New Network Selection Algorithm Based on Cosine Similarity Distance and PSO in Heterogeneous Wireless Networks

Abstract

Future wireless communication networks will be composed of different technologies with complementary characteristics. Thus, vertical handover (VHO) must support seamless mobility in such heterogeneous environments. The network selection is an important phase in the VHO process and it can be formulated as a multiattribute decision-making problem. So, the mobile terminal equipped with multiple interfaces will be able to choose the most suitable network. This work proposes an access network selection algorithm, based on cosine similarity distance, subjective weights using Fuzzy ANP, and objective weights using particle swarm optimization. The comprehensive weights are based on the cosine similarity distance between the networks and the ideal network. Finally, the candidate network with the minimum cosine distance to the ideal network will be selected in the VHO network selection stage. The performance analysis shows that our proposed method, based on cosine similarity distance and combination weights, reduces the ranking abnormality and number of handoffs in comparison with other MADM methods in the literature.

Ant colony optimization-based solution to optimize load balancing and throughput for 5G and beyond heterogeneous networks

“…Given the evolving landscape of large-scale, heterogeneous, dynamic, and intricate scenarios in HetNets, numerous studies have been undertaken to assess the efficacy of computational intelligence techniques in enhancing the operational performance of Het-Nets [21]. References [21,22] reviewed various AI-driven methodologies to foster the evolution of more intelligent HetNet infrastructure and systems. This review particularly addressed the research related to self-configuration and self-optimization, while providing a balanced analysis of the advantages and drawbacks inherent in each of these associated schemes.…”