arXiv subjects

Y. Jay Guo

Publications and source records attributed to Y. Jay Guo.

At least 19 recordsLinked to original sources

arXiv2026

Multi-UE Networked Sensing: A New Paradigm for 6G Perceptive Mobile Networks

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.

eess.SP↗

arXiv2026

Rainfall Sensing via Mobile Communication Signals

Rainfall monitoring is important for hydrological observation, disaster warning, and environmental sensing, but conventional rain gauges and weather radars suffer from sparse deployment and high infrastructure costs. This paper proposes PMN-RainSense, a rainfall sensing framework using sub-6-GHz mobile communication signals that supports practical single-antenna deployment. Unlike attenuation-based approaches, which are unreliable at sub-6 GHz because rain-induced attenuation over short mobile access links is only on the order of hundredths of a decibel, the proposed framework exploits fine-grained dynamics. A spectral-temporal channel state information (CSI) compensation method suppresses packet-wise timing and phase distortions while preserving sensing-relevant information. Rainfall-sensitive features are extracted from the delay-Doppler domain to mitigate environmental interference, with angle-domain filtering as an optional extension for multi-antenna receivers. Under bandwidth and antenna constraints, rainfall-correlated Doppler fluctuations serve as the dominant sensing signature, while Doppler-domain normalization improves robustness across links and deployments. Controlled WiFi experiments demonstrate rainfall-associated Doppler broadening and achieve a three-class classification accuracy of 95.48% using a random forest classifier. Long-Term Evolution (LTE) CSI measurements collected from cellular base stations over 11 carrier frequencies from 0.763 to 2.68 GHz yield a mean absolute error (MAE) of 0.25-0.27 mm/h for rainfall intensity estimation using a one-dimensional convolutional network.

eess.SP↗

arXiv2026

Uplink Networked Sensing via Multiuser Correlation Exploitation

In this correspondence, we investigate networked sensing in perceptive mobile networks under a bistatic multi-transmitter single-receiver uplink topology, where multiple user equipments (UEs) transmit signals over orthogonal frequency-division multiple access (OFDMA) resources and a single base station performs joint sensing. Uplink clock asynchronism introduces offsets that destroy inter-packet coherence and hinder high-resolution sensing, while multi-user observations exhibit exploitable cross-user correlation. We therefore formulate an asynchronous multi-user uplink OFDMA sensing model and exploit common delay-cluster sparsity across UEs. A line-of-sight (LoS)-referenced calibration first suppresses the offsets, after which a shared-private delay-domain sparse Bayesian learning (SBL) model is used for delay support recovery and user grouping. Doppler and angle of arrival are then estimated from temporal and spatial phase differences. Simulation results show that the proposed scheme outperforms per-user processing, particularly under limited subcarrier budgets and in low signal-to-noise ratio (SNR) regimes.

eess.SP↗

arXiv2026

Bistatic Passive Sensing via CSI Power

Passive object sensing with communication signals is a key enabler of perceptive mobile networks and integrated sensing and communication. In practical bistatic deployments, transmitter-receiver asynchrony and hardware impairments introduce time-varying random phase offsets in Channel State Information (CSI). Together with limited bandwidth and small antenna arrays, these effects degrade sensing accuracy. This work proposes a lightweight bistatic passive tracking and sensing framework that operates in the CSI-power domain. CSI power suppresses these offsets without explicit phase calibration, while preserving target-induced sensing cues. We show that physically admissible constraints in the spatial-frequency domain induced by transmitter-receiver geometry can resolve the mirror ambiguity inherent to real-valued CSI power. Building on these properties, we develop a real-time 3D Fourier-domain processing pipeline that jointly recovers spectral (delay), spatial (angle), and temporal (Doppler) signatures. The resulting features are integrated into an online framework with adaptive motion detection, outlier suppression, and extended Kalman filter tracking with deterministic initialization, followed by position-refined micro-Doppler feature extraction for micro-motion sensing. Extensive experiments, including simulations, a real-world prototype using 3.1 GHz LTE signals, and an open-source gait recognition dataset, demonstrate the effectiveness of the proposed CSI-power-based framework for bistatic passive tracking and sensing.

