arXiv subjects

Weijie Yuan

Publications and source records attributed to Weijie Yuan.

At least 73 records · Page 4Linked to original sources

arXiv2025

Resolution Limits of Non-Adaptive 20 Questions Estimation for Tracking Multiple Moving Targets

Motivated by the practical application of beam tracking of multiple devices in Multiple Input Multiple Output (MIMO) communication, we study the problem of non-adaptive twenty questions estimation for locating and tracking multiple moving targets under a query-dependent noisy channel. Specifically, we derive a non-asymptotic bound and a second-order asymptotic bound on resolution for optimal query procedures and provide numerical examples to illustrate our results. In particular, we demonstrate that the bound is achieved by a state estimator that thresholds the mutual information density over possible target locations. This single threshold decoding rule has reduced the computational complexity compared to the multiple threshold scheme proposed for locating multiple stationary targets (Zhou, Bai and Hero, TIT 2022). We discuss two special cases of our setting: the case with unknown initial location and known velocity, and the case with known initial location and unknown velocity. Both cases share the same theoretical benchmark {that applies to} stationary multiple target search in Zhou, Bai and Hero (TIT 2022) while the known initial location case is close to the theoretical benchmark for stationary target search when the maximal speed is inversely proportional to the number of queries. We also generalize our results to account for a piecewise constant velocity model introduced in Zhou and Hero (TIT 2023), where targets change velocity periodically. Finally, we illustrate our proposed algorithm for the application of beam tracking of multiple mobile transmitters in a 5G wireless network.

cs.IT↗

arXiv2025

SDR-Empowered Environment Sensing Design and Experimental Validation Using OTFS-ISAC Signals

This paper investigates the system design and experimental validation of integrated sensing and communication (ISAC) for environmental sensing, which is expected to be a critical enabler for next-generation wireless networks. We advocate exploiting orthogonal time frequency space (OTFS) modulation for its inherent sparsity and stability in delay-Doppler (DD) domain channels, facilitating a low-overhead environment sensing design. Moreover, a comprehensive environmental sensing framework is developed, encompassing DD domain channel estimation, target localization, and experimental validation. In particular, we first explore the OTFS channel estimation in the presence of fractional delay and Doppler shifts. Given the estimated parameters, we propose a three-ellipse positioning algorithm to localize the target's position, followed by determining the mobile transmitter's velocity. Additionally, to evaluate the performance of our proposed design, we conduct extensive simulations and experiments using a software-defined radio (SDR)-based platform with universal software radio peripheral (USRP). The experimental validations demonstrate that our proposed approach outperforms the benchmarks in terms of localization accuracy and velocity estimation, confirming its effectiveness in practical environmental sensing applications.

eess.SP↗

arXiv2025

Co-Design of Sensing, Communications, and Control for Low-Altitude Wireless Networks

The rapid advancement of Internet of Things (IoT) services and the evolution toward the sixth generation (6G) have positioned unmanned aerial vehicles (UAVs) as critical enablers of low-altitude wireless networks (LAWNs). This work investigates the co-design of integrated sensing, communication, and control ($\mathbf{SC^{2}}$) for multi-UAV cooperative systems with finite blocklength (FBL) transmission. In particular, the UAVs continuously monitor the state of the field robots and transmit their observations to the robot controller to ensure stable control while cooperating to localize an unknown sensing target (ST). To this end, a weighted optimization problem is first formulated by jointly considering the control and localization performance in terms of the linear quadratic regulator (LQR) cost and the determinant of the Fisher information matrix (FIM), respectively. The resultant problem, optimizing resource allocations, the UAVs' deployment positions, and multi-user scheduling, is non-convex. To circumvent this challenge, we first derive a closed-form expression of the LQR cost with respect to other variables. Subsequently, the non-convex optimization problem is decomposed into a series of sub-problems by leveraging the alternating optimization (AO) approach, in which the difference of convex functions (DC) programming and projected gradient descent (PGD) method are employed to obtain an efficient near-optimal solution. Furthermore, the convergence and computational complexity of the proposed algorithm are thoroughly analyzed. Extensive simulation results are presented to validate the effectiveness of our proposed approach compared to the benchmark schemes and reveal the trade-off between control and sensing performance.

eess.SP↗

arXiv2025

Multimodal Large Language Models-Enabled UAV Swarm: Towards Efficient and Intelligent Autonomous Aerial Systems

