A Robust Self-Optimization Algorithm Based on Idiosyncratic Adaptation of Handover Parameters for Mobility Management in LTE-A Heterogeneous Networks

Achhab, Tarek Al; Abboud, Fariz; Assalem, Abdulkarim

doi:10.1109/access.2021.3127326

Cited by 18 publications

(31 citation statements)

References 44 publications

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“…Here, the exiting methods are AHP-TOPSIS-Q-learning for handover optimization in LTE-A, 15 ATO for reducing frequent HO and HOF in LTE-A, 18 and PSO with adaptive neuro-fuzzy inference system (ANFIS) for LTE and LTE-A networks. 19 Figure 6, shows that the contrast of the HPP effect in relation to various moving UE speeds during eNB range. The result showed the superiority of the proposed AHP-ELECTRE-DR-learning over AHP-TOPSIS-Q-learning, ATO, and PSO-ANFIS methods, owing to the correct choices of triggering points using a sophisticated multicriteria system to use DRL learning methodology and to choose optimum eNB.…”

Section: Comparative Analysismentioning

confidence: 99%

“…Al Achhab et al 19 have presented the robust algorithm to reduce the number of PPHO, TLHO, and TEHO events to a minimum by an innovative mechanism that adaptively sets the HO control parameters (HCPs). This reduction was obtained without the need for unjustified techniques that assume certain thresholds for the ratio of PPHOs or the ratio of RLF HOs relative to the total number of HOs.…”

Section: Related Workmentioning

confidence: 99%

“…The proposed AHP‐ELECTRE‐DR‐learning approach is compared with exiting methods for identifying the efficiency of the proposed model. Here, the exiting methods are AHP‐TOPSIS‐Q‐learning for handover optimization in LTE‐A, 15 ATO for reducing frequent HO and HOF in LTE‐A, 18 and PSO with adaptive neuro‐fuzzy inference system (ANFIS) for LTE and LTE‐A networks 19 …”

Section: Experimental Analysismentioning

confidence: 99%

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An integrated AHP‐ELECTRE and deep reinforcement learning methods for handover performance optimization in an LTE‐A networks

Moses¹,

Ramkumarraja

2022

Trans Emerging Tel Tech

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Emblematic growth of telecommunications networks has also produced high data rates and low user equipment (UE) latency. Also, the third generation partnership project launched long term evolution (LTE) and LTE advance (A), which supports different types of network traffic including video, voice, and so on. A handover is needed to provide semantic interloper ability is the process to move the UE power from serving evolved node B (eNB) to the subsequent eNB without interruption. Consequently, high quality of service (QoS), reliability relies on a smooth execution. If eNBs cannot be selected from available eNBs in an optimized manner to transfer during UE mobility, the criteria for quality of service can be violated. In order to accommodate QoS requires to be more configured for smooth synchronization, eNB range, and trigger points. This article aims to automate handover by means of sophisticated program based on the analytical hierarchy process (AHP) with ELECTRE and deep reinforcement (DR) learning methods. Network performance is scrutinized and simulation is utilized to visualize the proposed scheme. The experiments demonstrated that the proposed system helps to reduce the handover failure rate and handover ping‐pong. Thus, the proposed AHP‐ELECTRE‐DR‐learning method has achieved better results in delay, throughput, fairness index, and packet loss than the existing works like AHP‐TOPSIS‐Q‐learning for handover optimization in LTE‐A, auto tuning optimization for reducing frequent handovers and handover failure in LTE‐A, and particle swarm optimization with adaptive neuro‐fuzzy inference system for LTE and LTE‐A networks, respectively.

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Section: Comparative Analysismentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Experimental Analysismentioning

confidence: 99%

See 1 more Smart Citation

An integrated AHP‐ELECTRE and deep reinforcement learning methods for handover performance optimization in an LTE‐A networks

Moses¹,

Ramkumarraja

2022

Trans Emerging Tel Tech

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“…The authors in [ 10 , 11 ] proposed several handover parameter optimization algorithms to overcome ping-pang problems, but only for the one-dimensional optimization of the hysteresis value (Hys). Furthermore, the authors in [ 12 ] proposed that Hys and TTT are not independent, and provided a functional relationship between Hys, TTT, and UE velocity, which can dynamically select reasonable handover parameters without the need for any predefined handover failure or ping-pang handover thresholds. The researchers in [ 13 ] simulated the effect of different handover parameter settings on 5G network performance at different speeds and proposed that medium handover parameter settings are the best solution.…”

Section: Introductionmentioning

confidence: 99%

A Reinforcement Learning Handover Parameter Adaptation Method Based on LSTM-Aided Digital Twin for UDN

Xiang

Wang

et al. 2023

Sensors

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Adaptation of handover parameters in ultra-dense networks has always been one of the key issues in optimizing network performance. Aiming at the optimization goal of effective handover ratio, this paper proposes a deep Q-learning (DQN) method that dynamically selects handover parameters according to wireless signal fading conditions. This approach seeks good backward compatibility. In order to enhance the efficiency and performance of the DQN method, Long Short Term Memory (LSTM) is used to build a digital twin and assist the DQN algorithm to achieve a more efficient search. Simulation experiments prove that the enhanced method has a faster convergence speed than the ordinary DQN method, and at the same time, achieves an average effective handover ratio increase of 2.7%. Moreover, in different wireless signal fading intervals, the method proposed in this paper has achieved better performance.

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“…HO is defined as the process of handing over the radio communication link from the serving base station (BS) to the target BS when the UE's received signal from the serving BS drops below the threshold level. The HO procedure will be more complicated in ultra-dense SBSs deployments which require efficient HO triggering algorithms to achieve optimal HO settings with minimal human intervention [10], [11]. Future cellular network generations (5G networks and beyond) will require advanced self-optimization techniques to avoid network degradation [12].…”

Section: Introductionmentioning

confidence: 99%

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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A Robust Self-Optimization Algorithm Based on Idiosyncratic Adaptation of Handover Parameters for Mobility Management in LTE-A Heterogeneous Networks

Cited by 18 publications

References 44 publications

An integrated AHP‐ELECTRE and deep reinforcement learning methods for handover performance optimization in an LTE‐A networks

An integrated AHP‐ELECTRE and deep reinforcement learning methods for handover performance optimization in an LTE‐A networks

A Reinforcement Learning Handover Parameter Adaptation Method Based on LSTM-Aided Digital Twin for UDN

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

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