eess.SP↗

arXiv2026

Rethinking RSSI for WiFi Sensing

The Received Signal Strength Indicator (RSSI) is ubiquitously available on commodity WiFi devices but is commonly regarded as too coarse for fine-grained sensing. This paper revisits its sensing potential and presents WiRSSI, a bistatic WiFi sensing framework that enables RSSI-only passive human tracking and motion sensing. WiRSSI employs a transmitter and a receiver equipped with a three-antenna array (1Tx-3Rx), and is readily extensible to Multiple-Input Multiple-Output (MIMO) deployments. We first show how Channel State Information (CSI) power implicitly preserves phase-related motion modulation and how this relationship carries over to RSSI, indicating that RSSI can retain exploitable Doppler, Angle-of-Arrival (AoA), and delay cues. WiRSSI extracts Doppler-AoA features via a lightweight 2D Fast Fourier Transform (FFT) pipeline and infers bistatic delay from amplitude-only information in the absence of subcarrier-level phase. The estimated AoA and delay are then mapped to Cartesian coordinates and denoised to recover motion trajectories. Experiments in practical environments show that WiRSSI achieves median XY localization errors of 0.905 m, 0.784 m, and 0.785 m for elliptical, linear, and rectangular trajectories, respectively, compared with 0.574 m, 0.599 m, and 0.514 m from a representative CSI-based method. We further demonstrate RSSI-only gesture recognition on the Widar3.0 dataset, where WiRSSI features provide meaningful discriminative performance. These results suggest that, despite lacking subcarrier-level information compared with CSI, RSSI can support practical WiFi sensing as a complementary and hardware-friendly option when CSI is restricted, unreliable, or privacy-sensitive.

eess.SP↗

arXiv2025

A Wideband Tri-Band Shared-Aperture Antenna Array for 5G and 6G Applications

This work presents a wideband tri-band shared-aperture antenna array covering the 5G mid-band and 6G centimetric band. The challenge of scattering and coupling suppression is holistically addressed across the wide bands. Guided by characteristic mode analysis (CMA), a segmented spiral radiator is developed to mitigate high-frequency scattering and coupling while maintaining low-frequency radiation performance. Compared with a conventional tube radiator, the proposed spiral achieves a reduced radar cross-section (RCS) over 4.7-21.5 GHz (128.2%). With the aid of serial resonators, the segmented-spiral dipole achieves impedance matching in the low band (LB, 3.05-4.68 GHz, 42.2%), covering the 5G band (3.3-4.2 GHz), while additional suppressors further reduce cross-band coupling. The middle band (MB) and high band (HB) antennas operate at 6.2-10.0 GHz (46.9%) and 10.0-15.6 GHz (43.8%), respectively, collectively covering the anticipated 5G-Advanced and 6G bands (6.425-15.35 GHz). Both the MB and HB antennas employ a planar magnetoelectric (ME) dipole structure to avoid common-mode resonances within the LB and MB and to minimize cross-band scattering in the HB. The proposed array maintains undistorted radiation patterns and better than 20 dB port isolation between any two ports across all three bands.

physics.app-ph↗

arXiv2025

A Tri-Band Shared-Aperture Base Station Antenna Array Covering 5G Mid-Band and 6G Centimetric Wave Band

This work proposes a tri-band shared-aperture antenna array with three wide bands, covering the 5G mid-band and the 6G centimetric band, which is a promising candidate for future 6G base station antennas. The challenge of suppressing interferences, including scattering and coupling, in the tri-band array is holistically addressed across wide bands. Guided by characteristic mode analysis (CMA), a segmented spiral radiator is efficiently developed to mitigate scattering and coupling at high frequencies while preserving radiation performance at low frequencies. Compared to a conventional tube radiator, the proposed spiral exhibits a reduced radar cross-section (RCS) over an ultra-wide range of 4.7-21.5 GHz (128.2%). With the aid of serial resonators, impedance matching of the segmented-spiral-based dipole antenna is achieved across the low band (LB) of 3.05-4.68 GHz (42.2%), spanning the 5G band 3.3-4.2 GHz. Moreover, suppressors are placed near the LB ports to further reduce the cross-band coupling. Middle band (MB) and high band (HB) antennas operate in 6.2-10.0 GHz (46.9%) and 10.0-15.6 GHz (43.8%), respectively, collectively covering the anticipated 5G-Advanced and 6G centimetric band of 6.425-15.35 GHz. Both the MB and HB antennas employ a planar magnetoelectric (ME) dipole structure, which prevents common-mode resonances in the LB and MB, and mitigates the scattering from the MB antenna in the HB. In this tri-band array, radiation patterns remain undistorted across the LB, MB, and HB, and the isolation between any two ports exceeds 20 dB over all three bands.

