  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
wireless multiple access channel (MAC) for cooperative target localization. To
improve the localization performance, 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-Lo\'eve transformation (KLT) encoding scheme for signal
quantization and proper node selection, under the Cram\'er-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 wireless backhaul resource allocation, together with a greedy
strategy for node selection. Despite the high non-convexness of the considered
problem, we prove that the proposed MCSCA algorithm is able to converge to the
set of Karush-Kuhn-Tucker (KKT) solutions of a relaxed problem obtained by
relaxing the discrete variables. Besides, a low-complexity quantization bit
reallocation algorithm is designed, which does not perform explicit node
selection, and is able to harvest most of the performance gain brought by
HISDCS. Finally, numerical simulations are presented to show that the proposed
HISDCS design is able to significantly outperform the baseline schemes.
