arXiv subjects

Weijie Yuan

Publications and source records attributed to Weijie Yuan.

At least 19 recordsLinked to original sources

arXiv2026

SemISAC: Semantic Integrated Sensing and Communications

Conventional integrated sensing and communications (ISAC) systems primarily integrate communications and sensing through shared physical resources, without explicitly exploiting task-relevant semantic information. To move beyond such physical-level integration, we propose semantic ISAC (SemISAC), a general framework that unifies semantic communication (SemCom) and semantic sensing (SemS) to convey source meaning and acquire environmental meaning. Specifically, the transmitter combines source semantics and sensing task information with available side information to design the shared waveform and allocate radio resources, while the receiver-side communication and sensing task decoders recover the source meaning and infer the required environmental information, respectively. We also provide an information-theoretic interpretation to characterize the relationship between physical and task-relevant information and the resulting semantic trade-off in SemISAC. Building on this framework, we formulate the general SemISAC design problem and propose two realization methods, namely end-to-end (E2E) SemISAC optimization and modular SemISAC optimization. As a concrete realization, we apply modular SemISAC optimization to jointly design a learnable time-frequency (TF) precoder in an orthogonal frequency-division multiplexing (OFDM) system for representative SemCom and SemS tasks. Simulation results demonstrate that the proposed realization reduces sensing semantic distortion under a given communication requirement and achieves a more favorable communication-sensing trade-off than baseline designs.

eess.SP↗

arXiv2026

Successive Refinement Under Strong-Sense Perfect Perception

We revisit a multiterminal lossy source coding problem named successive refinement and derive the rate-distortion-perception region under the strong-sense perfect perception constraint in the presence of unlimited common randomness. Specifically, in successive refinement, one aims to compress a source sequence and allows two distinct decoders to recover the source sequence at different distortion levels. By imposing the strong-sense perfect perception constraint, our results refine the previous result by analyzing the impact of the perceptual quality. Our achievability proof is inspired by output constrained lossy source coding and our converse proof adapts the proof steps of the standard successive refinement problem. Furthermore, we provide a numerical example of the Bernoulli source to illustrate our result and show that the Bernoulli source under Hamming distortion is successively refinable even with the strong-sense perfect perception constraint.

cs.IT↗

arXiv2026

Mobility Information Capacity in the Sky: A Gaussian Channel Perspective

Existing airspace capacity metrics mainly quantify occupancy or flow, although the same number of aerial vehicles may result in different motion alternatives. This letter establishes \emph{mobility information capacity} as an information-theoretic measure for low-altitude wireless networks. It quantifies the maximum information that trajectory observations reveal about intentional maneuver inputs under a given maneuver-resource budget and environmental uncertainty. For a common fixed feedback architecture, we formulate a lifted linear-Gaussian mobility channel and derive its finite-horizon log-determinant capacity. Cost and uncertainty whitening gives the spatiotemporal mobility eigenmodes, whose optimal maneuver-resource allocation follows water-filling. When the number of nondegenerate modes grows linearly with time and their efficiencies become asymptotically symmetric, we arrive at the Shannon-like law $R_M^{\rm G}=\frac{B_M}{2}\log_2(1+\mathrm{MNR})$, where MNR is the mobility-to-noise ratio. The proposed measure opens a motion-centric capacity perspective for the sky, while remaining a distinguishability baseline rather than a collision- or geometry-constrained airspace capacity.

cs.IT↗

arXiv2026

Relativistic Cramér-Rao Bound Scaling for Device-Based and Device-Free Sensing

This letter investigates range and velocity estimation under relativistic motion for device-based (DB) and device-free (DF) sensing. By deriving the exact time-scaling and time-shift relations induced by one-way and two-way propagation, both sensing modes are cast into a unified affine signal model. Closed-form Cramér--Rao bounds (CRBs) are obtained as explicit functions of normalized velocity, root-mean-squared (RMS) bandwidth, and RMS duration. The bounds recover the classical low-speed results but exhibit distinct velocity scaling in the ultrarelativistic regime. For rapidly receding motion, the range CRB diverges while the velocity CRB vanishes. For rapidly approaching motion, both CRBs vanish. The DB and DF modes further exhibit different asymptotic orders in the two directions, showing that relativistic motion changes not only the signal model but also the fundamental scaling laws governing sensing accuracy.