Recent breakthroughs in multimodal large language models (MLLMs) have endowed AI systems with unified perception, reasoning and natural-language interaction across text, image and video streams. Meanwhile, Unmanned Aerial Vehicle (UAV) swarms are increasingly deployed in dynamic, safety-critical missions that demand rapid situational understanding and autonomous adaptation. This paper explores potential solutions for integrating MLLMs with UAV swarms to enhance the intelligence and adaptability across diverse tasks. Specifically, we first outline the fundamental architectures and functions of UAVs and MLLMs. Then, we analyze how MLLMs can enhance the UAV system performance in terms of target detection, autonomous navigation, and multi-agent coordination, while exploring solutions for integrating MLLMs into UAV systems. Next, we propose a practical case study focused on the forest fire fighting. To fully reveal the capabilities of the proposed framework, human-machine interaction, swarm task planning, fire assessment, and task execution are investigated. Finally, we discuss the challenges and future research directions for the MLLMs-enabled UAV swarm. An experiment illustration video could be found online at https://youtu.be/zwnB9ZSa5A4.

cs.RO↗

arXiv2025

Faster-than-Nyquist Signaling is Good for Single-Carrier ISAC: An Analytical Study

In this paper, we provide an analytical study of single-carrier faster-than-Nyquist (FTN) signaling for integrated sensing and communications (ISAC). Our derivations show that FTN is advantageous for ISAC, and reveal new insights that these advantages come from the fact that FTN signaling can effectively avoid the spectral aliasing due to the mismatch between the symbol rate and the bandwidth of the shaping pulse. Specifically, the communication spectral efficiency advantages of FTN signaling over time-invariant multipath channels are analytically shown, where both upper- and lower-bounds on the spectral efficiency are derived. We show that the gap between these two bounds corresponds to the potential signal-to-noise ratio (SNR) variation due to the presence of multipath delay and spectral aliasing, which diminishes as the symbol rate grows higher. Particularly, in the limiting case, this SNR variation disappears while the degree of freedom (DoF) of the system attain the maximum. Furthermore, the sensing advantages for FTN signals are verified in terms of the expected normalized squared ambiguity function. We show that FTN signals generally enjoy a more robust ranging performance. More importantly, we prove that FTN signaling can effectively avoid the undesired peaks in the considered ambiguity function along the Doppler dimension, thereby reducing the ambiguities in velocity estimation. All these conclusions are explicitly verified by numerical results.

cs.IT↗

arXiv2025

Towards Intelligent Edge Sensing for ISCC Network: Joint Multi-Tier DNN Partitioning and Beamforming Design

The combination of Integrated Sensing and Communication (ISAC) and Mobile Edge Computing (MEC) enables devices to simultaneously sense the environment and offload data to the base stations (BS) for intelligent processing, thereby reducing local computational burdens. However, transmitting raw sensing data from ISAC devices to the BS often incurs substantial fronthaul overhead and latency. This paper investigates a three-tier collaborative inference framework enabled by Integrated Sensing, Communication, and Computing (ISCC), where cloud servers, MEC servers, and ISAC devices cooperatively execute different segments of a pre-trained deep neural network (DNN) for intelligent sensing. By offloading intermediate DNN features, the proposed framework can significantly reduce fronthaul transmission load. Furthermore, multiple-input multiple-output (MIMO) technology is employed to enhance both sensing quality and offloading efficiency. To minimize the overall sensing task inference latency across all ISAC devices, we jointly optimize the DNN partitioning strategy, ISAC beamforming, and computational resource allocation at the MEC servers and devices, subject to sensing beampattern constraints. We also propose an efficient two-layer optimization algorithm. In the inner layer, we derive closed-form solutions for computational resource allocation using the Karush-Kuhn-Tucker conditions. Moreover, we design the ISAC beamforming vectors via an iterative method based on the majorization-minimization and weighted minimum mean square error techniques. In the outer layer, we develop a cross-entropy based probabilistic learning algorithm to determine an optimal DNN partitioning strategy. Simulation results demonstrate that the proposed framework substantially outperforms existing two-tier schemes in inference latency.

