A Review on Machine Learning Assisted Handover Mechanisms for Future Generation Wireless Networks/strong>
Authors:-Priyanka Vishwakarma, Dr. Kamlesh Ahuja
Abstract-Machine Learning and Deep Learning Algorithms have been explored widely to identifyy potential avenues to optimize wireless networks. One such area happens to be a data driven model for initiating handover among multiple access techniques such as OFDM and NOMA. With increasing number of users and multimedia applications, bandwidth efficiency in cellular networks has become a critical aspect for system design. Bandwidth is a vital resource shared by wireless networks. Hence its in critical to enhance bandwidth efficiency. Orthogonal Frequency Division Multiplexing (OFDM) and Non-Orthogonal Multiple access (NOMA) have been the leading contenders for modern wireless networks. NOMA is a technique in which multiple users data is separated in the power domain. A typical wireless system generally has the capability of automatic fall back or handover. In such cases, there can be a switching from one of the technologies to another parallel or co-existing technology in case of changes in system parameters such as Bit Error Rate (BER) etc. This paper presents a review on existing machine learning based approaches for handover prediction in future generation wireless networks. The salient features of each of the approaches has been highlighted along with identifying potential research gaps.
