5G Wireless Channel Test Scenarios Including Simulations of Swarm Intelligence Optimization Algorithms on Massive MIMO Simulation Network

Abstract

5G Wireless Communication technology hardware trials, testbed and deployments are happening throughout the world in a faster pace since 2018. When this standard rolled out by IMT 2020, ITU and 3GPP, the deployment and market usage is expected by the year 2020. Various universities and telecom companies already deployed trials and implemented test trials. The principle document for any testbed or hardware trials or deployments need to cater the understanding of 3GPP release document as of now LTE release 17 – M.2412 document. This paper concentrates on the various channel propagation test environment scenarios adopted in this LTE release M2412 document. This paper shows the basic simulation that completes estimation of received power versus transmitter-receiver distance in urban macro and Rural Macro, LMLC test model environment. This research paper also aims for a second part which is also an extension of the author work published in one of the similar work in applying swarm intelligence optimization on a Massive MIMO simulation work. This work utilizes two swarm intelligence optimization algorithms, Chameleon swarm intelligence optimization (CSIO) and Capuchin search optimization (CSO) algorithms applied on Massive MIMO network. The constraint parameters of spectral efficiency estimation of uplink scenario identified are bandwidth, B, transmission power P and coherence time, Tau_C by taking a Massive MIMO network simulation framework. Lower bound and upper bound values are fixed for these constraint parameters and found out the best global values. These global values of constraint parameters are feed back to the Massive MIMO network simulator and observe the spectral efficiency considering Number of base station antennas, M = 10, number of user antennas, K=1 considering M-MMSE receiver combining scheme. This paper leads to a new research insight of developing mathematical structure of swarm intelligence to ensure the entire network which the author consider as a future scope of study.