eess.SP↗

arXiv2025

Low-Complexity Channel Estimation in OTFS Systems with Fractional Effects

Orthogonal Time Frequency Space (OTFS) modulation exploits the sparsity of Delay-Doppler domain channels, making it highly effective in high-mobility scenarios. Its accurate channel estimation supports integrated sensing and communication (ISAC) systems. The letter introduces a low-complexity technique for estimating delay and Doppler shifts under fractional effects, while addressing inter-path interference. The method employs a sequential estimation process combined with interference elimination based on energy leakage, ensuring accurate channel estimation. Furthermore, the estimated channel parameters can signifcantly improve ISAC system performance by enhancing sensing capabilities. Experimental results validate the effectiveness of this approach in achieving accurate channel estimation and facilitating sensing tasks for ISAC systems.

eess.SP↗

arXiv2025

Sensing With Random Communication Signals

Communication-centric Integrated Sensing and Communication (ISAC) has been recognized as a promising methodology to implement wireless sensing functionality over existing network architectures, due to its cost-effectiveness and backward compatibility to legacy cellular systems. However, the inherent randomness of the communication signal may incur huge fluctuations in sensing capabilities, leading to unfavorable detection and estimation performance. To address this issue, we elaborate on random ISAC signal processing methods in this article, aiming at improving the sensing performance without unduly deteriorating the communication functionality. Specifically, we commence by discussing the fundamentals of sensing with random communication signals, including the performance metrics and optimal ranging waveforms. Building on these concepts, we then present a general framework for random ISAC signal transmission, followed by an in-depth exploration of time-domain pulse shaping, frequency-domain constellation shaping, and spatial-domain precoding methods. We provide a comprehensive overview of each of these topics, including models, results, and design guidelines. Finally, we conclude this article by identifying several promising research directions for random ISAC signal transmission.

eess.SP↗

arXiv2025

Integration of SDN and Digital Twin for the Intelligent Detection of DoC Attacks in WRSNs

Wireless rechargeable sensor networks (WRSNs), supported by recent advancements in wireless power transfer (WPT) technology, hold significant potential for extending network lifetime. However, traditional approaches often prioritize scheduling algorithms and network optimization, overlooking the security risks associated with the charging process, which exposes the network to potential attacks. This paper addresses this gap by integrating Software-Defined Networking (SDN) and Digital Twin technologies for the intelligent detection of Denial of Charging (DoC) attacks in WRSNs. First, it leverages the flexibility and intelligent control of SDN, in combination with Digital Twin, to enhance real-time detection and mitigation of DoC attacks. Second, it employs four key metrics to detect such attacks including charging request patterns, energy consumption, behavioral and reputation scores, and charging behavior and efficiency. The numerical results demonstrate the superior performance of the proposed protocol in terms of energy usage efficiency, survival rate, detection rate, and travel distance.

cs.NI↗

arXiv2025

Uncovering the Iceberg in the Sea: Fundamentals of Pulse Shaping and Modulation Design for Random ISAC Signals

Integrated Sensing and Communications (ISAC) is expected to play a pivotal role in future 6G networks. To maximize time-frequency resource utilization, 6G ISAC systems must exploit data payload signals, that are inherently random, for both communication and sensing tasks. This paper provides a comprehensive analysis of the sensing performance of such communication-centric ISAC signals, with a focus on modulation and pulse shaping design to reshape the statistical properties of their auto-correlation functions (ACFs), thereby improving the target ranging performance. We derive a closed-form expression for the expectation of the squared ACF of random ISAC signals, considering arbitrary modulation bases and constellation mappings within the Nyquist pulse shaping framework. The structure is metaphorically described as an ``iceberg hidden in the sea", where the ``iceberg'' represents the squared mean of the ACF of random ISAC signals, that is determined by the pulse shaping filter, and the ``sea level'' characterizes the corresponding variance, caused by the randomness of the data payload. Our analysis shows that, for QAM/PSK constellations with Nyquist pulse shaping, Orthogonal Frequency Division Multiplexing (OFDM) achieves the lowest ranging sidelobe level across all lags. Building on these insights, we propose a novel Nyquist pulse shaping design to enhance the sensing performance of random ISAC signals. Numerical results validate our theoretical findings, showing that the proposed pulse shaping significantly reduces ranging sidelobes compared to conventional root-raised cosine (RRC) pulse shaping, thereby improving the ranging performance.

