
ISSN (Print)	1550-2252
ISSN (Electronic)	2577-2465

Conference

Conference	2023 IEEE 97th Vehicular Technology Conference (VTC2023-Spring)
Abbreviated title	VTC2023
Country/Territory	Italy
City	Florence
Period	20/06/23 → 23/06/23
Internet address	https://ieeexplore.ieee.org/xpl/conhome/10199114/proceeding

Keywords

Non-orthogonal multiple access; random access, ALOHA, relay, reinforcement learning
relay
random access
ALOHA
Non-orthogonal multiple access
reinforcement learning

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10.1109/VTC2023-Spring57618.2023.10200766Licence: Unspecified

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© 2023, 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. This is the accepted manuscript version of a conference paper which has been published in final form at https://doi.org/10.1109/VTC2023-Spring57618.2023.10200766
Accepted author manuscript, 2.43 MBLicence: Unspecified

Cite this

Lee, H., Lee, S., & Ko, Y. (2023). Multichannel Relay assisted NOMA-ALOHA with Reinforcement Learning based Random Access. In 2023 IEEE 97th Vehicular Technology Conference, VTC 2023-Spring - Proceedings (IEEE Vehicular Technology Conference; Vol. 2023-June). Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/VTC2023-Spring57618.2023.10200766

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Lee, H, Lee, S & Ko, Y 2023, Multichannel Relay assisted NOMA-ALOHA with Reinforcement Learning based Random Access. in 2023 IEEE 97th Vehicular Technology Conference, VTC 2023-Spring - Proceedings. IEEE Vehicular Technology Conference, vol. 2023-June, Institute of Electrical and Electronics Engineers (IEEE), Florence, Italy, 2023 IEEE 97th Vehicular Technology Conference (VTC2023-Spring), Florence, Italy, 20/06/23. https://doi.org/10.1109/VTC2023-Spring57618.2023.10200766

Multichannel Relay assisted NOMA-ALOHA with Reinforcement Learning based Random Access. / Lee, Haeyoung; Lee, Sunyoung; Ko, Youngwook.
2023 IEEE 97th Vehicular Technology Conference, VTC 2023-Spring - Proceedings. Florence, Italy: Institute of Electrical and Electronics Engineers (IEEE), 2023. (IEEE Vehicular Technology Conference; Vol. 2023-June).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AU - Lee, Sunyoung

AU - Ko, Youngwook

N1 - Conference code: 97

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Y1 - 2023/8/14

N2 - We investigate multichannel relay assisted non-orthogonal multiple access (NOMA) in slotted ALOHA systems, where each user randomly accesses one of different channel slots and different transmit power for uplink transmissions over two-hop links, to and from the relay. By using multi-agent reinforcement learning, we propose greedy and non-greedy random access methods so that each user can learn its best strategies of random access over multiple relay slots. Random collisions and fading over the relay slots are both considered. The behaviors of relay-aided NOMA-ALOHA strategies are evaluated with the simulation. It is shown that the greedy method outperforms the non-greedy method in terms of average success rate. For deployment of relay, the greedy method benefits in improving transmission reliability under the symmetric relay channels (between the two-hop links) compared to asymmetric channels. Thus, it is interpreted that the proposed greedy method is more promising to the NOMA-ALOHA systems under a symmetric multichannel relay.

AB - We investigate multichannel relay assisted non-orthogonal multiple access (NOMA) in slotted ALOHA systems, where each user randomly accesses one of different channel slots and different transmit power for uplink transmissions over two-hop links, to and from the relay. By using multi-agent reinforcement learning, we propose greedy and non-greedy random access methods so that each user can learn its best strategies of random access over multiple relay slots. Random collisions and fading over the relay slots are both considered. The behaviors of relay-aided NOMA-ALOHA strategies are evaluated with the simulation. It is shown that the greedy method outperforms the non-greedy method in terms of average success rate. For deployment of relay, the greedy method benefits in improving transmission reliability under the symmetric relay channels (between the two-hop links) compared to asymmetric channels. Thus, it is interpreted that the proposed greedy method is more promising to the NOMA-ALOHA systems under a symmetric multichannel relay.

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T2 - 2023 IEEE 97th Vehicular Technology Conference (VTC2023-Spring)

Y2 - 20 June 2023 through 23 June 2023

ER -

Multichannel Relay assisted NOMA-ALOHA with Reinforcement Learning based Random Access

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