eess.SP↗

arXiv2026

Memory Centric Power Allocation for Multi-Agent Embodied Question Answering

This paper considers multi-agent embodied question answering (MA-EQA), which enables robot teams to answer queries based on their long-horizon observations. In contrast to existing edge resource management methods that optimize sensing, communication, or computation performance metrics, MA-EQA focuses on the quality of aggregated memory. To address this paradigm shift, we propose a quality of memory (QoM) model based on generative adversarial exam (GAE), which leverages forward simulation to evaluate memory retrieval and utilizes the resulting exam scores to quantify QoM. Based on the QoM model, we develop a memory-centric power allocation (MCPA) scheme that maximizes memory quality under communication resource constraints. Through analytical characterization in the noise-limited regime, we reveal a GAE-augmented capped water-filling structure for MCPA. Extensive experiments demonstrate that MCPA achieves significant improvements over existing benchmarks across diverse metrics and scenarios.

cs.RO↗

arXiv2026

How Much Sensing Information Is Needed to Control an Unstable Linear System?

Modern control systems increasingly rely on sensing to infer the system state before control actions can be taken. Yet a given observation mechanism may fail to preserve sufficient information about the unstable modes, regardless of the downstream estimator or controller. This paper asks how much sensing information is needed to estimate and control an unstable linear system, whose measurements are generated by a prescribed, possibly nonlinear and non-Gaussian, observation law p(y_t|x_t). To address this question, we first quantify sensing information using directed information, thereby accounting for causal feedback. We then establish necessary and sufficient information rate conditions for estimating and controlling this linear system. For necessity, keeping either the estimation error or the closed-loop state bounded in mean square requires a directed information rate of at least the open-loop expansion rate R_exp. This lower bound remains valid under additive process noise. Since this rate is difficult to evaluate, we derive computable bounds for nonlinear observations with additive noise. An upper bound below R_exp certifies infeasibility, whereas a lower bound above R_exp + R_NG certifies sufficiency under posterior covariance regularity. For linear Gaussian observations, the tight upper bound is determined by the steady-state Riccati equation. For sufficiency, the posterior non-Gaussianity rate R_NG measures the divergence rate from the covariance-matched Gaussian. Under uniform posterior covariance regularity, a rate above R_exp + R_NG guarantees mean-square convergence of the estimation error. For a stabilizable plant, certainty-equivalence feedback also guarantees mean-square convergence of the closed-loop state. Finally, verifiable curvature conditions on the likelihood and prior make R_NG vanish, so the sufficient threshold equals R_exp.

eess.SY↗

arXiv2026

Semantic Sensing: Toward a Task-Oriented Paradigm

Sensing and communication are fundamental enablers of next-generation networks. While communication technologies have advanced significantly, sensing remains limited to conventional parameter estimation and is far from fully explored. Motivated by these limitations, we propose semantic sensing (SemS), a novel framework that shifts the design objective from reconstruction fidelity to semantic effective recognition. Specifically, we mathematically formulate the interaction between transmit waveforms and semantic entities, thereby establishing SemS as a semantics-oriented transceiver design. Within this architecture, we leverage the information bottleneck (IB) principle as a theoretical criterion to derive a unified objective, guiding the sensing pipeline to maximize task-relevant information extraction. To practically solve this optimization problem, we develop a deep learning (DL)-based framework that jointly designs transmit waveform parameters and receiver representations. The framework is implemented in an orthogonal frequency division multiplexing (OFDM) system, featuring a shared semantic encoder that employs a Gumbel-Softmax-based pilot selector to discretely mask task-irrelevant resources. At the receiver, we design distinct decoding architectures tailored to specific sensing objectives, comprising a 2D residual network (ResNet)-based classifier for target recognition and a correlation-driven 1D regression network for high-precision delay estimation. Numerical results demonstrate that the proposed semantic pilot design achieves superior classification accuracy and ranging precision compared to reconstruction-based baselines, particularly under constrained resource budgets.