eess.SP↗

arXiv2024

Unveiling the Potential of NOMA: A Journey to Next Generation Multiple Access

Revolutionary sixth-generation wireless communications technologies and applications, notably digital twin networks (DTN), connected autonomous vehicles (CAVs), space-air-ground integrated networks (SAGINs), zero-touch networks, industry 5.0, and healthcare 5.0, are driving next-generation wireless networks (NGWNs). These technologies generate massive data, requiring swift transmission and trillions of device connections, fueling the need for sophisticated next-generation multiple access (NGMA) schemes. NGMA enables massive connectivity in the 6G era, optimizing NGWN operations beyond current multiple access (MA) schemes. This survey showcases non-orthogonal multiple access (NOMA) as NGMA's frontrunner, exploring What has NOMA delivered?, What is NOMA providing?, and What lies ahead?. We present NOMA variants, fundamental operations, and applicability in multi-antenna systems, machine learning, reconfigurable intelligent surfaces (RIS), cognitive radio networks (CRN), integrated sensing and communications (ISAC), terahertz networks, and unmanned aerial vehicles (UAVs). Additionally, we explore NOMA's interplay with state-of-the-art wireless technologies, highlighting its advantages and technical challenges. Finally, we unveil NOMA research trends in the 6G era and provide design recommendations and future perspectives for NOMA as the leading NGMA solution for NGWNs.

cs.IT↗

arXiv2024

Low-Complexity Minimum BER Precoder Design for ISAC Systems: A Delay-Doppler Perspective

Orthogonal time frequency space (OTFS) modulation is anticipated to be a promising candidate for supporting integrated sensing and communications (ISAC) systems, which is considered as a pivotal technique for realizing next generation wireless networks. In this paper, we develop a minimum bit error rate (BER) precoder design for an OTFS-based ISAC system. In particular, the BER minimization problem takes into account the maximum available transmission power budget and the required sensing performance. Different from prior studies that considered ISAC in the time-frequency (TF) domain, we devise the precoder from the perspective of the delay-Doppler (DD) domain by exploiting the equivalent DD domain channel due to the fact that the DD domain channel generally tends to be sparse and quasi-static, which can facilitate a low-overhead ISAC system design. To address the non-convex optimization design problem, we resort to optimizing the lower bound of the derived average BER by adopting Jensen's inequality. Subsequently, the formulated problem is decoupled into two independent sub-problems via singular value decomposition (SVD) methodology. We then theoretically analyze the feasibility conditions of the proposed problem and present a low-complexity iterative solution via leveraging the Lagrangian duality approach. Simulation results verify the effectiveness of our proposed precoder compared to the benchmark schemes and reveal the interplay between sensing and communication for dual-functional precoder design, indicating a trade-off where transmission efficiency is sacrificed for increasing transmission reliability and sensing accuracy.

eess.SP↗

arXiv2024

E-Healthcare Systems: Integrated Sensing, Computing, and Semantic Communication with Physical Layer Security

This paper introduces an integrated sensing, computing, and semantic communication (ISCSC) framework tailored for smart healthcare systems. The framework is evaluated in the context of smart healthcare, optimising the transmit beamforming matrix and semantic extraction ratio for improved data rates, sensing accuracy, and general data protection regulation (GDPR) compliance, while considering IoRT device computing capabilities. Semantic metrics such as semantic transmission rate and semantic secrecy rate are derived to evaluate data rate performance and GDPR risk, respectively, while the Cramér-Rao Bound (CRB) assesses sensing performance. Simulation results demonstrate the framework's effectiveness in ensuring reliable sensing, high data rates, and secure communication.

eess.SP↗

arXiv2024

Symbiotic Sensing and Communication: Framework and Beamforming Design

In this paper, we propose a novel symbiotic sensing and communication (SSAC) framework, comprising a base station (BS) and a passive sensing node. In particular, the BS transmits communication waveform to serve vehicle users (VUEs), while the sensing node is employed to execute sensing tasks based on the echoes in a bistatic manner, thereby avoiding the issue of self-interference. Besides the weak target of interest, the sensing node tracks VUEs and shares sensing results with BS to facilitate sensing-assisted beamforming. By considering both fully digital arrays and hybrid analog-digital (HAD) arrays, we investigate the beamforming design in the SSAC system. We first derive the Cramer-Rao lower bound (CRLB) of the two-dimensional angles of arrival estimation as the sensing metric. Next, we formulate an achievable sum rate maximization problem under the CRLB constraint, where the channel state information is reconstructed based on the sensing results. Then, we propose two penalty dual decomposition (PDD)-based alternating algorithms for fully digital and HAD arrays, respectively. Simulation results demonstrate that the proposed algorithms can achieve an outstanding data rate with effective localization capability for both VUEs and the weak target. In particular, the HAD beamforming design exhibits remarkable performance gain compared to conventional schemes, especially with fewer radio frequency chains.

