Energy Efficient Pico Cell Range Expansion and Density Joint Optimization for Heterogeneous Networks with eICIC

Sun, Yanzan; Xia, Wenqing; Zhang, Shunqing; Wu, Yating; Wang, Tao; Fang, Yong

doi:10.3390/s18030762

Cited by 8 publications

(7 citation statements)

References 44 publications

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“…In recent years, stochastic geometry has attracted attention in analyzing the optimal BO for CRE. In [14], the authors jointly optimized the BO and the density of SBSs to maximize energy efficiency (EE). They analytically derived the closed-form expression of the network EE as a function of the density of SBSs and BO based on stochastic geometry theory.…”

Section: Related Workmentioning

confidence: 99%

“…The CMQ module in each BSs has to decide the optimal BO with not only considering its QT able S and QT able C but also considering neighboring BSs' QT able S and QT able C . Thus, it finds the optimal joint action ∆ * that maximizes (14) through repeatedly sending a message value This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/ This article has been accepted for publication in a future issue of this journal, but has not been fully edited.…”

Section: ) Message-passing-based Cooperative Q-learning Algorithmmentioning

confidence: 99%

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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%

Section: ) Message-passing-based Cooperative Q-learning Algorithmmentioning

confidence: 99%

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

et al. 2021

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“…By the end of 2020, it is anticipated that up to 50 billion devices will exist in the world including static and mobile platforms [7]. Due to this reason, there has been an upsurge in the research on HetNets to provide efficient and long-term solutions [8]. Zhang et al in [9] investigated the problem of user association in HetNets.…”

Section: A Related Workmentioning

confidence: 99%

Power Allocation and User Assignment Scheme for beyond 5G Heterogeneous Networks

Bakht

Jameel

Ali

et al. 2019

Wireless Communications and Mobile Computing

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The issue of spectrum scarcity in wireless networks is becoming prominent and critical with each passing year. Although several promising solutions have been proposed to provide a solution to spectrum scarcity, most of them have many associated tradeoffs. In this context, one of the emerging ideas relates to the utilization of cognitive radios (CR) for future heterogeneous networks (HetNets). This paper provides a marriage of two promising candidates (i.e., CR and HetNets) for beyond fifth generation (5G) wireless networks. More specifically, a joint power allocation and user assignment solution for the multi-user underlay CR-based HetNets has been proposed and evaluated. To counter the limiting factors in these networks, the individual power of transmitting nodes and interference temperature protection constraints of the primary networks have been considered. An efficient solution is designed from the dual decomposition approach, where the optimal user assignment is obtained for the optimized power allocation at each node. The simulation results validate the superiority of the proposed optimization scheme against conventional baseline techniques.

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“…Contrary to the uniform case, their results show that biasing has distinct effects on the coverage and rate performance of a non-uniform HetNet. Lastly, authors in [ 19 ] propose a load balancing solution for a two-tier HetNet based on stochastic geometry. Their algorithm performs a CRE biasing to achieve an optimal SBS density regarding network energy efficiency.…”

Section: Related Workmentioning

confidence: 99%

Machine Learning Aided Scheme for Load Balancing in Dense IoT Networks

Gómez

Shami

Wang

2018

Sensors

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With the dramatic increase of connected devices, the Internet of things (IoT) paradigm has become an important solution in supporting dense scenarios such as smart cities. The concept of heterogeneous networks (HetNets) has emerged as a viable solution to improving the capacity of cellular networks in such scenarios. However, achieving optimal load balancing is not trivial due to the complexity and dynamics in HetNets. For this reason, we propose a load balancing scheme based on machine learning techniques that uses both unsupervised and supervised methods, as well as a Markov Decision Process (MDP). As a use case, we apply our scheme to enhance the capabilities of an urban IoT network operating under the LoRaWAN standard. The simulation results show that the packet delivery ratio (PDR) is increased when our scheme is utilized in an unbalanced network and, consequently, the energy cost of data delivery is reduced. Furthermore, we demonstrate that better outcomes are attained when some techniques are combined, achieving a PDR improvement of up to about 50% and reducing the energy cost by nearly 20% in a multicell scenario with 5000 devices requesting downlink traffic.

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Energy Efficient Pico Cell Range Expansion and Density Joint Optimization for Heterogeneous Networks with eICIC

Cited by 8 publications

References 44 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

Power Allocation and User Assignment Scheme for beyond 5G Heterogeneous Networks

Machine Learning Aided Scheme for Load Balancing in Dense IoT Networks

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