BAYESIAN RECEIVER DESIGN AND BEAMFORMING FOR MULTIUSER MIMO THZ COMMUNICATIONS USING PROBABILISTIC GRAPHICAL MODELS AND GRAPH NEURAL NETWORKS

Topic description

Context:

Wireless transmissions in sub-THz and THz bands [1] are widely regarded as an enabling technology for future 6G communications, due to the availability of unused spectrum. The short wavelengths enable to pack a large number of antenna elements at the transmitter and receiver side, thus enabling ultra-massive multiple-input multiple-output (UM-MIMO) with the potential of tera-bits per second data rates and precise localization. Besides traditional backhaul links, deploying sub-THz/THz technologies seems relevant for emerging applications such as vehicular [2] and device-to-device communications. Nevertheless, a number of signal processing processing issues remain largely unsolved in order to make affordable sub-THz/THz technologies available. Firstly, MIMO channels in these frequency bands suffer from huge signal-to-noise degradation due to significant path loss and blockage [3], which can partly be compensated using high-...