Optimal learning paradigm and clustering for effective radio resource management in 5G HetNets

Iqbal, Muhammad Usman, Ansari, Ejaz Ahmad, Akhtar, Saleem, Farooq-i-Azam, Muhammad, Hassan, Syed Raheel and Asif, Rameez (2023) Optimal learning paradigm and clustering for effective radio resource management in 5G HetNets. IEEE Access, 11. pp. 41264-41280. ISSN 2169-3536

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Ultra-dense heterogeneous networks (UDHN) based on small cells are a requisite part of the future cellular networks as they are proposed as one of the enabling technologies to handle coverage and capacity problems. But co-tier and cross-tier interferences in UDHN severely degrade the quality of service due to K-tiered architecture. Machine learning based radio resource management either through independent learning or cooperative learning is a proven efficient scheme for interference mitigation and quality of service provision in UDHN in a both distributive and cooperative manner. However, an optimal learning paradigm selection, i.e., either independent or cooperative learning and optimal cooperative cluster size in cooperative learning for efficient radio resource management in UDHN is still an open research problem. In this article, a Q-learning based radio resource management scheme is proposed and evaluated for both distributive and cooperative schemes using independent and cooperative learning. The proposed Q-learning solution follows the $\epsilon -$ greedy policy for optimal convergence. The simulation results for the UDHN in an urban setup show that in comparison to the independent learning paradigm, cooperative learning has no significant impact on macro cell user capacity. However, there is a significant improvement in small cell user capacity and the sum capacity of the cooperating small cells in the cluster. A significant increase of 48.57% and 37.9% is observed in the small cell user capacity, and sum capacity of the cooperating small cells, respectively, using cooperative learning as compared to independent learning which sets cooperative learning as an optimal learning strategy in UDHN. The improvement in small cell user capacity is at cost of increased computational time which is directly proportional to the number of cooperating small cells. To solve the issue of computational time in cooperative learning, an optimal clustering algorithm is proposed. The proposed optimal clustering reduced the computational time by four times in cooperative Q-learning.

Item Type: Article
Uncontrolled Keywords: 5g,5g mobile communication,adaptive systems,heterogeneous networks,interference,optimization,q-learning,q-learning,quality of service,radio resource management,resource management,computer science(all),materials science(all),engineering(all),electrical and electronic engineering,4* ,/dk/atira/pure/subjectarea/asjc/1700
Faculty \ School: Faculty of Science > School of Computing Sciences
UEA Research Groups: Faculty of Science > Research Groups > Cyber Security Privacy and Trust Laboratory
Faculty of Science > Research Groups > Smart Emerging Technologies
Faculty of Science > Research Groups > Centre for Photonics and Quantum Science
Related URLs:
Depositing User: LivePure Connector
Date Deposited: 17 May 2023 09:33
Last Modified: 28 Aug 2023 01:13
URI: https://ueaeprints.uea.ac.uk/id/eprint/92076
DOI: 10.1109/ACCESS.2023.3268543

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