physics.app-ph↗

arXiv2025

Wavelet-Guided Water-Level Estimation for ISAC

Real-time water-level monitoring across many locations is vital for flood response, infrastructure management, and environmental forecasting. Yet many sensing methods rely on fixed instruments - acoustic, radar, camera, or pressure probes - that are costly to install and maintain and are vulnerable during extreme events. We propose a passive, low-cost water-level tracking scheme that uses only LTE downlink power metrics reported by commodity receivers. The method extracts per-antenna RSRP, RSSI, and RSRQ, applies a continuous wavelet transform (CWT) to the RSRP to isolate the semidiurnal tide component, and forms a summed-coefficient signature that simultaneously marks high/low tide (tide-turn times) and tracks the tide-rate (flow speed) over time. These wavelet features guide a lightweight neural network that learns water-level changes over time from a short training segment. Beyond a single serving base station, we also show a multi-base-station cooperative mode: independent CWTs are computed per carrier and fused by a robust median to produce one tide-band feature that improves stability and resilience to local disturbances. Experiments over a 420 m river path under line-of-sight conditions achieve root-mean-square and mean-absolute errors of 0.8 cm and 0.5 cm, respectively. Under a non-line-of-sight setting with vegetation and vessel traffic, the same model transfers successfully after brief fine-tuning, reaching 1.7 cm RMSE and 0.8 cm MAE. Unlike CSI-based methods, the approach needs no array calibration and runs on standard hardware, making wide deployment practical. When signals from multiple base stations are available, fusion further improves robustness.

eess.SP↗

arXiv2025

Efficient River Water Level Sensing Using Cellular CSI and Joint Space-Time Processing

Accurate and timely water level monitoring is critical for flood prevention, environmental management, and emerging smart infrastructure systems. Traditional water sensing methods often rely on dedicated sensors, which can be costly to deploy and difficult to maintain and are vulnerable to damage during floods.In this work, we propose a novel cellular signalbased sensing scheme that passively estimates water level changes using downlink mobile signals from existing communication infrastructure. By capturing subtle variations in channel state information (CSI), the proposed method estimates the length changes of the water-reflected signal path, which correspond to water level variations. A space-time processing framework is developed to jointly estimate the angle of arrival and Doppler shift, enabling isolation and enhancement of the water-reflected path via beamforming, while effectively suppressing environmental noise. The phase evolution of the beamformed signal is then extracted to infer water level changes. To address clock asynchronism between the transmitter and receiver inherent in bistatic systems, we introduce a beamforming-based compensation technique for removing time-varying random phase offsets in CSI. Field experiments conducted across a river demonstrate that the proposed method enables accurate and reliable water level estimation, achieving a mean accuracy ranging from 1.5 cm to 3.05 cm across different receiver configurations and deployments.

eess.SP↗

arXiv2025

Towards SISO Bistatic Sensing for ISAC

Integrated Sensing and Communication (ISAC) is a key enabler for next-generation wireless systems. However, real-world deployment is often limited to low-cost, single-antenna transceivers. In such bistatic Single-Input Single-Output (SISO) setup, clock asynchrony introduces random phase offsets in Channel State Information (CSI), which cannot be mitigated using conventional multi-antenna methods. This work proposes WiDFS 3.0, a lightweight bistatic SISO sensing framework that enables accurate delay and Doppler estimation from distorted CSI by effectively suppressing Doppler mirroring ambiguity. It operates with only a single antenna at both the transmitter and receiver, making it suitable for low-complexity deployments. We propose a self-referencing cross-correlation (SRCC) method for SISO random phase removal and employ delay-domain beamforming to resolve Doppler ambiguity. The resulting unambiguous delay-Doppler-time features enable robust sensing with compact neural networks. Extensive experiments show that WiDFS 3.0 achieves accurate parameter estimation, with performance comparable to or even surpassing that of prior multi-antenna methods, especially in delay estimation. Validated under single- and multi-target scenarios, the extracted ambiguity-resolved features show strong sensing accuracy and generalization. For example, when deployed on the embedded-friendly MobileViT-XXS with only 1.3M parameters, WiDFS 3.0 consistently outperforms conventional features such as CSI amplitude, mirrored Doppler, and multi-receiver aggregated Doppler.

