To associate or not to associate? A user-based threshold scheme for 5G and beyond networks

Research output: Contribution to journalArticlepeer-review

Abstract

Traditionally, User-cell Association (UA) schemes for cellular networks are based solely on the quality and strength of the received signal. This mechanism may not always be adequate for the complex nature of fifth generation and beyond (B5G) networks, and may lead to load biasing. Additional parameters, such as the load on the neighboring cells, the needs of the user equipment (UE), and the UE mobility should be considered as well. The realization that relying solely on the signal strength and quality for UA may violate load balancing has been recognized for a long time. However, the approach taken was to balance the load by a network-dependent mechanism. Yet, the UA mechanism remains based on the best-received signal. The underlying assumption that all the users have the same needs remains. Load-aware UA was considered only upon mobility-driven handover to small cells. This study suggests a UE-based threshold scheme for load-aware UA that is suitable for B5G networks. Since the optimal UA problem is known to be NP-hard, we suggest a heuristic mechanism, that is sufficiently simple and reliable to be implemented in practice, yet sufficiently efficient to significantly outperform the existing UA mechanism. Simulation results demonstrated that the suggested UA scheme can potentially reduce the required network bandwidth resources by up to 25% and significantly reduce the service blocking probability and the average waiting time for service.

Original languageEnglish
Pages (from-to)390-401
Number of pages12
JournalICT Express
Volume11
Issue number3
DOIs
StatePublished - Jun 2025

Bibliographical note

Publisher Copyright:
© 2025 The Author

Keywords

  • 5G and beyond networks
  • Cellular networks
  • IoT
  • Massive network connectivity
  • User-cell association

ASJC Scopus subject areas

  • Software
  • Information Systems
  • Hardware and Architecture
  • Computer Networks and Communications
  • Artificial Intelligence

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