Quantum Machine Learning-Assisted Channel Capacity Analysis of L —branch SC Diversity Receiver in α — µ Fading and CCI Environment

Abstract

This paper deals with the derivation of the expression for the channel capacity (CC) of selection combining (SC) receiver with L branches in the conditions of short-term fading and co-channel interference (CCI) under α − µ distribution. Usage of α − µ distribution is usually used model for short-term fading of THz links. We first derive the analytical results for the CC in the closed-form under α µ distribution. Then, some graphs are plotted to highlight the− impact of short-term fading and CCI on the CC performance. In addition, quantum computing-based machine learning approach to service consumer number prediction and Quality of Service (QoS) level estimation leveraging the previously obtained channel capacity value using Qiskit library in Python is introduced.