Particle Swarm-Based Cell Range Expansion for Heterogeneous Mobile Networks

Kuribayashi, Hugo; Souza, Marcelo Alves de; Gomes, Diego de Azevedo; Silva, Ketyllen Da Costa; Silva, Marcelino; Costa, João Crisóstomo Weyl Albuquerque; Francês, Carlos Renato Lisboa

doi:10.1109/access.2020.2975981

Cited by 21 publications

(7 citation statements)

References 52 publications

(71 reference statements)

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“…In [21], a particle swarm optimization (PSO) algorithm was utilized to find optimal BOs for all SBSs. The authors formulated a user association problem to maximize the number of UEs with fulfilled downlink requirements and the number of BSs associated with users.…”

Section: Related Workmentioning

confidence: 99%

A Cooperative Online Learning-Based Load Balancing Scheme for Maximizing QoS Satisfaction in Dense HetNets

et al. 2021

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This paper proposes a cooperative multi-agent online reinforcement learning-based (CO-MORL) bias offset (BO) control scheme for cell range expansion (CRE) in dense heterogeneous networks (HetNets). The proposed COMORL scheme controls BOs for CRE to maximize the number of user equipments (UEs) that satisfy their quality of service (QoS) requirements, especially in terms of delay and data rates. For this purpose, we develop a QoS satisfaction indicator that measures a violation of delay requirements by considering both QoS requirements and signal-to-interference-plus-noise ratio (SINR). Also, we formulate a Markov decision process (MDP) model and solve it with a cooperative multi-agent online reinforcement learning algorithm. The proposed COMORL scheme maximizes a global utility for load-coupled base stations. Our simulation results validate the proposed COMORL scheme's effectiveness in terms of throughput, delay satisfaction ratio, and fairness. Specifically, we verify that the proposed COMORL scheme achieves a maximum of approximately 27% and 30% improvement of the delay satisfaction ratio, which is how many UEs satisfy their delay requirement over whole UEs in a serving BS, under medium and full traffic load, respectively, in a dynamic scenario, compared to the Max-SINR scheme.INDEX TERMS HetNets, cell range expansion, load balancing, QoS, cooperative multi-agent reinforcement learning.

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Section: Related Workmentioning

confidence: 99%

A Cooperative Online Learning-Based Load Balancing Scheme for Maximizing QoS Satisfaction in Dense HetNets

et al. 2021

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“…Besides, the proposed solution seeks to tackle the problem of user coverage in a critical and temporary situation. The evaluation was based on publications such as [20], [56], [57] and [58]. The proposed algorithm is shown in Algorithm 1.…”

Section: ) Stopping Criteriamentioning

confidence: 99%

Novel Multilayered Cellular Automata for Flying Cells Positioning on 5G Cellular Self-Organising Networks

et al. 2020

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“…The PSO is adopted in [70] to overcome drawbacks of the conventional cell range expansion techniques, i.e., users tend to connect to the BS with the highest received signal power. Instead of maximizing the sum rate as considered in existing literature, this work considers maximizing the number of users with satisfactory rate requirements and the number of BSs with at least one connected user.…”

Section: B User Association and Load Balancingmentioning

confidence: 99%

Swarm Intelligence for Next-Generation Wireless Networks: Recent Advances and Applications

Pham¹,

Nguyen²,

Mirjalili³

et al. 2020

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Due to the proliferation of smart devices and emerging applications, many next-generation technologies have been paid for the development of wireless networks. Even though commercial 5G has just been widely deployed in some countries, there have been initial efforts from academia and industrial communities for 6G systems. In such a network, a very large number of devices and applications are emerged, along with heterogeneity of technologies, architectures, mobile data, etc., and optimizing such a network is of utmost importance. Besides convex optimization and game theory, swarm intelligence (SI) has recently appeared as a promising optimization tool for wireless networks. As a new subdivision of artificial intelligence, SI is inspired by the collective behaviors of societies of biological species. In SI, simple agents with limited capabilities would achieve intelligent strategies for high-dimensional and challenging problems, so it has recently found many applications in nextgeneration wireless networks (NGN). However, researchers may not be completely aware of the full potential of SI techniques. In this work, our primary focus will be the integration of these two domains: NGN and SI. Firstly, we provide an overview of SI techniques from fundamental concepts to well-known optimizers. Secondly, we review the applications of SI to settle emerging issues in NGN, including spectrum management and resource allocation, wireless caching and edge computing, network security, and several other miscellaneous issues. Finally, we highlight open challenges and issues in the literature, and introduce some interesting directions for future research.

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Particle Swarm-Based Cell Range Expansion for Heterogeneous Mobile Networks

Cited by 21 publications

References 52 publications

A Cooperative Online Learning-Based Load Balancing Scheme for Maximizing QoS Satisfaction in Dense HetNets

A Cooperative Online Learning-Based Load Balancing Scheme for Maximizing QoS Satisfaction in Dense HetNets

Novel Multilayered Cellular Automata for Flying Cells Positioning on 5G Cellular Self-Organising Networks

Swarm Intelligence for Next-Generation Wireless Networks: Recent Advances and Applications

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