Mobility-aware load Balancing for Reliable Self-Organization Networks: Multi-agent Deep Reinforcement Learning

Mohajer, Amin; Bavaghar, Maryam; Farrokhi, Hamid

doi:10.1016/j.ress.2020.107056

Cited by 30 publications

(9 citation statements)

References 21 publications

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“…One of the objectives is to observe multiple HO events with associated parameters, use this information to train its machine learning model, and try to identify sets of parameters that lead to successful HOs and sets of parameters that lead to unintended events. Some recent literatures have shown that machine learning‐based mobility robustness optimization can improve the user satisfaction rate and increase network performance significantly by optimizing HO parameters [51,52]. We anticipate that the mobility optimization enhanced by the use of machine learning can find the optimal HO parameters to maximize the MASE solving the various complex trade‐offs.…”

Section: Discussionmentioning

confidence: 94%

ZEUS: Handover algorithm for 5G to achieve zero handover failure

et al. 2021

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In 5G, the required target for interruption time during a handover (HO) is 0 ms. However, when a handover failure (HOF) occurs, the interruption time increases significantly to more than hundreds of milliseconds. Therefore, to fulfill the requirement in as many scenarios as possible, we need to minimize HOF rate as close to zero as possible. 3GPP has recently introduced conditional HO (CHO) to improve mobility robustness. In this study, we propose "ZEro handover failure with Unforced and automatic time-to-execute Scaling" (ZEUS) algorithm to optimize HO parameters easily in the CHO. Analysis and simulation results demonstrate that ZEUS can achieve a zero HOF rate without increasing the ping-pong rate. These two metrics are typically used to assess an HO algorithm because there is a tradeoff between them. With the introduction of the CHO, which solves the tradeoff, only these two metrics are insufficient anymore. Therefore, to evaluate the optimality of an HO algorithm, we define a new integrated HO performance metric, mobility-aware average effective spectral efficiency (MASE). The simulation results show that ZEUS provides higher MASE than LTE and other CHO variants.

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Section: Discussionmentioning

confidence: 94%

ZEUS: Handover algorithm for 5G to achieve zero handover failure

et al. 2021

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“…• In 2020 [68], a DRL-based MRO scheme was proposed to learn the optimum parameter values used to describe the mobility patterns of cells. The optimal mobility setting for HO parameters depend on the UE distribution and their velocity.…”

Section: B Related Studies Based On MLmentioning

confidence: 99%

Machine Learning-Based Load Balancing Algorithms in Future Heterogeneous Networks: A Survey

et al. 2022

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The massive growth of mobile users and the essential need for high communication service quality necessitate the deployment of ultra-dense heterogeneous networks (HetNets) consisting of macro, micro, pico and femto cells. Each cell type provides different cell coverage and distinct system capacity in HetNets. This leads to the pressing need to balance loads between cells, especially with the random distribution of users in numerous mobility directions. This paper provides a survey on the intelligent load balancing models that have been developed in HetNets, including those based on the machine learning (ML) technology. The survey provides a guideline and a roadmap for developing cost-effective, flexible and intelligent load balancing models in future HetNets. An overview of the generic problem of load balancing is also presented. The concept of load balancing is first introduced, and its purpose, functionality and evaluation criteria are then explained. Besides, a basic load balancing model and its operational procedure are described. A comprehensive literature review is then conducted, including techniques and solutions of addressing the load balancing problem. The key performance indicators (KPIs) used in the evaluation of load balancing models in HetNets are presented, along with the concurrent optimisation of coverage (CCO) and mobility robustness optimisation (MRO) relationship of load balancing. A comprehensive literature review of ML-driven load balancing solutions is specifically accomplished to show the historical development of load balancing models. Finally, the current challenges in implementing these models are explained as well as the future operational aspects of load balancing.

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“…Some recent applications include reliable handover in cellular network operations for mobility robustness optimization (MRO). This is a challenging problem for traditional rulebased methods, the objectives of which are to minimize the number of dropped calls/unsatisfied customers, increase each cell throughput, and ensure a more balanced network using cell load-sharing [140]. The authors developed a Deep Reinforcement Learning (DRL) solution that outperformed (on user QoS) and required fewer parameters to tune than traditional methods for reliably handling wireless user handover across cells.…”

Section: Reinforcement Learning Applicationsmentioning

confidence: 99%