arXiv subjects

Ziguo Zhong

Publications and source records attributed to Ziguo Zhong.

3 recordsLinked to original sources

arXiv2026

Characterization and Mitigation of Polyphase-Code Artifacts in 5G NR ISAC

Target sensing utilizing 5G NR reference signals has emerged as a prominent research direction in both academia and industry. However, non-ideal factors in practical deployments exert a significant detrimental impact on target sensing performance, manifesting as artifacts in the RV spectrum. These artifacts mask weak targets and cause severe false alarms. To address these challenges, this paper establishes a theoretical model of artifacts and constructs a data-physics-driven deep learning paradigm for artifact mitigation. First, the origin of artifacts and their characteristics are theoretically derived. These analyses demonstrate that the artifacts are associated with polyphase codes, e.g., Zadoff-Chu sequences, and reveal their characteristics, including periodic extensions in the range domain and spectral spreading in the velocity domain. Then, the physical priors of artifacts are formalized as temporal continuity and spatial consistency, informing the design of the training mechanism for the proposed network. Guided by these insights, we propose a PIAENet. At its core is a multi-frame selective-masked encoder-decoder module, explicitly designed to incorporate the above priors. Specifically, temporal continuity is implemented via a multi-frame mechanism to capture features across consecutive RV spectra. Meanwhile, spatial consistency is realized through a selective masking mechanism to enhance reconstruction of artifact-affected regions. Extensive validation is conducted using real-world measured data collected with commercial mmWave equipment. The polyphase-code-related characteristics of the artifacts are experimentally validated. Meanwhile, the experimental results demonstrate that the proposed PIAENet not only effectively reduces the false target count but also improves the detection probability from 79.58% to 98.88%.

eess.SP↗

arXiv2025

Bayesian Probability Fusion for Multi-AP Collaborative Sensing in Mobile Networks

Integrated sensing and communication is widely acknowledged as a foundational technology for next-generation mobile networks. Compared with monostatic sensing, multi-access point (AP) collaborative sensing endows mobile networks with broader, more accurate, and resilient sensing capabilities, which are critical for diverse location-based sectors. This paper focuses on collaborative sensing in multi-AP networks and proposes a Bayesian probability fusion framework for target parameter estimation using orthogonal frequency-division multiplexing waveform. The framework models multi-AP received signals as probability distributions to capture stochastic observations from channel noise and scattering coefficients. Prior information is then incorporated into the joint probability density function to cast the problem as a constrained maximum a posteriori estimation. To address the high-dimensional optimization, we develop a prior-constrained gradient ascent (PCGA) algorithm that decouples correlated parameters and performs efficient gradient updates guided by the target prior. Theoretical analysis covers optimal fusion weights for global signal-to-noise ratio maximization, PCGA convergence, and the Cramer-Rao lower bound of the estimator, with insights applicable to broader fusion schemes. Extensive numerical simulations and real-world experiments with commercial devices show the framework reduces transmission overhead by 90% versus signal fusion and lowers estimation error by 41% relative to parameter fusion. Notably, field tests achieve submeter accuracy with 50% probability in typical coverage of mmWave APs. These improvements highlight a favorable balance between communication efficiency and estimation accuracy for practical multi-AP sensing deployment. The dataset is released for research purposes and is publicly available at: http://pmldatanet.com.cn/dataapp/multimodal

eess.SP↗

arXiv2025

Active IRS-Enabled Integrated Sensing and Communications with Extended Targets

This paper studies the active intelligent reflecting surface (IRS)-enabled integrated sensing and communications (ISAC), in which an active IRS is deployed to assist the base station (BS) in serving multiple communication users (CUs) and simultaneously sensing an \emph{extended} target at the non-line-of-sight (NLoS) area of the BS. The active IRS has the capability of amplifying the reflected signals so as to overcome significant reflection path loss in NLoS communication and sensing. In particular, we derive the sensing Cramér-Rao bound (CRB) for estimating the target response matrix. Accordingly, we jointly optimize the transmit beamforming at the BS and the reflective beamforming at the active IRS to minimize the sensing CRB, subject to the signal-to-interference-plus-noise ratio (SINR) requirements at the CUs, the transmit power budgets at the BS and active IRS, as well as the power amplification gain constraints at the active IRS. The CRB minimization problem is highly non-convex and thus difficult to solve in general. To address this challenge, we first focus on two specified conditions by considering the sensing-only scenario via ignoring the SINR constraints for communications, for which the closed-form optimal transmit beamforming is derived. Then, we propose two efficient alternating optimization (AO)-based algorithms to obtain high-quality solutions for the general ISAC scenarios. Next, we analyze the inherent relationship between the power scaling at the BS and the amplification scaling at the active IRS. It is shown that the active IRS always amplifies the signal using the maximum amplification gain under practical system settings. Finally, numerical results are provided to verify the effectiveness of the proposed AO-based algorithms and the benefits of active IRS-enabled ISAC compared to its passive IRSs counterparts.

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