Handover and load balancing self-optimization models in 5G mobile networks

Saad, Wasan Kadhim; Shayea, Ibraheem; Alhammadi, Abdulraqeb; Sheikh, Muntasir; El‐Saleh, Ayman A.

doi:10.1016/j.jestch.2023.101418

Cited by 10 publications

(4 citation statements)

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“…In 5G/6G networks, handover decision techniques are critical in identifying when and which cell, a user should be handed over to provide uninterrupted and high-quality communication [115]. While specific techniques for handover decision-making in future networks are still in the development process, here are some common techniques that can be used for algorithmic design in 5G/6G, i.e., the Received signal strength (RSS) [116], Signal-to-interference-plusnoise ratio (SINR) [117], Quality of Service (QoS) [118] and Hybrid Approach based on SINR, QoS requirements for load balancing to determine the most suitable target cell for handover [119].…”

Section: Mobility and Handover In Ai-ran 6gmentioning

confidence: 99%

AI-RAN in 6G Networks: State-of-the-Art and Challenges

Khan,

Schmid

2024

IEEE Open J. Commun. Soc.

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6G is a next-generation cellular communication technology that builds up on existing 5G networks which are currently rolled out worldwide. Through incorporation of artificial intelligence (AI) and machine learning (ML), the core 5G network is advanced into an intelligent 6G network. The 6G Artificial Intelligence Radio Access Network (AI-RAN) is anticipated to offer advanced features like reduced latency, improved bandwidth, data rates and coverage. Furthermore, AI-RAN is expected to support complex use cases such as extreme connectivity, multi-user communications and dynamic spectrum access. This paper provides a detailed survey and thorough assessment of AI-RAN's vision and state-of-the-art challenges. We first present a concise introduction to 6G AI-RAN followed by background information on the current 5G RAN and its challenges that must be overcome to implement 6G AI-RAN. The paper then examines trending research issues in AI-RAN, i.e., challenges related to spectrum allocation, network architecture, and resource management. We discuss the methods to overcome these challenges which include the adoption of advanced machine learning and edge computing technologies to boost the performance of 6G AI-RAN. We conclude by stating open research directions.

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Section: Mobility and Handover In Ai-ran 6gmentioning

confidence: 99%

AI-RAN in 6G Networks: State-of-the-Art and Challenges

Khan,

Schmid

2024

IEEE Open J. Commun. Soc.

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“…Previous works, as in [10], [21], [22] choose the target cell based on the reference signal received power of the serving cell and the handover control parameters (HCPs), i.e., handover margin (HM) and time to trigger (TTT), that are presented in (7):…”

Section: Choosing the Target Cell Based On Adaptive Handover Control ...mentioning

confidence: 99%

Adaptive handover control parameters based on cell load capacity in a B5G/6G heterogeneous network

Mohamed,

Elbadawy,

Ammar

2024

TELKOMNIKA

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In the B5G/6G network, the deployment of small cells increased to keep up with the growth of mobile traffic. This deployment will increase the number of handovers (HOs) between cells. Ping-pong handover (PPHO) and radio link failure (RLF) are considered the two major problems in HO that may occur. So, the challenge is to set the handover control parameters (HCPs) carefully to find out the proper HO decision that should be appropriate to the environmental constraints. Therefore, in our paper, we propose an adaptive HCPs algorithm that adapts to environmental constraints. In addition, the proposed algorithm will have immunity to RLF and will significantly minimize the amount of PPHO compared to other workers. In the simulation results, our proposed model is evaluated using two frequency plans. By using frequency plan 1, the user mean throughput increased from 270 Kbps to 281 Kbps when the serving cell was fully loaded. By using frequency plan 2, the user mean throughput increased from 6 Mbps to 20 Mbps when the serving cell was fully loaded. In addition, the amount of ping-pong handover between overlapped small cells decreased and will not exceed one PPHO compared to another literature model.

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“…Recent research has focused on optimizing HO control parameters appropriately for efficiently address HO issues during user mobility [9][10][11]. In [9], the authors propose a fuzzy-coordinated, self-optimizing HO scheme to achieve a seamless HO while users move in multi-radio access networks.…”

Section: Introductionmentioning

confidence: 99%

“…The load balancing optimization function adaptively adjusts the settings of HO control parameter to achieve balance uneven loads between adjacent cells. In [10], the authors have focused on analyzing the performance of load balancing self-optimization within 5G cellular networks. The conflict resolution technique is introduced in the self-optimization network, which is responsible for addressing contraction between mobility robustness optimization and load balancing optimization [11].…”

Section: Introductionmentioning

confidence: 99%