DDPG-Based Wireless Resource Allocation for Time-Constrained Applications
Hu, Hang; Hernandez, Marco; Kim, Yang G.; Ahmed, Kazi J.; Tsukamoto, Kazuya; Lee, Myung J. (2024-07-03)
IEEE
03.07.2024
H. Hu, M. Hernandez, Y. G. Kim, K. J. Ahmed, K. Tsukamoto and M. J. Lee, "DDPG-Based Wireless Resource Allocation for Time-Constrained Applications," 2024 IEEE Wireless Communications and Networking Conference (WCNC), Dubai, United Arab Emirates, 2024, pp. 1-6, doi: 10.1109/WCNC57260.2024.10570841.
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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-202408285621
https://urn.fi/URN:NBN:fi:oulu-202408285621
Tiivistelmä
Abstract
This paper presents a novel model-free resource allocation framework for the downlink of 5G cellular networks to guarantee stringent QoS requirements in wireless applications. A Deep deterministic policy gradient (DDPG) agent with a modified Genetic Algorithm (GA) based resource allocation framework is proposed to balance the tradeoffs between reliability, latency, and data rate. Any feasible point in the rate-latency-reliability domain can be achieved with this approach. Compared to state-of-the-art approaches DDPG-Dual and DDPG-PSO, the proposed model achieves higher reliability and scalability in joint optimization with QoS constraints. Specifically, the proposed model guarantees the expected reliability with 25 % and 42.86 % improvement respectively over the compared models. In terms of conventional effective bandwidth approach, the proposed model achieves 30.82 % improvement of energy efficiency under the same QoS constraints. Moreover, the proposed model offers a practical solution, namely, three times faster convergence and only 6.7% of the scheduling time compared to the ground truth Dual decomposition optimization.
This paper presents a novel model-free resource allocation framework for the downlink of 5G cellular networks to guarantee stringent QoS requirements in wireless applications. A Deep deterministic policy gradient (DDPG) agent with a modified Genetic Algorithm (GA) based resource allocation framework is proposed to balance the tradeoffs between reliability, latency, and data rate. Any feasible point in the rate-latency-reliability domain can be achieved with this approach. Compared to state-of-the-art approaches DDPG-Dual and DDPG-PSO, the proposed model achieves higher reliability and scalability in joint optimization with QoS constraints. Specifically, the proposed model guarantees the expected reliability with 25 % and 42.86 % improvement respectively over the compared models. In terms of conventional effective bandwidth approach, the proposed model achieves 30.82 % improvement of energy efficiency under the same QoS constraints. Moreover, the proposed model offers a practical solution, namely, three times faster convergence and only 6.7% of the scheduling time compared to the ground truth Dual decomposition optimization.
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