eess.SP↗

arXiv2025

ISAC: From Human to Environmental Sensing

Integrated Sensing and Communications (ISAC) is poised to become one of the defining capabilities of the sixth generation (6G) wireless communications systems, enabling the network infrastructure to jointly support high-throughput communications and situational awareness. While recent advances have explored ISAC for both human-centric applications and environmental monitoring, existing research remains fragmented across these domains. This paper provides the first unified review of ISAC-enabled sensing for both human activities and environment, focusing on signal-level mechanisms, sensing features, and real-world feasibility. We begin by characterising how diverse physical phenomena, ranging from human vital sign and motion to precipitation and flood dynamics, impact wireless signal propagation, producing measurable signatures in channel state information (CSI), Doppler profiles, and signal statistics. A comprehensive analysis is then presented across two domains: human sensing applications including localisation, activity recognition, and vital sign monitoring; and environmental sensing for rainfall, soil moisture, and water level. Experimental results from Long-Term Evolution (LTE) sensing under non-line-of-sight (NLOS) conditions are incorporated to highlight the feasibility in infrastructure-limited scenarios. Open challenges in signal fusion, domain adaptation, and generalisable sensing architectures are discussed to facilitate future research toward scalable and autonomous ISAC.

eess.SP↗

arXiv2025

Water Level Sensing via Communication Signals in a Bi-Static System

Accurate water level sensing is essential for flood monitoring, agricultural irrigation, and water resource optimization. Traditional methods require dedicated sensor deployments, leading to high installation costs, vulnerability to interference, and limited resolution. This work proposes PMNs-WaterSense, a novel scheme leveraging Channel State Information (CSI) from existing mobile networks for water level sensing. Our scheme begins with a CSI-power method to eliminate phase offsets caused by clock asynchrony in bi-static systems. We then apply multi-domain filtering across the time (Doppler), frequency (delay), and spatial (Angle-of-Arrival, AoA) domains to extract phase features that finely capture variations in path length over water. To resolve the $2π$ phase ambiguity, we introduce a Kalman filter-based unwrapping technique. Additionally, we exploit transceiver geometry to convert path length variations into water level height changes, even with limited antenna configurations. We validate our framework through controlled experiments with 28 GHz mmWave and 3.1 GHz LTE signals in real time, achieving average height estimation errors of 0.025 cm and 0.198 cm, respectively. Moreover, real-world river monitoring with 2.6 GHz LTE signals achieves an average error of 4.8 cm for a 1-meter water level change, demonstrating its effectiveness in practical deployments.

eess.SP↗

arXiv2024

Wireless Communications in Doubly Selective Channels with Domain Adaptivity

Wireless communications are significantly impacted by the propagation environment, particularly in doubly selective channels with variations in both time and frequency domains. Orthogonal Time Frequency Space (OTFS) modulation has emerged as a promising solution; however, its high equalization complexity, if performed in the delay-Doppler domain, limits its universal application. This article explores domain-adaptive system design, with an emphasis on adaptive equalization, while also discussing modulation and pilot placement strategies. It investigates the dynamic selection of best-fit domains based on channel conditions to enhance performance across diverse environments. We examine channel domain connections, signal designs, and equalization techniques with domain adaptivity, and highlight future research opportunities.

eess.SP↗

arXiv2024

Sensing in Bi-Static ISAC Systems with Clock Asynchronism: A Signal Processing Perspective

Integrated Sensing and Communication (ISAC) has been identified as a pillar usage scenario for the impending 6G era. Bi-static sensing, a major type of sensing in ISAC, is promising to expedite ISAC in the near future, as it requires minimal changes to the existing network infrastructure. However, a critical challenge for bi-static sensing is clock asynchronism due to the use of different clocks at far-separated transmitters and receivers. This causes the received signal to be affected by time-varying random phase offsets, severely degrading, or even failing, direct sensing. Hence, to effectively enable ISAC, considerable research has been directed toward addressing the clock asynchronism issue in bi-static sensing. This paper provides an overview of the issue and existing techniques developed in an ISAC background. Based on the review and comparison, we also draw insights into the future research directions and open problems, aiming to nurture the maturation of bi-static sensing in ISAC.