eess.SP↗

arXiv2026

Toward Intelligent Skies: Signal Processing and AI Foundations of Low-Altitude Wireless Networks

The rapid growth of low-altitude aerial services and applications, driven by uncrewed aerial vehicles (UAVs), calls for a new class of digital infrastructure beyond conventional terrestrial networks. The low-altitude wireless network (LAWN) has been proposed as dynamically reconfigurable three-dimensional architectures that integrate aerial and ground nodes to provide connectivity, sensing, and control in open, safety-critical airspace. This tutorial presents a comprehensive treatment of LAWNs from the joint perspectives of artificial intelligence (AI) and signal processing. We first review the historical evolution and architectural foundations of LAWNs, introducing altitude-based layers and functional planes, and summarizing the regulatory and standardization landscape. Building on this system view, we then discuss signal processing fundamentals for LAWNs, including 3D channel and system models, performance metrics, waveform and receiver design, localization and tracking, and multi-functionality co-design. Next, we survey AI techniques for LAWNs, covering discriminative and generative models for perception, control, resource management, and security, as well as emerging paradigms such as foundation models, large language models, and digital twins for mission planning and closed-loop optimization. To illustrate AI-signal processing integration in practice, we provide a case study of an AI-driven multi-tier LAWN with hybrid satellite, high-altitude, and ground nodes. The tutorial concludes by outlining key research challenges in architecture design, signal processing-AI co-design, safety and security, experimentation, and standardization, and by highlighting opportunities for LAWNs to evolve into dependable, AI-native infrastructure for the intelligent skies.

eess.SP↗

arXiv2026

Zak-OTFS ISAC with Bistatic Sensing via Semi-Blind Atomic Norm Denoising Scheme

Integrated sensing and communication (ISAC) through Zak-transform-based orthogonal time frequency space (Zak-OTFS) modulation is a promising solution for high-mobility scenarios. Realizing accurate bistatic sensing and robust communication necessitates precise channel estimation; however, this remains a formidable challenge in doubly dispersive environments, where fractional delay-Doppler shifts induce severe channel spreading. This paper proposes a semi-blind atomic norm denoising scheme for Zak-OTFS ISAC with bistatic sensing. We first derive the discrete-time input-output (I/O) relationship of Zak-OTFS under fractional delay-Doppler shifts and rectangular windowing. Based on this I/O relation, we formulate the joint channel parameter estimation and data detection task as an atomic norm denoising problem, utilizing the negative square penalty method to handle the non-convex discrete constellation constraints. To solve this problem efficiently, we develop an accelerated iterative algorithm that integrates majorization-minimization, accelerated projected gradient, and inexact accelerated proximal gradient methods. We provide a rigorous convergence proof for the proposed algorithm. Simulation results demonstrate that the proposed scheme achieves super-resolution sensing accuracy and communication performance approaching the perfect channel state information lower bound.

eess.SP↗

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 Cramér-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 $λ_{t}D_{f}\ln^{-2}J$. 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

Sense Smarter, Think Better: A Survey on Edge Perception for Next-Generation Networks

Edge perception has emerged as a foundational capability for future wireless networks, enabling the network edge to proactively sense, interpret, and interact with the physical environment in a task-oriented and resource-aware manner. This survey provides a comprehensive and structured overview of edge perception. We first review representative sensing modalities and edge artificial intelligence (AI) techniques as the fundamental building blocks. We then examine their synergistic interactions. We systematically analyze how edge AI enhances sensing capabilities, encompassing both in-band and out-of-band modalities, as well as multi-modal sensor data fusion. Moreover, we discuss the role of task-driven sensing in facilitating edge AI, including integrated sensing-communication-computation designs, and active perception frameworks that dynamically adapt sensing strategies for downstream applications. Finally, we identify key challenges and open issues. By consolidating fragmented research across sensing, communication, and edge AI, this survey provides forward-looking insights for the design and implementation of edge perception systems for sixth-generation (6G) networks.

eess.SP↗

arXiv2026

LAWNs Meet SWIPT: Beamforming and Power Splitting Optimization for Predictive Control

