Cooperative Sensing in Uplink ISAC System: A Multi-User Waveform Optimization Approach

• Published in: IEEE transactions on vehicular technology Vol. 74; no. 7; pp. 10943 - 10957
• Main Authors: Li, Yiheng, Wei, Zhiqing, Wang, Yi, Liu, Haoming, Feng, Zhiyong
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.07.2025; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Communication; Constraints; cooperative sensing; Cramer-Rao lower bound; Downlink; Hardware; Integrated sensing and communication; Interference; Lower bounds; multi-target detection; OFDM; Optimization; Orthogonal Frequency Division Multiplexing; resource allocation; Resource management; Root-mean-square errors; Sensors; Subcarriers; Subsystems; Uplink; Uplinking; Waveforms
• ISSN: 0018-9545; 1939-9359
• DOI: 10.1109/TVT.2025.3548138

Integrated sensing and communication (ISAC) is expected to become a crucial component of the sixth-generation (6G) networks owing to its outstanding spectrum management capability. However, improving the cooperative sensing capabilities of multiple ISAC user equipments (ISAC-UEs) in complex interference environment presents a significant research challenge. This paper focuses on the multi-user cooperative sensing in uplink orthogonal frequency division multiplexing (OFDM) ISAC system. By utilizing the stochastic geometry, we model the distribution of communication UEs (COM-UEs) as a one-dimensional Matern hard-core point process (1-D MHCP), and derive a closed-form expression for interference power. To further enhance cooperative sensing accuracy while maintaining quality of service (QoS) in communication, we perform waveform optimization by jointly optimizing the weighted range-velocity Cramer-Rao lower bound (CRLB) subject to communication data rate (CDR) and subcarrier power ratio (SPR) constraints. This approach involves selecting the optimal subcarriers for sensing and allocating the corresponding power on each subcarrier for communication and sensing subsystems. By employing the convex relaxation and the cyclic minimization algorithm (CMA), we decompose the complex optimization problem into three sub-problems, simplifying the original NP-hard problem into a solvable one via a cyclic optimization framework. The simulation results validate the effectiveness of our optimization strategy, and evaluate the influence of CDR and SPR constraints using the CRLB and root mean square error (RMSE).