A Service-Centric Q-Learning Algorithm for Mobility Robustness Optimization in LTE

Marí-Altozano, María Luisa; Mwanje, Stephen S.; Ramírez, Salvador Luna; Toril, M.; Sanneck, Henning; Gijón, Carolina

doi:10.1109/tnsm.2021.3073244

Cited by 19 publications

(14 citation statements)

References 38 publications

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“…Operators have been pushed by these developments to re-focus their attention from network performance to end user opinion (i.e., quality of experience (QoE)). For this reason, María et al proposed QoE-aware Q-learning algorithm for the MRO function to reduce the ratio of HOPP and RLF [39]. Several facilities were examined such as video streaming, web browsing, file download service, and voice over internet protocol.…”

Section: Mro Using Reinforcement Learningmentioning

confidence: 99%

“…Based on the algorithms addressed in this study, HCPs of the MRO were optimized under a several network topologies. These topologies includes 5G network [8], [41], [48], [89], HetNet [23], [30], [38], [56], [59], [60], [71], [75], [82], LTE-network [14], [29], [32], [33], [34], [39], [70], [74], [76], [77]. Moreover, several network topologies are addressed in Tables 2, 3.…”

Section: A Velocity-aware and Topologies In Future Mobile Networkmentioning

confidence: 99%

“…The fuzzy Q-learning technique was addressed in [14], [29], [30], and [33]. The research of [31], [32], [35], [38], [39], [40] also presented the Q-learning technique for obtaining ideal HCP settings. Other techniques (i.e., AHP-TOPSIS, subtractive clustering, subtractive clustering with FLC, and TOPSIS deep learning) integrated with Q-learning were also addressed in [34], [36], [37], [42].…”

Section: Reinforcement Learning For Mromentioning

confidence: 99%

“…The common types of ML have been implemented to optimize the HCPs of MRO functions, such as the supervised ML [21], [22], [23], [24], [25], [26], [27], unsupervised ML [28], and reinforcement learning [14], [29], [30], [31], [32], [33], [34], [35], [36], [37], [38], [39], [40], [41], [42]. Several ML techniques under each type have been addressed as a solution method.…”

Section: Introductionmentioning

confidence: 99%

“…The fuzzy Q-learning technique was used throughout numerous works [14], [29], [30], [33]. The research of [31], [32], [35], [38], [39], and [40] presented a Q-learning technique to acquire optimal HCP settings. The Q-learning technique was integrated with other methods such as Q-learning and Analytic hierarchy process technique for order of preference by similarity to ideal solution (AHP-TOPSIS) [34], deep Q-learning [42].…”

Section: Introductionmentioning

confidence: 99%

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Advanced Mobility Robustness Optimization Models in Future Mobile Networks Based on Machine Learning Solutions

et al. 2022

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Ultra-dense heterogeneous networks (HetNets) are deployment scenarios in the advent of fifth generation (5G) and beyond network generations. A massive number of small base stations (SBSs) and connected devices have been exponentially increasing. This has subsequently led to a rise of several mobility management issues which require optimization techniques to avoid performance degradation. Machine learning (ML) is a promising approach for future mobile communication networks (5G and beyond). It has the ability of improving the efficiency of complicated heterogeneous and decentralized networks. ML has proven to be significant in the mobility management field since it optimizes handover control parameters (HCPs) over various dynamic environments. To the best of the authors' knowledge, no comprehensive survey deeply discussing a state-of-the-art ML algorithms in mobility robustness optimization (MRO) functions. However, each summarized algorithm in this study includes deployment scenario, ML type, methodology used, criteria, HCPs, key performance indicators (KPIs), simulators, and achievements which can assist researchers for future investigations in MRO functions. In addition, this study serves as a guide in the selection of proper optimization algorithms according to the outcomes of each algorithm. Furthermore, this study presented the common types of ML and the techniques used from each type to optimize the HCPs of the MRO functions. Moreover, high-mobility-aware and network topologies are presented in MRO function for further system enhancements. Besides, the survey further highlights several potential problems for upcoming research and provides future directions to address the issues of next generation wireless networks.

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Section: Mro Using Reinforcement Learningmentioning

confidence: 99%