Networked sensing, which jointly exploits observations from multiple distributed nodes, is essential for unlocking the full sensing potential of integrated sensing and communications (ISAC). This article introduces multi-UE sensing, a new networked sensing paradigm for future perceptive mobile networks that exploits the correlated sensing observations naturally arising from distributed user equipment devices (UEs) interacting with common targets. Representative uplink, downlink, and hybrid sensing architectures are presented, together with a multi-view signal processing framework encompassing synchronization, correlation-aware parameter estimation, and sensing fusion. Key open challenges, including correlation modelling, target association, sensing information compression, and communication-sensing co-optimization, are also discussed.
J. A. Zhang, Jingying Bao, Kai Wu et al.· 0 citations
Network-level integrated sensing and communication (ISAC) is recognized as a transformative technology for next-generation mobile radio systems. By enabling collaboration among multiple transceivers, network-level ISAC can significantly enhance both communication and sensing performance through spatial diversity. However, existing resource allocation strategies typically overlook the impact of spatial geometry, where identical time-frequency resources contribute differently to sensing accuracy depending on the transceiver's location. This leaves the fundamental coupling between spatial topology and resource efficacy unclear, rendering optimal resource allocation a critical challenge for unlocking the full potential of network-level ISAC.To address this challenge, this paper investigates the optimal distribution of time-frequency resources across spatially distributed transceivers through a theoretically grounded two-stage framework. First, we analytically derive the optimal time and frequency aperture distributions for sensing, defined as the variances of the allocated symbol and subcarrier indices, respectively, under both two-transmitter and multi-transmitter scenarios. By exploiting the mathematical isomorphism between delay and Doppler estimation, we prove that the optimal resource allocation strategy follows the gradient direction of the Cramer-Rao Lower Bound (CRLB) with respect to the apertures. Second, to bridge the gap between theoretical aperture values and practical OFDMA constraints, such as the minimized communication rate of each user equipment (UE), we formulate the resource allocation as a combinatorial integer partitioning problem. To tackle the NP-hard nature of the formulated problem, a low-complexity Variance-Guided Partitioning Algorithm (VGPA) is proposed to jointly optimize the subcarrier and symbol patterns for communication and sensing.
Xiao-Yang Wang, Luting Kong, Lei Cao et al.· 0 citations
The unprecedented growth of machine-type devices has underscored the need for fundamental solutions to support emerging massive connectivity. In particular, unsourced random access (URA) has emerged as a promising paradigm, reframing the massive connectivity problem as a coding-theoretic challenge with favorable energy and spectral efficiency. Among the widely studied URA models, the Gaussian multiple-access channel (GMAC) and multi-input multi-output (MIMO) systems are of particular significance. Sparse code design is well-suited for URA, offering scalable solutions while retaining many advantages of legacy access protocols. However, existing sparse code designs often suffer from limited sparsity control, inefficient interference cancellation, and a strong dependence on specific channel code designs, posing challenges for long-term adaptability as more powerful channel codes continue to evolve. In MIMO-URA systems, additional activity detection and channel estimation phases typically lead to increased missed detection (MD) and false alarm (FA) errors compared with the GMAC model, which does not require these phases. While prior studies have predominantly focused on minimizing MD errors, the effective mitigation of FA errors remains an open problem. To address this challenge, we propose a sparse code with slotted transmission under the GMAC model, combined with an analytical power division strategy to enhance interference cancellation. Furthermore, we introduce a novel MIMO receiver framework based on joint pattern–data–channel (JPDC) estimation, which significantly reduces FA errors by leveraging the intrinsic correlation between user activity and transmitted data. Notably, the proposed method achieves improved overall system performance without requiring additional transmission overhead or complex algorithms.
Zhen-Tian Zhang, Mohammad Javad Ahmadi, Kai-Kit Wong et al.· IEEE Transactions on Wireles...· 5 citations
A modulation- and receive-filter-aware framework for the sensing-interference management in multi-cell OFDM-ISAC systems is developed and closed-form signal-to-interference-plus-noise ratio (SINR) expressions for each range--Doppler bin under matched filtering (MF) and reciprocal filtering (RF).
Kaitao Meng, Kawon Han, C. Masouros et al.· 0 citations
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