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Joint Node Selection and Resource Allocation Optimization for Cooperative Sensing With a Shared Wireless Backhaul

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  • NETL
  • NSTL
  • IEEE
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-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 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.

SensorsReceiversLocation awarenessQuantization (signal)TransmittersSignal processing algorithmsBackhaul networksAccuracyDelay effectsWireless sensor networks

Mingxin Chen、Ming-Min Zhao、An Liu、Min Li、Qingjiang Shi

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College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, China|Zhejiang Provincial Key Laboratory of Multi-Modal Communication Networks and Intelligent Information Processing, Hangzhou, China

School of Software Engineering, Tongji University, Shanghai, China|Shenzhen Research Institute of Big Data, Shenzhen, China

2025

10.1109/TSP.2024.3516709

IEEE transactions on signal processing

IEEE transactions on signal processing

ISSN：
年,卷(期)：2025.73(1)
  • 44