Decentralized energy-efficient base station operation for green cellular networks

Wong, Wei-Te; Yu, Ya-Ju; Pang, Ai‐Chun

doi:10.1109/glocom.2012.6503945

Cited by 12 publications

(1 citation statement)

References 10 publications

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“…BS switching operations are a promising method for addressing energy consumption problems in dense mobile networks. However, it is an NP-hard problem to determine the optimal BS switching operation that minimizes the energy consumption with various constraints in polynomial time [9]. Therefore, heuristic and greedy methods have been adopted to determine optimal BS switching operations [4,5,7,10].…”

Section: Related Studiesmentioning

confidence: 99%

Deep reinforcement learning for base station switching scheme with federated LSTM‐based traffic predictions

Park,

Yoon

2024

ETRI Journal

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To meet increasing traffic requirements in mobile networks, small base stations (SBSs) are densely deployed, overlapping existing network architecture and increasing system capacity. However, densely deployed SBSs increase energy consumption and interference. Although these problems already exist because of densely deployed SBSs, even more SBSs are needed to meet increasing traffic demands. Hence, base station (BS) switching operations have been used to minimize energy consumption while guaranteeing quality‐of‐service (QoS) for users. In this study, to optimize energy efficiency, we propose the use of deep reinforcement learning (DRL) to create a BS switching operation strategy with a traffic prediction model. First, a federated long short‐term memory (LSTM) model is introduced to predict user traffic demands from user trajectory information. Next, the DRL‐based BS switching operation scheme determines the switching operations for the SBSs using the predicted traffic demand. Experimental results confirm that the proposed scheme outperforms existing approaches in terms of energy efficiency, signal‐to‐interference noise ratio, handover metrics, and prediction performance.

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Section: Related Studiesmentioning

confidence: 99%

Deep reinforcement learning for base station switching scheme with federated LSTM‐based traffic predictions

Park,

Yoon

2024

ETRI Journal

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Base station density optimization for high energy efficiency in two-tier cellular networks

Peng

Yang

et al. 2014

2014 IEEE Global Communications Conference

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The base station (BS) density configuration is a key factor to improve energy efficiency (EE) performances. In this paper, BS density configurations for achieving the optimal EE performance in two-tier cellular networks are analyzed, where the Poisson point process (PPP) is used to model the BS spatial distribution. To make the EE performances trackable and analyzable, the BS density optimization in each tier is transformed into an equivalent problem that jointly optimizes "the sum and the ratio of BS densities". This equivalent optimization problem is not necessarily convex, while its monotonicity with different power consumptions of BSs is analyzable. Considering the optimal BS density configuration is not unique and the closed-form solution for achieving the best EE performance is extremely difficult to be derived, a dynamic gradient based iterative algorithm by solving the quadratic functions is proposed. Furthermore, the quantitative analysis of EE performances based on the data fitting method shows that the approximately linear relationship between the optimal BS density and the user density holds only under specific conditions. Simulation results have demonstrated the effectiveness of the proposed algorithm and verified relevant conclusions.

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Greedy search algorithm based two stage energy saving technique for dense LTE-A HetNets

Narasimhan

Kashyap

Vikram

et al. 2017

2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI)

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Decentralized energy-efficient base station operation for green cellular networks

Cited by 12 publications

References 10 publications

Deep reinforcement learning for base station switching scheme with federated LSTM‐based traffic predictions

Deep reinforcement learning for base station switching scheme with federated LSTM‐based traffic predictions

Base station density optimization for high energy efficiency in two-tier cellular networks

Greedy search algorithm based two stage energy saving technique for dense LTE-A HetNets

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