Integrated Sensing, Communication, and Positioning in Cellular Vehicular Networks

In this correspondence, a novel integrated sensing and communication (ISAC) framework is proposed to accomplish data communication, vehicle positioning, and environment sensing simultaneously in a cellular vehicular network. By incorporating the vehicle positioning problem with the existing computational-imaging-based ISAC models, we formulate a special integrated sensing, communication, and positioning problem in which the unknowns are highly coupled. To mitigate the rank deficiency and make it solvable, we discretize the region of interest (ROI) into sensing and positioning pixels respectively, and exploit both the line-of-sight and non-line-of-sight propagation of the vehicles' uplink access signals. The resultant problem is shown to be a polynomial bilinear compressed sensing (CS) reconstruction problem, which is then solved by the alternating optimization (AO) algorithm to iteratively achieve symbol detection, vehicle positioning and environment sensing. Performance analysis and numerical results demonstrate the effectiveness of the proposed method