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.

翻译：本文在未来兼具通信与感知能力的多功能网络背景下，研究一种协作感知框架。该框架中，一个基站作为感知发射机，若干邻近基站作为感知接收机。每个接收机接收目标反射的感知信号，并通过无线多址信道与融合中心进行通信，以实现协作目标定位。为提升定位性能，我们提出一种混合信息-信号域协作感知设计方案，其中每个感知接收机向融合中心同时传输估计的时延/有效反射系数以及接收到的感知信号在估计时延附近的采样值。随后，在克拉美-罗下界约束与多址信道容量限制下，我们提出通过采用高效的Karhunen-Loève变换编码方案进行信号量化与节点选择，以最小化信道使用次数。本文设计了一种新颖的矩阵不等式约束逐次凸逼近算法，用于优化无线回程资源分配，并结合贪婪策略进行节点选择。尽管所考虑问题具有高度非凸性，我们证明了所提算法能够收敛到通过松弛离散变量所获松弛问题的Karush-Kuhn-Tucker解集。此外，本文设计了一种低复杂度的量化比特重分配算法，该算法无需执行显式节点选择，且能够获得混合信息-信号域协作感知方案带来的大部分性能增益。最后，数值仿真结果表明，所提混合信息-信号域协作感知设计方案显著优于基线方案。