An adaptive fuzzy handover triggering approach for Long‐Term Evolution network

Kwong, Chiew Foong; Chuah, Teong Chee; Tan, Su Wei; Akbari-Moghanjoughi, Ayyoub

doi:10.1111/exsy.12120

Cited by 11 publications

(6 citation statements)

References 17 publications

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“…Compared with conventional fuzzy logic-based handover triggering mechanisms and other traditional approaches, the proposed algorithm in Reference [20] demonstrated that it provided a significant improvement in handover performance in terms of mobility robustness and mobility load balancing.…”

Section: Intelligent Handover Triggering Mechanismsmentioning

confidence: 99%

“…However, the approach in Reference [20] was unable to process too many metrics as input parameters; otherwise, the whole system becomes complicated and the training process is time-consuming. In Reference [21], the authors adopted model-free asynchronous advantage actor-critic (A3C) reinforcement learning techniques to learn an optimal handover method.…”

Section: Intelligent Handover Triggering Mechanismsmentioning

confidence: 99%

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Intelligent Handover Triggering Mechanism in 5G Ultra-Dense Networks Via Clustering-Based Reinforcement Learning

et al. 2021

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Ultra-dense networks (UDNs) are considered as key 5G technologies. They provide mobile users a high transmission rate and efficient radio resource management. However, UDNs lead to the dense deployment of small base stations (BSs) that can cause stronger interference and subsequently increase the handover management complexity. At present, the conventional handover triggering mechanism of user equipment (UE) is only designed for macro mobility and thus could result in negative effects such as frequent handovers, ping-pong handovers, and handover failures on the handover process of UE at UDNs. These effects degrade the overall network performance. In addition, a massive number of BSs significantly increase the network maintenance system workload. To address these issues, this paper proposes an intelligent handover triggering mechanism for UE based on Q-learning frameworks and subtractive clustering techniques. The input metrics are first converted to state vectors by subtractive clustering, which can improve the efficiency and effectiveness of the training process.Afterward, the Q-learning framework learns the optimal handover triggering policy from the environment. The trained Q table is deployed to UE to trigger the handover process. The simulation results demonstrate that the proposed method can ensure the stronger mobility robustness of UE that is improved by 60%-90% compared to the conventional approach with respect to the number of handovers, ping-ping handover rate, and handover failure rate while maintaining other key performance indicators (KPIs), that is, a relatively high level of throughput and network latency. In addition, through integration with subtractive clustering, the proposed mechanism is further improved by an average of 20% in terms of all the evaluated KPIs.

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Section: Intelligent Handover Triggering Mechanismsmentioning

confidence: 99%

Section: Intelligent Handover Triggering Mechanismsmentioning

confidence: 99%

Intelligent Handover Triggering Mechanism in 5G Ultra-Dense Networks Via Clustering-Based Reinforcement Learning

et al. 2021

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“…The fuzzy logic is used to initial HO and utility functions are then applied to select the optimal access networks. Paper [9] [10] integrates artificial neuro networks into the fuzzy logic system. In this way, the fuzzy membership functions can dynamically self-adjust based on the changes of environment, which could also improve the system efficiency by reducing human intervention.…”

Section: Related Workmentioning

confidence: 99%

“…Afterwards, the normalised fuzzy decision matrix DM will multiply the fuzzy weight array W to obtain weighted normalised fuzzy decision matrix ̃ as, (9) Based on this normalised fuzzy decision matrix, the fuzzy positive ideal solution ( * ) and fuzzy negative ideal solution ( − ) are calculated by (10) (11),…”

Section: System Modelmentioning

confidence: 99%

Fuzzy-TOPSIS Based Optimal Handover Decision-making Algorithm for Fifth-generation of Mobile Communications System

Liu¹,

Kwong²,

Zhang³

et al. 2019

jcm

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With the increasing demand for higher bandwidth and data rate of the mobile user. There are massive base stations (BS) will be deployed in the future wireless environment. Several issues could be raised dues to dense deployment of BSs, i.e. handover (HO) ping-pong effect, unnecessary HO and frequent HO. To avoid these effects, the handover decision-making strategies become extremely important to select the optimal BS among all detected BS and ensure QoS for each mobile user. In this paper, the author develops a fuzzy-TOPSIS based HO algorithm to minimise the ping-pong effect and number of HO. The proposed algorithm integrates both advantages of fuzzy logic and TOPSIS. The received signal strength intensity (RSSI) and signal to noise ratio (SNR) are considered as HO criteria in this approach. For the simulation result, the proposed HO algorithm can reduce ping-pong rate and a number of HO effectivity by comparing to conventional RSSI-based HO approach and classical multi-attribute decision making (MADM) HO method, i.e. simple additive weighting (SAW) and TOPSIS.

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“…Reference [13] shows the adaptive handover triggering mechanisms by combining fuzzy logic with neuro networks. In this approach, the subtractive clustering is used to initialise membership functions, which is tuned by using artificial neuro network corresponds to the changes in the mobile environment.…”

Section: Introductionmentioning

confidence: 99%

A fuzzy-clustering based approach for MADM handover in 5G ultra-dense networks

et al. 2019

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As the global data traffic has significantly increased in the recent year, the ultra-dense deployment of cellular networks (UDN) is being proposed as one of the key technologies in the fifthgeneration mobile communications system (5G) to provide a much higher density of radio resource. The densification of small base stations (BSs) could introduce much higher inter-cell interference and lead user to meet the edge of coverage more frequently. As the current handover scheme was originally proposed for macro BS, it could cause serious handover issues in UDN i.e. ping-pong handover, handover failures and frequent handover. In order to address these handover challenges and provide a high quality of service (QoS) to the user in UDN. This paper proposed a novel handover scheme, which integrates both advantages of fuzzy logic and multiple attributes decision algorithms (MADM) to ensure handover process be triggered at the right time and connection be switched to the optimal neighbouring BS. To further enhance the performance of the proposed scheme, this paper also adopts the subtractive clustering technique by using historical data to define the optimal membership functions within the fuzzy system.Performance results show that the proposed handover scheme outperforms traditional approaches and can significantly minimise the number of handovers and the ping-pong handover while maintaining QoS at a relatively high level.

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An adaptive fuzzy handover triggering approach for Long‐Term Evolution network

Cited by 11 publications

References 17 publications

Intelligent Handover Triggering Mechanism in 5G Ultra-Dense Networks Via Clustering-Based Reinforcement Learning

Intelligent Handover Triggering Mechanism in 5G Ultra-Dense Networks Via Clustering-Based Reinforcement Learning

Fuzzy-TOPSIS Based Optimal Handover Decision-making Algorithm for Fifth-generation of Mobile Communications System

A fuzzy-clustering based approach for MADM handover in 5G ultra-dense networks

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