Joint Transmission and Compression Design for 6G Networked Sensing with Limited-Capacity Backhaul

This paper considers networked sensing in cellular network, where multiple base stations (BSs) first compress their received echo signals from multiple targets and then forward the quantized signals to the cloud via limited-capacity backhaul links, such that the cloud can leverage all useful echo signals to perform high-resolution localization. Under this setup, we manage to characterize the posterior Cramér-Rao Bound (PCRB) for localizing all the targets as a function of the transmit covariance matrix and the compression noise covariance matrix of each BS. Then, a PCRB minimization problem subject to the transmit power constraints and the backhaul capacity constraints is formulated to jointly design the BSs' transmission and compression strategies. We propose an efficient algorithm to solve this problem based on the alternating optimization technique. Specifically, it is shown that when either the transmit covariance matrices or the compression noise covariance matrices are fixed, the successive convex approximation technique can be leveraged to optimize the other type of covariance matrices locally. Numerical results are provided to verify the effectiveness of our proposed algorithm.