Dense user communication systems suffer from coverage degradation at cell edges and increased power consumption due to unfavorable propagation conditions and high user density. To address these challenges, this paper presents an integrated communication framework that combines Intelligent Reflecting Surfaces (IRSs) and cell-free massive multiple-input multiple-output (MIMO) systems. By deploying IRSs, network coverage is extended to blind regions, while the use of distributed access points (APs) helps reduce transmit power per AP. The high user density is efficiently managed using the non-orthogonal multiple access (NOMA) technique through power-domain multiplexing. The mathematical formulations of the proposed framework are presented along with the channel and signal transmission modeling. Furthermore, two IRS phase shift optimization methods, namely alternating optimization (AO) and semidefinite relaxation (SDR) are investigated for maximum achievable rate. The two methods are compared for their convergence and complexity behaviour. Next, an AP selection algorithm, namely APSA, is proposed that dynamically assigns a set of APs to each user to reduce the resource overhead. The computational complexity analysis of the proposed algorithm is also investigated. The simulation results reveal an SINR gain of 4 dB for cell-center users and 2.4 dB for cell-edge users with the proposed selection approach. Comparative performance analysis is carried out against existing baseline approaches for varying transmit power, IRS elements, and user locations. The proposed integrated system is also compared with conventional system configurations. A use case scenario is presented to demonstrate its applicability in road surveillance and traffic monitoring. In the end, the practical constraints of the proposed work and the future directions are discussed.
Shrivastava et al. (Fri,) studied this question.