Abstract
This paper proposes a potential enhancement of handover for the next-generation multi-tier cellular network, utilizing two fifth-generation (5G) enabling technologies: multi-access edge computing (MEC) and machine learning (ML). MEC and ML techniques are the primary enablers for enhanced mobile broadband (eMBB) and ultra-reliable and low latency communication (URLLC). The subset of ML chosen for this research is deep learning (DL), as it is adept at learning long-term dependencies. A variant of artificial neural networks called a long short-term memory (LSTM) network is used in conjunction with a look-up table (LUT) as part of the proposed solution. Subsequently, edge computing virtualization methods are utilized to reduce handover latency and increase the overall throughput of the network. A realistic simulation of the proposed solution in a multi-tier 5G radio access network (RAN) showed a 40–60% improvement in overall throughput. Although the proposed scheme may increase the number of handovers, it is effective in reducing the handover failure (HOF) and ping-pong rates by 30% and 86%, respectively, compared to the current 3GPP scheme.
Highlights
If at any point during the handover procedure, the desired BS’s channel quality information (CQI) is < 1, the handover is stopped, and the user equipment (UE) is moved to the connected state; If 16 or more communication failures occur in a set handover period, these are considered gross handover failures [26], the UE will be disconnected from the BS and become idle
The training is based on supervised learning, where the long short-term memory (LSTM) is made aware of all 24 classification variations
The results showed that the quality of experience (QoE) targets are achieved with improvement in the UE satisfaction rate by 40% over the 3rd generation partnership project (3GPP) scheme
Summary
A variant of artificial neural networks called a long short-term memory (LSTM) network is used in conjunction with a look-up table (LUT) as part of the proposed solution. Edge computing virtualization methods are utilized to reduce handover latency and increase the overall throughput of the network. A realistic simulation of the proposed solution in a multi-tier 5G radio access network (RAN) showed a 40–60% improvement in overall throughput. With the introduction of the fifth-generation (5G) cellular network [1], the industry is posed with many diverse challenges. The issues relating to multi-tier handovers have not been effectively resolved to this day. There is literature addressing the issues of handover, but only a small portion of these adopt a form of artificial intelligence (AI) or cloud computing techniques in their solutions
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