Leveraging Channel Capacity of a Macro Diversity MIMO System in Gamma-Shadowed Nakagami-m Fading Channel for QoS estimation using Quantum Machine Learning

In this paper we consider a system of one macro diversity SC receiver (MD SC) and two micro MRC (mD MRC) receivers, operating in a Nakagami-m multipath environment with gamma shadowing. The combination of the maximum L-branch SNR signal-to-noise ratio is realized at the micro level, and the selector combiner (SC) with two base stations is realized at the macro level. A closed-form expression for the channel capacity (CC) of the envelope of the SC macro diversity output signal is calculated. Graphical results show the effects of different system parameters on system performance, as well as the improvement due to the use of a combination of the micro and macro diversity. The derived expressions are used within GPU-based mobile network for modeling, planning and simulation of the system to estimate the quality of service (QoS).