The next-generation, i.e., 6G and beyond communication systems, are now a topic of significant research interest. The 6G communication system is predicted to offer higher data rates, stringent conditions on latency, and energy efficiency to meet the demand for innovative futuristic applications. In order to fulfill these requirements, many enabling technologies are proposed. In this thesis, we analyze a few key enablers for next-generation communication systems. We focus on cell-free massive MIMO (CF-mMIMO) systems and intelligent reflecting surface (IRS). Also, we study user-centric cell-free massive MIMO, a variant of CF-mMIMO.

We focus on the statistical characterization of the signal-to-interference-plus-noise ratio (SINR) of the uplink of CF-mMIMO. We have approximated the SINR via Log-normal distribution using a two-step moment matching method. Further, we use the Log-normal approximation of SINR to derive the uplink ergodic rate expression, which leads to simple upper and lower bounds on the ergodic rate. We also investigated the uplink performance of the user-centric CF mMIMO system in the presence of a mixed ADC resolution profile at the APs. A joint optimization problem is formulated to optimize the antenna selection coefficients at the APs and user transmit power. We have proposed an alternating optimization algorithm to maximize the uplink sumrate subject to energy constraints. A complete meta-heuristic algorithm is proposed with low time complexity.
The second part of the talk will be focused on the IRS-assisted communication systems. We analyzed the outage probability (OP) of an IRS-assisted SISO communication system in the most general case, i.e., the presence of an SD link,