Cascade Fuzzy Logic for Handover Optimization in Mobile Networks

Handover management plays a vital role in load balancing by strategically transferring users from overloaded base stations to less congested stations, ultimately optimizing network performance. This paper proposes a novel handover management solution that leverages a two-layer cascaded fuzzy logic c...

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Bibliographic Details
Published in2024 IEEE International Mediterranean Conference on Communications and Networking (MeditCom) pp. 293 - 298
Main Authors Gures, Emre, Becvar, Zdenek, Mach, Pavel
Format Conference Proceeding
LanguageEnglish
Published IEEE 08.07.2024
Subjects
Base stations
Decision making
Fuzzy logic
Handover
handover optimization
heterogeneous networks
Interference
load balancing
Load management
System performance
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Summary:Handover management plays a vital role in load balancing by strategically transferring users from overloaded base stations to less congested stations, ultimately optimizing network performance. This paper proposes a novel handover management solution that leverages a two-layer cascaded fuzzy logic controller (FLC) for enhanced load balancing efficiency. The first layer focuses on signal quality evaluation for both the serving and target base stations. It employs separate fuzzy inference systems that consider reference signal received power (RSRP) and signal-to-interference-plus-noise ratio (SINR) to assess overall signal quality. This information is then fed into the second layer. Here, the FLC analyzes four key inputs: load levels of both the serving and target base stations, alongside the signal quality for each (obtained from the first layer's output). By employing a hierarchical architecture, the cascaded FLC significantly reduces the number of fuzzy rules required for decision-making, leading to faster processing and improved system performance. Simulations indicate the proposed FLC solution efficiently associates 80% of users with less congested stations (below 50% load level), ultimately increasing network capacity by up to 51.39% compared to competitive algorithms.
DOI:10.1109/MeditCom61057.2024.10621353