eess.SP↗

arXiv2024

Near-field Beam training for Extremely Large-scale MIMO Based on Deep Learning

Extremely Large-scale Array (ELAA) is considered a frontier technology for future communication systems, pivotal in improving wireless systems' rate and spectral efficiency. As ELAA employs a multitude of antennas operating at higher frequencies, users are typically situated in the near-field region where the spherical wavefront propagates. The near-field beam training in ELAA requires both angle and distance information, which inevitably leads to a significant increase in the beam training overhead. To address this problem, we propose a near-field beam training method based on deep learning. We use a convolutional neural network (CNN) to efficiently learn channel characteristics from historical data by strategically selecting padding and kernel sizes. The negative value of the user average achievable rate is utilized as the loss function to optimize the beamformer. This method maximizes multi-user networks' achievable rate without predefined beam codebooks. Upon deployment, the model requires solely the pre-estimated channel state information (CSI) to derive the optimal beamforming vector. The simulation results demonstrate that the proposed scheme achieves a more stable beamforming gain and significantly improves performance compared to the traditional beam training method. Furthermore, owing to the inherent traits of deep learning methodologies, this approach substantially diminishes the near-field beam training overhead.

cs.LG↗

arXiv2024

Pulse Shaping for Random ISAC Signals: The Ambiguity Function Between Symbols Matters

Integrated sensing and communications (ISAC) has emerged as a pivotal enabling technology for next-generation wireless networks. Despite the distinct signal design requirements of sensing and communication (S&C) systems, shifting the symbol-wise pulse shaping (SWiPS) framework from communication-only systems to ISAC poses significant challenges in signal design and processing This paper addresses these challenges by examining the ambiguity function (AF) of the SWiPS ISAC signal and introducing a novel pulse shaping design for single-carrier ISAC transmission. We formulate optimization problems to minimize the average integrated sidelobe level (ISL) of the AF, as well as the weighted ISL (WISL) while satisfying inter-symbol interference (ISI), out-of-band emission (OOBE), and power constraints. Our contributions include establishing the relationship between the AFs of both the random data symbols and signaling pulses, analyzing the statistical characteristics of the AF, and developing algorithmic frameworks for pulse shaping optimization using successive convex approximation (SCA) and alternating direction method of multipliers (ADMM) approaches. Numerical results are provided to validate our theoretical analysis, which demonstrate significant performance improvements in the proposed SWiPS design compared to the root-raised cosine (RRC) pulse shaping for conventional communication systems.

eess.SP↗

arXiv2024

Beamforming Design for Secure MC-NOMA Empowered ISAC Systems with an Active Eve

As the integrated sensing and communication(ISAC) technology emerges as a promising component of sixth generation (6G), the study of its physical layer security has become a key concern for researchers. Specifically, in this work, we focus on the security issues over a multi-carrier (MC)-non-orthogonal multiple access (NOMA) assisted ISAC system, considering imperfect channel state information (CSI) of an active Eve and graded confidentiality demands for users. To this end, the subcarrier allocation, the information, and artificial noise beamforming are designed to maximize the minimum communication rate, while ensuring diverse confidentiality and sensing performance demands. An effective security strategy is devised via the Lagrangian dual transformation and successive convex approximation methods. Simulations confirm the validity and robustness of the proposed scheme in terms of the security performance.

cs.IT↗

arXiv2024

ISAC-Assisted Wireless Rechargeable Sensor Networks with Multiple Mobile Charging Vehicles

As IoT-based wireless sensor networks (WSNs) become more prevalent, the issue of energy shortages becomes more pressing. One potential solution is the use of wireless power transfer (WPT) technology, which is the key to building a new shape of wireless rechargeable sensor networks (WRSNs). However, efficient charging and scheduling are critical for WRSNs to function properly. Motivated by the fact that probabilistic techniques can help enhance the effectiveness of charging scheduling for WRSNs, this article addresses the aforementioned issue and proposes a novel ISAC-assisted WRSN protocol. In particular, our proposed protocol considers several factors to balance the charging load on each mobile charging vehicle (MCV), uses an efficient charging factor strategy to partially charge network devices, and employs the ISAC concept to reduce the traveling cost of each MCV and prevent charging conflicts. Simulation results demonstrate that this protocol outperforms other classic, cutting-edge protocols in multiple areas.

cs.NI↗