Simultaneous wireless information and power transfer (SWIPT) has emerged as a promising paradigm for enabling sustainable connectivity in battery-limited low-altitude wireless networks (LAWNs). This paper investigates a SWIPT-enabled LAWN system in which a multi-antenna base station (BS) simultaneously delivers control information and wireless energy to a fleet of uncrewed aircraft systems (UASs) via power splitting. In particular, the BS remotely guides the UASs to accurately track predefined reference trajectories toward their destinations while avoiding multiple mobile no-fly zones (NFZs). To guarantee collision-free path planning, we first construct smooth and safe reference trajectories using stream function theory. Then, a real-time optimization problem is formulated, which jointly takes into account the wireless control cost and energy sustainability by optimizing control inputs, transmit beamforming vectors, and the power splitting ratios. To address the resultant non-convex problem, a two-stage optimization framework is proposed. First, we develop a model predictive control (MPC)-based method to generate predictive control inputs. Subsequently, we derive a computationally efficient iterative algorithm to optimize the beamforming vectors and power splitting ratios by applying semidefinite relaxation (SDR) and successive convex approximation (SCA) techniques. We further prove that the SDR is tight for our formulation. Extensive numerical results demonstrate that our proposed design significantly outperforms benchmark schemes in terms of tracking accuracy and harvested energy, thereby validating its effectiveness for sustainable implementation in LAWN systems.

eess.SY↗

arXiv2026

From Ground to Sky: Architectures, Applications, and Challenges Shaping Low-Altitude Wireless Networks

In this article, we introduce a novel low-altitude wireless network (LAWN), which is a reconfigurable, three-dimensional (3D) layered architecture. In particular, the LAWN integrates connectivity, sensing, control, and computing across aerial and terrestrial nodes that enable seamless operation in complex, dynamic, and mission-critical environments. Different from the conventional aerial communication systems, LAWN's distinctive feature is its tight integration of functional planes in which multiple functionalities continually reshape themselves to operate safely and efficiently in the low-altitude sky. With the LAWN, we discuss several enabling technologies, such as integrated sensing and communication (ISAC), semantic communication, and fully-actuated control systems. Finally, we identify potential applications and key cross-layer challenges. This article offers a comprehensive roadmap for future research and development in the low-altitude airspace.

eess.SP↗

arXiv2026

Predictive Control over Low-Altitude Wireless Networks: Joint Trajectory Design and Resource Allocation

Low-altitude wireless networks (LAWNs) have been envisioned as flexible and transformative platforms for enabling delay-sensitive control applications in Internet of Things (IoT) systems. In this work, we investigate the real-time wireless control over LAWNs, where an aerial drone is employed to serve multiple mobile automated guided vehicles (AGVs) via finite blocklength (FBL) transmission. Toward this end, we adopt the model predictive control (MPC) to ensure accurate trajectory tracking, while we analyze the communication reliability using the outage probability. Subsequently, we formulate an optimization problem to jointly determine control policy, transmit power allocation, and drone trajectory by accounting for the maximum travel distance and control input constraints. To address the resultant non-convex optimization problem, we first derive the closed-form expression of the outage probability under FBL transmission. Based on this, we reformulate the original problem as a quadratic programming (QP) problem, followed by developing an alternating optimization (AO) framework. Specifically, we employ the projected gradient descent (PGD) method and the successive convex approximation (SCA) technique to achieve computationally efficient sub-optimal solutions. Furthermore, we thoroughly analyze the convergence and computational complexity of the proposed algorithm. Extensive simulations and AirSim-based experiments are conducted to validate the superiority of our proposed approach compared to the baseline schemes in terms of control performance.

eess.SP↗

arXiv2026

Low-Altitude Wireless Networks: The Next Horizon of Wireless Infrastructure

Low-altitude airspace, roughly defined as the region up to 3000 meters above ground level, is envisioned as a new spatial domain for daily human and machine activities. This article introduces the concept of the Low-Altitude Wireless Network (LAWN), which represents a paradigm shift from the current ground-based communication-only network to a three-dimensional (3D) multifunctional network. We analyze the key driving forces, network architecture, and limiting factors of LAWN, with a particular focus on the tight integration of communication, sensing, and control in highly dynamic airspace environments. By establishing the coupling between airspace capacity and wireless channel capacity, we reveal the intrinsic limits of airspace management and identify the fundamental challenges and opportunities associated with its evolution.

