Marketing

Algorithm

Engineering

Selection algorithm

Service (business)

Statistics

Theoretical computer science

Bipartite graph

Quality of experience

Quality of service

Telecommunications

Selection (genetic algorithm)

Computer science

Distributed computing

Business

Computer network

Operations research

Analytic hierarchy process

Heterogeneous network

Graph

Mathematics

Enhanced Data Rates for GSM Evolution

Blossom algorithm

Artificial intelligence

Matching (statistics)

Wireless

Machine learning

Wireless network

IEEE Transactions on Vehicular Technology

In the 5G ultra-dense wireless heterogeneous network system, it is a crucial issue to implement an effective network selection strategy to satisfy the demands of massive edge users and novel 5G services. In this paper, we model the network selection problem of edge users requesting different services as a bipartite graph, and propose a network selection algorithm based on weighted bipartite graph matching for 5G ultra-dense heterogeneous networks, named BGMNS. The proposed algorithm combines Analytic Hierarchy Process (AHP) and Grey Relation Analysis (GRA) to analyze the preferences of multiple services for different network attributes and obtain the Quality of Experience (QoE) of different edge users for each network. Moreover, in order to realize the fair allocation of network resources, we comprehensively consider the importance of the requested services and the obtained QoE by edge users to construct system fairness index. The BGMNS algorithm can optimize the overall QoE of users under the premise of ensuring the system fairness. Simulation results show that compared to the existing network selection algorithms, the proposed BGMNS algorithm can not only provide stable access to users when network status fluctuates randomly, but also effectively reduce user blocking probability as well as total packet loss rate, and significantly improve user average energy efficiency.

Adaptive Multi-Access Algorithm for Multi-Service Edge Users in 5G Ultra-Dense Heterogeneous Networks