Review of Resource Allocation Strategies for Handoff in 5G Mobile Communication System

Machine learning-based handoff management has emerged as a promising approach to improve handover performance and quality of service in 5G networks. This approach involves using machine learning algorithms and models to automate handover prediction, decision-making, and resource allocation. Machine learning models can be trained on historical network data to predict when and where a handover may occur, which can help trigger proactive handovers or allocate resources in anticipation of a handover. By analysing current network conditions and user preferences, machine learning algorithms can make intelligent decisions on whether to perform a handover or not, and which target cell or network to handover to. Additionally, machine learning models can be used to optimize the allocation of network resources, such as bandwidth and power, for handover management, which can improve network capacity and reduce energy consumption. However, there are also some challenges associated with machine learning-based handoff management. The availability and quality of data can affect the accuracy of the models, and the complexity of the models may make it difficult to identify the root cause of handover problems or to debug the models. In summary, machine learning-based handoff management has the potential to improve handover performance and quality of service in 5G networks. This paper presents a review of ML based resource allocation strategies for Handoff in 5G Mobile Communication System along with a proposed ML based model. By leveraging the power of machine learning algorithms and models, handover prediction, decision-making, and resource allocation can be optimized to improve network performance, capacity, and efficiency