cs.NI↗

arXiv2026

Delay-Doppler Domain Signal Processing Aided OFDM (DD-a-OFDM) for 6G and Beyond

High-mobility scenarios will be a critical part of 6G systems. Since the widely deployed orthogonal frequency division multiplexing (OFDM) waveform suffers from subcarrier orthogonality loss under severe Doppler spread, delay-Doppler domain multi-carrier (DDMC) modulation systems, such as orthogonal time frequency space (OTFS), have been extensively studied. While OTFS can exploit time-frequency (TF) domain channel diversity, it faces challenges including high receiver complexity and inflexible TF resource allocation, making OFDM still the most promising waveform for 6G. In this article, we propose a DD domain signal processing-aided OFDM (DD-a-OFDM) scheme to enhance OFDM performance based on DDMC research insights. First, we design a DD-a-OFDM system structure, retaining the classical OFDM transceiver while incorporating DD domain channel estimation and TF domain equalization. Second, we detail DD domain channel estimation using discrete TF pilots and prove that TF domain inter-carrier interference (ICI) could be transformed into DD domain Gaussian interference. Third, we derive closed-form Cramér-Rao lower bounds (CRLBs) for DD domain channel estimation. Fourth, we develop maximum likelihood (ML) and peak detection-based channel estimators, along with a corresponding TF domain equalizer. Numerical results verify the proposed design, showing that DD-a-OFDM reduces the bit-error rate (BER) compared to classical OFDM and outperforms OTFS in channel estimation accuracy with lower pilot overhead.

eess.SP↗

arXiv2026

Unveiling Uncertainty-Aware Autonomous Cooperative Learning Based Planning Strategy

In future intelligent transportation systems, autonomous cooperative planning (ACP), becomes a promising technique to increase the effectiveness and security of multi-vehicle interactions. However, multiple uncertainties cannot be fully addressed for existing ACP strategies, e.g. perception, planning, and communication uncertainties. To address these, a novel deep reinforcement learning-based autonomous cooperative planning (DRLACP) framework is proposed to tackle various uncertainties on cooperative motion planning schemes. Specifically, the soft actor-critic (SAC) with the implementation of gate recurrent units (GRUs) is adopted to learn the deterministic optimal time-varying actions with imperfect state information occurred by planning, communication, and perception uncertainties. In addition, the real-time actions of autonomous vehicles (AVs) are demonstrated via the Car Learning to Act (CARLA) simulation platform. Evaluation results show that the proposed DRLACP learns and performs cooperative planning effectively, which outperforms other baseline methods under different scenarios with imperfect AV state information.

cs.RO↗

arXiv2026

Low-Altitude Wireless Networks: A Comprehensive Survey

The rapid development of the low-altitude economy has imposed unprecedented demands on wireless infrastructure to accommodate large-scale drone deployments and facilitate intelligent services in dynamic airspace environments. However, unlocking its full potential in practical applications presents significant challenges. Traditional aerial systems predominantly focus on air-ground communication services, often neglecting the integration of sensing, computation, control, and energy-delivering functions, which hinders the ability to meet diverse mission-critical demands. Besides, the absence of systematic low-altitude airspace planning and management exacerbates issues regarding dynamic interference in three-dimensional space, coverage instability, and scalability. To overcome these challenges, a comprehensive framework, termed low-altitude wireless network (LAWN), has emerged to seamlessly integrate communication, sensing, computation, control, and air traffic management into a unified design. This article provides a comprehensive overview of LAWN systems, introducing LAWN system fundamentals and the evolution of functional designs. Subsequently, we delve into performance evaluation metrics and review critical concerns surrounding privacy and security in the open-air network environment. Finally, we present the cutting-edge developments in airspace structuring and air traffic management, providing insights to facilitate the practical deployment of LAWNs.

eess.SP↗