Article: Distributed capacity optimisation and resource allocation in heterogeneous mobile networks using advanced serverless connectivity strategies Journal: International Journal of Sensor Networks (IJSNET) 2024 Vol.45 No.3 pp.127 - 147 Abstract: In response to the increasing complexity and diversity of heterogeneous mobile networks, this paper addresses the challenges posed by the increasing complexity and diversity of heterogeneous mobile networks, which require support for a wide range of applications and services with varying quality-of-service requirements. We proposed a novel approach to enhance network performance and efficiency in which, our methodology integrates artificial intelligence, machine learning, and serverless computing to develop a cognitive mobile network architecture that adapts in real-time to changing conditions. Additionally, we introduce a downlink resource management framework utilising game theory and optimisation algorithms, a hybrid power control and user association algorithm based on reinforcement learning, and a suite of distributed algorithms for intelligent task-offloading. Through extensive simulations and comparisons with existing methods, our approach demonstrates significant improvements in network performance and user satisfaction, achieving up to 25% and 40% reductions in network-wide throughput and energy consumption, respectively. Inderscience Publishers - linking academia, business and industry through research

Title: Distributed capacity optimisation and resource allocation in heterogeneous mobile networks using advanced serverless connectivity strategies

Authors: Nan Zhou; Ya Nan Li; Amin Mohajer

Addresses: Xuchang Vocational and Technical College, XuChang, HeNan, 461000, China ' Xuchang Vocational and Technical College, XuChang, HeNan, 461000, China ' Department of Communications Technology, ICT Research Institute (ITRC), Tehran, Iran

Abstract: In response to the increasing complexity and diversity of heterogeneous mobile networks, this paper addresses the challenges posed by the increasing complexity and diversity of heterogeneous mobile networks, which require support for a wide range of applications and services with varying quality-of-service requirements. We proposed a novel approach to enhance network performance and efficiency in which, our methodology integrates artificial intelligence, machine learning, and serverless computing to develop a cognitive mobile network architecture that adapts in real-time to changing conditions. Additionally, we introduce a downlink resource management framework utilising game theory and optimisation algorithms, a hybrid power control and user association algorithm based on reinforcement learning, and a suite of distributed algorithms for intelligent task-offloading. Through extensive simulations and comparisons with existing methods, our approach demonstrates significant improvements in network performance and user satisfaction, achieving up to 25% and 40% reductions in network-wide throughput and energy consumption, respectively.

Keywords: cognitive mobile network architecture; downlink resource management; hybrid power control and user association; UA; intelligent task-offloading; serverless computing.

DOI: 10.1504/IJSNET.2024.139853

International Journal of Sensor Networks, 2024 Vol.45 No.3, pp.127 - 147

Received: 02 Aug 2023
Accepted: 23 Feb 2024
Published online: 08 Jul 2024 *

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Title: Distributed capacity optimisation and resource allocation in heterogeneous mobile networks using advanced serverless connectivity strategies

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