Handover optimization scheme for LTE-Advance networks based on AHP-TOPSIS and Q-learning

Goyal, Tanu; Kaushal, Sakshi

doi:10.1016/j.comcom.2018.10.011

Cited by 61 publications

(50 citation statements)

References 15 publications

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“…The simulation results showed that the proposed approach can effectively reduce the number of frequent HOs and RLFs and improve the mean UE's throughput. In another work [21], the authors utilized the analytic hierarchy process-TOPSIS technique to introduce an intelligent scheme for optimal eNB selection. In addition, the Q-learning approach was adopted to optimize HCPs after selecting the optimal eNB.…”

Section: Related Workmentioning

confidence: 99%

Auto Tuning Self-Optimization Algorithm for Mobility Management in LTE-A and 5G HetNets

et al. 2020

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Ultra-dense networks represent the trend for future wireless 5G networks, which can provide high transmission rates in dense urban environments. However, a massive number of small cells are required to be deployed in such networks, and this requirement increases interference and number of handovers (HOs) in heterogeneous networks (HetNets). In such scenario, mobility management becomes an important issue to guarantee seamless communication while the user moves among cells. In this paper, we propose an autotuning optimization (ATO) algorithm that utilizes user speed and received signal reference power to adapt HO margin and time to trigger. The proposed algorithm aims to reduce the number of frequent HOs and HO failure (HOF) ratio. The performance of the proposed algorithm is evaluated through simulation with a two-tier model that consists of 4G and 5G networks. Simulation results show that the average rates of pingpong HOs and HOF are significantly reduced by the proposed algorithm compared with other algorithms from the literature. In addition, the ATO algorithm achieves a low call drop rate and reduces HO delay and interruption time during user mobility in HetNets. INDEX TERMS Ultra-dense, heterogeneous networks, handover, self-optimization.

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Section: Related Workmentioning

confidence: 99%

Auto Tuning Self-Optimization Algorithm for Mobility Management in LTE-A and 5G HetNets

et al. 2020

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“…In [17], the focus is on finding optimal triggering points such as time-to-trigger and hysteresis so that HOFs that are too early and too late, and the HPP effect can be minimized. For this purpose, the AHP and TOPSIS methods are used when considering the RSRP, RSRQ, eNodeB resources blocks (RB), SINR, and UE's movement and location.…”

Section: Related Workmentioning

confidence: 99%

A Novel Heuristic for Handover Priority in Mobile Heterogeneous Networks

et al. 2020

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Handover (HO) is designed to facilitate user mobility and ensure quality of service in mobile networks. In multiple base station (eNodeBs) scenarios, the HO priority process is a problem that has been studied in many surveys, as neglecting the use of priority-based schemes can result in high amounts of HO and, consequently, a decrease in the quality of services provided. This paper presents a Heuristic for Handover based on AHP-TOPSIS-FUZZY (H 2 ATF), which generates a priority ranking of eNodeBs from the use of (a) the analytical hierarchical process (AHP) to define the weights of the criteria; (b) the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to rank the selected target cells; and (c) the use of an adaptive hysteresis calculated through a fuzzy inference system based on parameters that directly impact the HO process. Through this proposal, it was possible to define the best time and, together, the best antenna to perform the HO. The results demonstrate a decrease of up to 43% in HO ping pong (HPP), a widely used metric in the literature to evaluate HO heuristics. INDEX TERMS Handover, priority, heterogeneous networks, mobile networks, AHP-TOPSIS, fuzzy logic.

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“…TOPSIS is favoured due to its robustness and reliability, as discussed in surveys [14]- [16]. The TOPSIS approach was first developed by Hwang and Yoon [17] and is widely used for target BS selection, as reported in [18]- [22]. References [18] and [19] proposed a MADM based handover scheme to eliminate unnecessary handover in LTE-Advance and UDN.…”

Section: Introductionmentioning

confidence: 99%

“…The TOPSIS approach was first developed by Hwang and Yoon [17] and is widely used for target BS selection, as reported in [18]- [22]. References [18] and [19] proposed a MADM based handover scheme to eliminate unnecessary handover in LTE-Advance and UDN. The proposed scheme first applies a subjective weighting approach -analytical hierarchy process mechanism to prioritise the criteria for obtaining the weight.…”

Section: Introductionmentioning

confidence: 99%

“…Simulation results in [18]- [22] shows that TOPSIS approach can effectivity deal with multiple handover related criteria and select an optimal handover target, which can significantly reduce ping-pong effect, number of handover failures, and enhancing user throughput. However, conventional MADM approach cannot deal directly with any form of imprecise data [23].…”

Section: Introductionmentioning

confidence: 99%

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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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Handover optimization scheme for LTE-Advance networks based on AHP-TOPSIS and Q-learning

Cited by 61 publications

References 15 publications

Auto Tuning Self-Optimization Algorithm for Mobility Management in LTE-A and 5G HetNets

Auto Tuning Self-Optimization Algorithm for Mobility Management in LTE-A and 5G HetNets

A Novel Heuristic for Handover Priority in Mobile Heterogeneous Networks

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

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