Distributed self optimization techniques for heterogeneous network environments using active antenna tilt systems

Qureshi, Muhammad Nauman; Tiwana, Moazzam Islam; Haddad, Majed

doi:10.1007/s11235-018-0494-5

Cited by 7 publications

(2 citation statements)

References 22 publications

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“…The methods used for comparison include a Generic vertical handover management scheme (Khan et al, 2017), ATO (Alhammadi et al, 2019a(Alhammadi et al, , 2019b, APA (Saleh et al, 2019) and SCLA (Qureshi et al, 2019). Technique for vertical handover management…”

Section: Competing Methodsmentioning

confidence: 99%

“…The method solved the handover issues in PCS networks by predicting the future movement of mobile users, but the complex dispersed streaming problems may occur. Qureshi et al (2019) developed stochastic cellular learning automata (SCLA) technique for the HetNets by considering active antenna tilt systems. The method quickly processed and adapts to the environment, the inter-cell interference can be brought to a minimum and the interference is controlled, but failed to include artificial neural networks that has inherent characteristics for solving dimensionality decrease.…”

Section: Literature Surveymentioning

confidence: 99%

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5G heterogeneous network (HetNets): a self-optimization technique for vertical handover management

Kiran

Rao

2021

IJPCC

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Purpose Vertical handover has been grown rapidly due to the mobility model improvements. These improvements are limited to certain circumstances and do not provide the support in the generic mobility, but offering vertical handover management in HetNets is very crucial and challenging. Therefore, this paper presents a vertical handoff management method using the effective network identification method. Design/methodology/approach This paper presents a vertical handoff management method using the effective network identification method. The handover triggering schemes are initially modeled to find the suitable position for starting handover using computed coverage area of the WLAN access point or cellular base station. Consequently, inappropriate networks are removed to determine the optimal network for performing the handover process. Accordingly, the network identification approach is introduced based on an adaptive particle-based Sailfish optimizer (APBSO). The APBSO is newly designed by incorporating self-adaptive particle swarm optimization (APSO) in Sailfish optimizer (SFO) and hence, modifying the update rule of the APBSO algorithm based on the location of the solutions in the past iterations. Also, the proposed APBSO is utilized for training deep-stacked autoencoder to choose the optimal weights. Several parameters, like end to end (E2E) delay, jitter, signal-to-interference-plus-noise ratio (SINR), packet loss, handover probability (HOP) are considered to find the best network. Findings The developed APBSO-based deep stacked autoencoder outperformed than other methods with a minimal delay of 11.37 ms, minimal HOP of 0.312, maximal stay time of 7.793 s and maximal throughput of 12.726 Mbps, respectively. Originality/value The network identification approach is introduced based on an APBSO. The APBSO is newly designed by incorporating self-APSO in SFO and hence, modifying the update rule of the APBSO algorithm based on the location of the solutions in the past iterations. Also, the proposed APBSO is used for training deep-stacked autoencoder to choose the optimal weights. Several parameters, like E2E delay, jitter, SINR, packet loss and HOP are considered to find the best network. The developed APBSO-based deep stacked autoencoder outperformed than other methods with minimal delay minimal HOP, maximal stay time and maximal throughput.

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