Buffer transference strategy for power control in B5G-ultra-dense wireless cellular networks

Anzaldo, Alexis; Andrade, Ángel G.

doi:10.1007/s11276-022-03087-6

Cited by 2 publications

(1 citation statement)

References 17 publications

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“…Moreover, in [18], the energy optimization problem in mobile networks is considered by using a neural network-based algorithm to enable the MIMO feature only when necessary to reach a satisfactory user QoE. The authors presented a different viewpoint in [19], where they use RL for power control in cellular networks. They propose a novel training strategy to accelerate learning to avoid performance degradation during state space exploration.…”

Section: Literature Reviewmentioning

confidence: 99%

Self-Optimized Agent for Load Balancing and Energy Efficiency: A Reinforcement Learning Framework with Hybrid Action Space

Sabry,

Nabil,

Alsuhli

et al. 2023

Preprint

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Section: Literature Reviewmentioning

confidence: 99%

Self-Optimized Agent for Load Balancing and Energy Efficiency: A Reinforcement Learning Framework with Hybrid Action Space

Sabry,

Nabil,

Alsuhli

et al. 2023

Preprint

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Intelligence-learning driven resource allocation for B5G Ultra-Dense Networks: A structured literature review

Anzaldo¹,

Rodríguez²,

Andrade³

2023

Preprint

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Network densification is a suitable solution to improve the capacity of future mobile networks. However, deploying massive low-power base stations sharing the radio spectrum will cause increased interference reducing the ultra-dense networks (UDN) performance. Resource Allocation (RA) proposals have been developed for decades to meet mobile subscribers' data traffic and QoS demands and to prevent harmful interference. However, as networks evolve and mobile applications request more bandwidth, high data rates, and ultra-reliable low latency, the RA problem has become more complex. Machine Learning (ML) techniques have recently been explored to significantly reduce the computational complexity of RA problems and improve overall UDN performance compared to traditional methods. This paper systematically focuses on the most relevant research contributions that use ML techniques to produce accurate channel and power allocation results in UDN. A total of 56 articles were analyzed from a thorough selection process from manuscripts published from 2010 to 2022 in different academic databases. We describe the main aim of these research works and, according to the ML technique applied, have classified them into ANN-based, RL-based, or DRL-based models. Also, we identify the design features of reinforcement learning strategies used to enhance Key Performance Indicators (KPIs), such as energy and spectral efficiency, throughput, interference, or fairness. Research directions are discussed based on the findings.

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Buffer transference strategy for power control in B5G-ultra-dense wireless cellular networks

Cited by 2 publications

References 17 publications

Self-Optimized Agent for Load Balancing and Energy Efficiency: A Reinforcement Learning Framework with Hybrid Action Space

Self-Optimized Agent for Load Balancing and Energy Efficiency: A Reinforcement Learning Framework with Hybrid Action Space

Intelligence-learning driven resource allocation for B5G Ultra-Dense Networks: A structured literature review

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