arXiv subjects

Jingli Li

Publications and source records attributed to Jingli Li.

5 recordsLinked to original sources

arXiv2026

Safety-Aware Forward Detection in Networked ISAC for Low-Altitude UAV Flight

Networked integrated sensing and communication (ISAC) exploits cooperation among multiple ground base stations (GBSs) to support safe uncrewed aerial vehicle (UAV) flight in low-altitude wireless networks (LAWNs). Existing studies mainly focus on communication enhancement or target parameter estimation, while the detection reliability of non-cooperative targets in the UAV forward region remains insufficiently investigated. To address this issue, this paper proposes a safety-aware forward detection design in networked ISAC, where multiple GBSs jointly support UAV downlink communication, state estimation, and non-cooperative target detection within the forward region of interest (ROI). First, the forward ROI is determined by the UAV position, velocity, and safe braking distance, and is voxelized to characterize target-existence states. Then, the Cram\'er-Rao lower bound (CRLB) for UAV state estimation and the forward-ROI miss-detection probability are derived, and their scaling laws are characterized: In detail, the UAV state-estimation CRLB approximately decreases as $\ln^{-2}J$ with the number of cooperative GBSs $J$, while the forward-ROI miss-detection probability follows an exponential-form scaling law as $\lambda_{t}D_{f}\ln^{-2}J$. Furthermore, a safety-aware resource optimization problem is formulated to jointly configure the sensing pilot ratio, transmit power, and beam direction, balancing UAV state-estimation performance and forward detection reliability under the communication-rate constraint. Simulation results show that, compared with the baseline scheme without forward detection, the proposed design reduces the average miss-detection probability and the corresponding sensing-induced collision risk by $17.05\%$, while introducing only limited state-estimation performance degradation, reflected by a $14.82\%$ increase in the average CRLB.

cs.IT↗

arXiv2026

Closed-Loop Integrated Sensing, Communication, and Control for Efficient Drone Flight

Low-altitude wireless networks (LAWN) require drones to follow specific trajectories controlled by ground base stations (GBSs). However, given complex low-altitude channel conditions and limited spectrum and power resources, sensing errors and wireless link unreliability cannot be ignored, leading to trajectory deviations that threaten flight safety. To address this issue, this paper proposes an integrated sensing-communication-control (ISCC) closed-loop trajectory tracking approach, aiming to reveal the coupling mechanisms among communication, sensing, and control during drone flight. In detail, we incorporate sensing errors in trajectory state estimation, packet losses in control command transmission, and finite blocklength transmission effects into the closed-loop dynamics. First, through theoretical analysis, we identify the dominant role of the time-frequency resources allocated to control in ensuring system stability and derive a lower bound on the resources required to guarantee stable operation. Second, to minimize tracking error, we formulate a time-frequency resource allocation optimization problem for the sensing, communication, and control components, subject to constraints on communication rate and closed-loop stability. Accordingly, a solution algorithm based on successive convex approximation is proposed. Third, simulation results indicate that once stability is ensured, system performance is primarily determined by sensing accuracy, with the trajectory tracking error exhibiting an approximately linear dependence on the position error bound. Finally, it is shown that the proposed ISCC scheme avoids trajectory divergence under FBL transmission compared with ISCC designs ignoring control packet loss, and could achieve decimeter-level average tracking accuracy, reducing the error to only 17.37% of that observed in the baseline global navigation satellite system scheme.

cs.PF↗

arXiv2025

Integrated Sensing, Communication, Computing, and Control Meets UAV Swarms in 6G

To develop the low-altitude economy, the establishment of the low-altitude wireless network (LAWN) is the first priority. As the number of unmanned aerial vehicles (UAVs) increases, how to support the reliable flying and effective functioning of UAV swarms is challenging. Recently, the integrated sensing, communication, computing, and control (ISCCC) strategy was designed, which could act as effective physical {\it reflex arc} links in the intelligent LAWN system. Thus, in this article, we outline the challenges and opportunities when ISCCC meets UAV swarm in LAWN in 6G. First, we propose a three-layer ISCCC structure for the UAV swarm, which is categorized according to the UAV swarm's requirements, i.e., flying, self-organizing, and functioning. Second, for different scenarios, we study the basic problem, promising technologies, and challenges to design ISCCC for UAV swarms. Third, through a case study that minimizes the flying trajectory error of the UAV swarm based on the ISCCC principle, we show that ISCCC is promising to simultaneously improve the reliability and efficiency of LAWN via jointly designing four components. Finally, we discuss the promising directions for the ISCCC-based UAV swarm in LAWN.

eess.SP↗

arXiv2025

Sensing-Enhanced Handover Criterion for Low-Altitude Wireless Network (LAWNs)

With the rapid growth of the low-altitude economy, the demand for cellular-enabled low-altitude wireless networks (LAWN) is rising significantly. The three-dimensional mobility of drones will lead to frequent handovers (HOs) in cellular networks, while traditional reference signal received power (RSRP)-based criteria may fail to capture the dynamic environment, causing redundant HOs or HO failures. To address this issue and motivated by the underutilization of sensing information in conventional HO mechanisms, we propose a novel HO activation criterion for drone systems that integrates both sensing parameters provided by integrated sensing and communication (ISAC) signals and RSRP. First, we construct an ISAC signal model tailored for low-altitude scenarios and derive the Cram\'er--Rao lower bound for sensing distance estimation. Subsequently, we propose a novel joint HO criterion that extends the conventional RSRP-based method by integrating sensing information from ISAC signals, enabling more reliable HOs in dynamic drone environments. Simulation results show that the joint HO criterion outperforms the baseline RSRP-based criterion under different signal-to-noise ratio (SNR) and sensing pilot ratio conditions. Particularly, when the SNR exceeds 0dB and the sensing pilot ratio is 20%, the proposed joint HO criterion reduces the average HO region length by 75.20% and improves the activation probability by 76.31%.

cs.IT↗

arXiv2024

Channel Spreading Function-Inspired Channel Transfer Function Estimation for OFDM Systems with High-Mobility

In this letter, we propose a novel channel transfer function (CTF) estimation approach for orthogonal frequency division multiplexing (OFDM) systems in high-mobility scenarios, that leverages the stationary properties of the delay-Doppler domain channel spreading function (CSF). First, we develop a CSF estimation model for OFDM systems that relies solely on discrete pilot symbols in the time-frequency (TF) domain, positioned at predefined resource elements. We then present theorems to elucidate the relationship between CSF compactness and pilot spacing in the TF domain for accurate CSF acquisition. Based on the estimated CSF, we finally estimate the CTF for data symbols. Numerical results show that, in high-mobility scenarios, the proposed approach outperforms traditional interpolation-based methods and closely matches the optimal estimator in terms of estimation accuracy. This work may pave the way for CSF estimation in commercial OFDM systems, benefiting high-mobility communications, integrated sensing and communications, and related applications.

eess.SP↗