Conference PaperIRS-Aided Massive MIMO ISAC SystemsKulathunga, Ranga [School of Electrical, Computer, and Biomedical Engineering, Southern Illinois University,Carbondale,IL,USA,62901]; Dassanayake, Janith Kavindu [School of Electrical, Computer, and Biomedical Engineering, Southern Illinois University,Carbondale,IL,USA,62901]; ArumaBaduge, Gayan AmarasuriyaThe performance of integrated sensing and communications (ISAC) empowered intelligent reflecting surface (IRS)aided massive multiple-input multiple-output (MIMO) systems operating over spatially correlated Rician fading is investigated. Computationally-efficient linear precoders are used to construct the ISAC signal by invoking the maximal ratio transmission (MRT) criterion into the composite channels containing both direct and IRS reflected channels. The uplink communication channels are estimated based on the linear minimum mean square error criterion and used to construct user precoders. The IRS phase-shifts are optimized based on the statistical channel knowledge to maximize the minimum average power gains of the composite communication channels subject to an average power threshold for the reflected sensing channel. The communication performance is evaluated by deriving the achievable user rates, while the sensing performance is studies by locating the target via the 2D MUltiple SIgnal Classification (MUSIC) algorithm. Our numerical results are used to study the trade-off between the communication and sensing performance metrics in IRS-aided massive MIMO systems with MRT-based linear precoders.IEEE2025-06-08106522914044 to 4049https://doi.org/10.1109/ICC52391.2025.111614702326621National Science Foundation