Cooperative Sensing Optimization over Multiple Access Channel with Limited Backhaul Capacity

In this paper, we consider a cooperative sensing framework in the context of future multi-functional network with both communication and sensing ability, where one base station (BS) serves as a sensing transmitter and several nearby BSs serve as sensing receivers. Each receiver receives the sensing signal reflected by the target and communicates with the fusion center (FC) through a backhaul-limited multiple access channel (MAC) for cooperative localization of the target. Different from schemes on only information domain or signal domain cooperation, we present a hybrid information-signal domain cooperative sensing (HISDCS) design, where each sensing receiver transmits both the estimated time delay/effective reflecting coefficient and the received sensing signal sampled around the estimated time delay to the FC. Then, we propose to minimize the number of channel uses by utilizing an efficient Karhunen-Loéve transformation (KLT) encoding scheme for signal quantization and proper node selection, under the Cramér-Rao lower bound (CRLB) constraint and the capacity limits of MAC. A novel matrix-inequality constrained successive convex approximation (MCSCA) algorithm is proposed to optimize the backhaul resource allocation, together with a greedy strategy for node selection. Finally, numerical simulations are presented to show that the proposed HISDCS design is able to outperform the baseline schemes significantly.