eess.SP↗

arXiv2024

Performance Bounds and Optimization for CSI-Ratio based Bi-static Doppler Sensing in ISAC Systems

Bi-static sensing is crucial for exploring the potential of networked sensing capabilities in integrated sensing and communications (ISAC). However, it suffers from the challenging clock asynchronism issue. CSI ratio-based sensing is an effective means to address the issue. Its performance bounds, particular for Doppler sensing, have not been fully understood yet. This work endeavors to fill the research gap. Focusing on a single dynamic path in high-SNR scenarios, we derive the closed-form CRB. Then, through analyzing the mutual interference between dynamic and static paths, we simplify the CRB results by deriving close approximations, further unveiling new insights of the impact of numerous physical parameters on Doppler sensing. Moreover, utilizing the new CRB and analyses, we propose novel waveform optimization strategies for noise- and interference-limited sensing scenarios, which are also empowered by closed-form and efficient solutions. Extensive simulation results are provided to validate the preciseness of the derived CRB results and analyses, with the aid of the maximum-likelihood estimator. The results also demonstrate the substantial enhanced Doppler sensing accuracy and the sensing capabilities for low-speed target achieved by the proposed waveform design.

cs.IT↗

arXiv2024

Anchor-points Assisted Uplink Sensing in Perceptive Mobile Networks

Uplink sensing in integrated sensing and communications (ISAC) systems, such as Perceptive Mobile Networks, is challenging due to the clock asynchronism between transmitter and receiver. Existing solutions typically require the presence of a dominating line-of-sight path and the knowledge of transmitter location at the receiver. In this paper, relaxing these requirements, we propose a novel and effective uplink sensing scheme with the assistance of static anchor points. Two major algorithms are proposed in the scheme. The first algorithm estimates the relative timing and carrier frequency offsets due to clock asynchronism, with respect to those at a randomly selected reference snapshot. Theoretical performance analysis is provided for the algorithm. The estimates from the first algorithm are then used to compensate for the offsets and generate the angle-Doppler maps. Using the maps, the second algorithm identifies the anchor points, and then locates the UE and dynamic targets. Feasibility of UE localization is also analyzed. Simulation results are provided and demonstrate the effectiveness of the proposed algorithms.

eess.SP↗

arXiv2023

Simultaneous Beam and User Selection for the Beamspace mmWave/THz Massive MIMO Downlink

Beamspace millimeter-wave (mmWave) and terahertz (THz) massive MIMO constitute attractive schemes for next-generation communications, given their abundant bandwidth and high throughput. However, their user and beam selection problem has to be efficiently addressed. Inspired by this challenge, we develop low-complexity solutions explicitly. We introduce the dirty paper coding (DPC) into the joint user and beam selection problem. We unveil the compelling properties of the DPC sum rate in beamspace massive MIMO, showing its monotonic evolution against the number of users and beams selected. We then exploit its beneficial properties for substantially simplifying the joint user and beam selection problem. Furthermore, we develop a set of algorithms striking unique trade-offs for solving the simplified problem, facilitating simultaneous user and beam selection based on partial beamspace channels for the first time. Additionally, we derive the sum rate bound of the algorithms and analyze their complexity. Our simulation results validate the effectiveness of the proposed design and analysis, confirming their superiority over prior solutions.

eess.SP↗

arXiv2023

Green Joint Communications and Sensing Employing Analog Multi-Beam Antenna Arrays

Joint communications and sensing (JCAS) is potentially a hallmark technology for the sixth generation mobile network (6G). Most existing JCAS designs are based on digital arrays, analog arrays with tunable phase shifters, or hybrid arrays, which are effective but are generally complicated to design and power inefficient. This article introduces the energy-efficient and easy-to-design multi-beam antenna arrays (MBAAs) for JCAS. Using pre-designed and fixed analog devices, such as lens or Butler matrix, MBAA can simultaneously steer multiple beams yet with negligible power consumption compared with other techniques. Moreover, MBAAs enable flexible beam synthesis, accurate angle-of-arrival estimation, and easy handling/utilization of the beam squint effect. All these features have not been well captured by the JACS community yet. To promote the awareness of them, we intuitively illustrate them and also exploit them for constructing a multi-beam JCAS framework. Finally, the challenges and opportunities are discussed to foster the development of green JCAS systems.

eess.SP↗