Handover Parameters Optimisation Techniques in 5G Networks

Saad, Wasan Kadhim; Shayea, Ibraheem; Hamza, Bashar J.; Mohamad, Hafizal; Daradkeh, Yousef Ibrahim; Jabbar, Waheb A.

doi:10.3390/s21155202

Cited by 44 publications

(16 citation statements)

References 56 publications

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“…This will reduce human interference in network management and optimisation tasks. Several studies have focused on HO optimisation by introducing schemes and algorithms for enhancing network performance [1,11,13,14,33,38,40,43,[49][50][51][52][53].…”

Section: Relevant Studiesmentioning

confidence: 99%

“…The HPPP can be mathematically expressed where if a call is delivered to a new cell and returned to the cell of source in less than the critical time (Tc), the resulting HO is considered to be HPPP. Thus, the HPPP ratio represents the number of pingpong handovers (M PPH ) divided by the total number of HOs; i.e., the M PPH , the number of HOs where no pingpong occurs (M NPPH ) and the number of Handover Failures (M HOF ), given as follows [49,64]:…”

Section: B Hpppmentioning

confidence: 99%

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Performance Evaluation of Mobility Robustness Optimization (MRO) in 5G Network With Various Mobility Speed Scenarios

et al. 2022

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The massive deployment of small-sized cells for the Fifth Generation (5G) mobile network will increase the Handover Probability (HOP), potentially causing higher Handover Ping-Pong Probability (HPPP) and/or Radio Link Failure (RLF). Inappropriate usage of Handover Control Parameter (HCP) settings may further exasperate this issue. Therefore, Mobility Robustness Optimisation (MRO) has been introduced and further developed as a significant Self-Optimisation Network (SON) function in the 5G network and beyond. The main aim of MRO is to address Mobility Management (MM) issues during user mobility between cells to ensure a smooth connection. Although various algorithms were suggested in the literature, they mostly cater to 4G networks which may not be effective for the 5G network due to different network characterisations. This paper analyses the performance of various MRO algorithms with various system settings and scenarios for the 5G network. The investigated algorithms from the literature include the Distance (Dis), Cost Function (CF), Fuzzy Logic Controller (FLC) and Handover Performance Indicator (HPI). Validation has been accomplished for different mobility conditions in the 5G network. A simulation based on the MATLAB software has been conducted using various system tools. The evaluation analysis is in terms of Signal to-Interference-plus-Noise-Ratio (SINR), HPPP and RLF effects since these are major indicators in assessing system performance and selecting the handover decision during user mobility. The simulation outcomes show that the HPI algorithm performance is more reactive to mobile speed scenarios over time, significantly reducing the HPPP compared to the other algorithms which do not provide large reactions in the same conditions. Simultaneously, the HPI algorithm exhibits the highest RLF and SINR from among the other algorithms. The distance algorithm is the best in terms of RLF and SINR, achieving an acceptable level in terms of HPPP. These results point to that the MRO algorithm that operate based on distance is the most robust compared to the other investigated algorithms, confirming the potential of the Dis approach for the 5G network.

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Section: Relevant Studiesmentioning

confidence: 99%

Section: B Hpppmentioning

confidence: 99%

Performance Evaluation of Mobility Robustness Optimization (MRO) in 5G Network With Various Mobility Speed Scenarios

et al. 2022

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“…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. However, this paper analyzed fixed handover parameter settings so the algorithm could not optimize parameters automatically.…”

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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“…MRO manages HO issues according to UE movements, while LBO focuses on traffic load balancing [15]. The MRO basically auto-tunes HCPs based on the network status to control irregular HO triggering [16], [17]. MRO functions can be achieved by preserving the connection quality and utilizing network resources.…”

Section: Introductionmentioning

confidence: 99%

“…ML also applies statistical techniques to enhance the ML process without being explicitly programmed [20]. Therefore, ML techniques are suitable for solving various HO self-optimization issues in heterogeneous networks (HetNets) [17].…”

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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Handover Parameters Optimisation Techniques in 5G Networks

Cited by 44 publications

References 56 publications

Performance Evaluation of Mobility Robustness Optimization (MRO) in 5G Network With Various Mobility Speed Scenarios

Performance Evaluation of Mobility Robustness Optimization (MRO) in 5G Network With Various Mobility Speed Scenarios

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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