Resource Allocation for FeMBB and eURLLC Coexistence in RSMA-Based Cellular Networks
Taskou, Shiva Kazemi; Rasti, Mehdi (2024-07-03)
IEEE
03.07.2024
S. K. Taskou and M. Rasti, "Resource Allocation for FeMBB and eURLLC Coexistence in RSMA-Based Cellular Networks," 2024 IEEE Wireless Communications and Networking Conference (WCNC), Dubai, United Arab Emirates, 2024, pp. 1-6, doi: 10.1109/WCNC57260.2024.10570774
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© 2024 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.
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:oulu-202410046184
https://urn.fi/URN:NBN:fi:oulu-202410046184
Tiivistelmä
Abstract
This paper studies the Joint Resource block (RB) allocation and Power control (JRP) problem for the coexistence of further enhanced mobile broadband (FeMBB) and extreme ultrareliable low latency (eURLLC) services in rate splitting multiple access-based next-generation (e.g., 5G-Advanced and 6G) wireless networks. In the JRP problem, for each FeMBB user, a minimum data rate requirement is considered, and eURLLC users should meet their latency and reliability requirements. To address the JRP problem, we propose a hybrid deep reinforcement learning (HDRL-JRP) algorithm, in which a double dueling deep Q-network is employed for RB allocation and a deep deterministic policy gradient is used for power control. Via simulation results, the performance of the HDRL-JRP algorithm is demonstrated in terms of total data rate.
This paper studies the Joint Resource block (RB) allocation and Power control (JRP) problem for the coexistence of further enhanced mobile broadband (FeMBB) and extreme ultrareliable low latency (eURLLC) services in rate splitting multiple access-based next-generation (e.g., 5G-Advanced and 6G) wireless networks. In the JRP problem, for each FeMBB user, a minimum data rate requirement is considered, and eURLLC users should meet their latency and reliability requirements. To address the JRP problem, we propose a hybrid deep reinforcement learning (HDRL-JRP) algorithm, in which a double dueling deep Q-network is employed for RB allocation and a deep deterministic policy gradient is used for power control. Via simulation results, the performance of the HDRL-JRP algorithm is demonstrated in terms of total data rate.
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