User Association in 5G Networks: A Survey and an Outlook
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Abstract (arXiv)
The fifth generation (5G) mobile networks are envisioned to support the deluge of data traffic with reduced energy consumption and improved quality of service (QoS) provision. To this end, the key enabling technologies, such as heterogeneous networks (HetNets), massive multiple-input multiple-output (MIMO) and millimeter wave (mmWave) techniques, are identified to bring 5G to fruition. Regardless of the technology adopted, a user association mechanism is needed to determine whether a user is associated with a particular base station (BS) before the data transmission commences. User association plays a pivotal role in enhancing the load balancing, the spectrum efficiency and the energy efficiency of networks. The emerging 5G networks introduce numerous challenges and opportunities for the design of sophisticated user association mechanisms. Hence, substantial research efforts are dedicated to the issues of user association in HetNets, massive MIMO networks, mmWave networks and energy harvesting networks. We introduce a taxonomy as a framework for systematically studying the existing user association algorithms. Based on the proposed taxonomy, we then proceed to present an extensive overview of the state-of-the-art in user association conceived for HetNets, massive MIMO, mmWave and energy harvesting networks. Finally, we summarize the challenges as well as opportunities of user association in 5G and provide design guidelines and potential solutions for sophisticated user association mechanisms.
Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). First posted on arXiv 1 Sep 2015.
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Citing papers per year: 2021: 2; 2022: 0; 2023: 1; 2024: 3; 2025: 0; 2026: 1.
7 papers.
- 2024Large Language Model-Driven Curriculum Design for Mobile Networkscited by 8
- 2021Transferable and Distributed User Association Policies for 5G and Beyond Networkscited by 2
- 2021Reinforcement Learning Algorithm for Traffic Steering in Heterogeneous Networkcited by 2
- 2024Contextual Bandits with Non-Stationary Correlated Rewards for User Association in MmWave Vehicular Networkscited by 2
- 2023Privacy-Preserving Joint Edge Association and Power Optimization for the Internet of Vehicles via Federated Multi-Agent Reinforcement Learningcited by 1
- 2024Structured Reinforcement Learning for Delay-Optimal Data Transmission in Dense mmWave Networkscited by 0
- 2026FedQoS: Federated QoS-Risk Learning for Heterogeneous Indoor-Outdoor Access Selectioncited by 0
- 2026FedQoS: Federated QoS-Risk Learning for Heterogeneous Indoor-Outdoor Access Selection26 Aug 2026
- 2024Contextual Bandits with Non-Stationary Correlated Rewards for User Association in MmWave Vehicular Networks8 Oct 2024
- 2024Large Language Model-Driven Curriculum Design for Mobile Networks28 May 2024
- 2024Structured Reinforcement Learning for Delay-Optimal Data Transmission in Dense mmWave Networks25 Apr 2024
- 2023Privacy-Preserving Joint Edge Association and Power Optimization for the Internet of Vehicles via Federated Multi-Agent Reinforcement Learning26 Jan 2023
- 2021Reinforcement Learning Algorithm for Traffic Steering in Heterogeneous Network29 Nov 2021
- 2021Transferable and Distributed User Association Policies for 5G and Beyond Networks4 Jun 2021