Abstract: Embodiments herein provide a method handover management in a wireless network (1000). Ultra-reliable low latency communication (URLLC) is a key feature in 5G which requires improved mobility performance and reliability. In future, the number of mobility (handover) scenarios is bound to increase many folds, and without proper technologies, the number of mobility may induce more handover failures. For a better quality of experience (QoE) in 5G new radio (NR), it is important to have minimal interruption time and a high handover success rate. The method in the present disclosure provides a novel machine learning (ML) based advance handover (HO). Further, the method provides initiating HO, by a source gNB in advance before a user equipment (UE) runs into radio link failure (RLF) to ensure less handover failure (HOF) rate.

Abstract: Embodiments herein provide a method handover management in a wireless network (1000). Ultra-reliable low latency communication (URLLC) is a key feature in 5G which requires improved mobility performance and reliability. In future, the number of mobility (handover) scenarios is bound to increase many folds, and without proper technologies, the number of mobility may induce more handover failures. For a better quality of experience (QoE) in 5G new radio (NR), it is important to have minimal interruption time and a high handover success rate. The method in the present disclosure provides a novel machine learning (ML) based advance handover (HO). Further, the method provides initiating HO, by a source gNB in advance before a user equipment (UE) runs into radio link failure (RLF) to ensure less handover failure (HOF) rate.