OTFSensi: OTFS Sensing for Human Activity Recognition in Future 6G Networks Xinyuan Wei, Weijie Yuan, Kecheng Zhang, Qin Tao, Fan Liu, Rui Wang IEEE Transactions on Mobile Computing, 2026 Wireless sensing enables contactless and accurate recognition of human activities and physiological states by using electromagnetic signals. As a promising enabler for sixth-generation (6 G) multi-functional networks, orthogonal time frequency space (OTFS) modulation exhibits strong resilience to high Doppler shifts in high-mobility environments, while also supporting precise human sensing in low-mobility scenarios. In this work, we propose a novel two-dimensional (2D) delay-Doppler motion profiling framework based on the OTFS waveform to extract distinctive features of human activities. To enhance recognition performance, a fractional-Doppler enhancement network is integrated with a convolutional neural network (CNN)-aided encoder-only Transformer architecture. Extensive experiments are conducted to assess the cross-domain generalization capability of the proposed OTFSensi system. Compared with existing classification models based on CNN, gated recurrent unit (GRU), and long short-term memory (LSTM) networks, OTFSensi demonstrates substantial improvements in adaptability across diverse environments and observation angles. Furthermore, a comparative analysis with various radio frequency (RF) sensing technologies confirms the superior classification performance achieved by OTFSensi.
Robust and Extensible Multi-Branch Semantic Communication in LAWNs: Deployment-Efficient Design With SDR-Based Validation Guixiong Chen, Hongjia Huang, Xinyuan Wei, Ruizhi Ruan, Yuanhao Cui, Weijie Yuan IEEE Transactions on Mobile Computing, 2026 Semantic communication is increasingly recognized as a promising paradigm for enhancing the communication capabilities of wireless systems in the 6G era. Existing deep learning (DL)-based semantic methods typically enhance system robustness through module-centric strategies, where additional components are integrated into the model. Due to the significant computational overhead, these strategies are often unsuitable for resource-limited systems, such as the emerging low-altitude wireless networks (LAWNs). Moreover, most existing methods are optimized for fixed channel models and lack architectural adaptability across diverse environments, leading to repeated retraining and increased maintenance complexity. To address these challenges, we propose a novel Dual-Branch Architecture (DBA) for semantic communication. DBA employs a training-only auxiliary decoder branch to provide noise-free supervision for the main decoder, thereby enhancing robustness without adding deployment or inference overhead. Specifically, we introduce a contrastive learning mechanism to align the outputs of the noisy and noise-free branches, reinforcing semantic consistency. Building on this, we further propose the Extensible Multi-Channel Architecture (EMCA), a multi-branch design that incorporates multiple decoder branches optimized for different channel models and jointly trains them with a shared encoder and auxiliary branch, improving scalability without duplicating model parameters. Simulation results demonstrate that DBA and EMCA consistently outperform existing baselines in terms of semantic fidelity across additive white Gaussian noise (AWGN), Rayleigh, and Rician fading channels, without incurring any additional inference cost. Additionally, experiments conducted using a software-defined radio (SDR)-based platform with universal software radio peripheral (USRP) further validate the robustness and practicality of the proposed methods in practical environmental applications.
Cargo UAVs Pick-Up Systems for Low-Altitude Economy With Communication Quality, Battery Energy, and Time Window Constraints Mingjian Chen, Liang Yang, Jiangling Cao, Guangxu Zhu, Weijie Yuan, Hongbo Jiang, Dusit Niyato IEEE Transactions on Mobile Computing, 2026 The rapid development of the low-altitude economy (LAE) has accelerated the deployment of cargo unmanned aerial vehicles (UAVs) for intelligent logistics and delivery services. However, large-scale UAV operations still face multiple practical challenges, including unstable communication connectivity, limited onboard battery energy, and strict customer time-window constraints. To address these issues, this paper investigates the trajectory and task scheduling optimization problem for multi-UAV cooperative cargo pick-up under joint communication, energy, and time-window constraints. We develop a collision-aware cooperative multi-UAV optimization algorithm (CACMO) that integrates a Dueling Deep Q-Network (D3QN) for communication-aware trajectory learning with a simulated annealing (SA) based global task-sequence planner and an explicit inter-UAV conflict-resolution mechanism. The D3QN module enables adaptive trajectory generation in unknown and time-varying radio environments without requiring an a priori radio map, maintaining stable connectivity while reducing flight cost, whereas the SA module determines efficient task orders and enforces safe coordination among multiple UAVs through collision-aware refinement. Simulation results demonstrate that the proposed CACMO algorithm framework achieves an optimal balance between task completion time (1,719 seconds) and user satisfaction (score of 0.9969) under typical operating conditions, delivering a 70–75% reduction in total weighted cost compared to representative baseline methods. Crucially, this substantial improvement is achieved while explicitly enforcing multi-UAV collision avoidance-a critical constraint absent in most baseline methods. The framework maintains zero communication outage and guarantees safe inter-UAV separation throughout the mission while satisfying all energy and time window constraints in realistic urban environments, confirming its robustness and scalability for cooperative multi-UAV logistics operations within the LAE.
Sparse Bayesian Learning-Based Grating Lobe Suppression for DoA Estimation in Mobile ISAC Networks Chenglin Huang, Kaikai Liu, Zengshan Tian, Jiacheng Wang, Weijie Yuan IEEE Transactions on Mobile Computing, 2026 Integrated sensing and communication (ISAC) utilizes existing communication devices for sensing and is emerging as a key technology in wireless networks, particularly for mobile applications such as vehicular networks. Most systems rely on path parameters, such as direction of arrival (DoA), for accurate sensing. However, commercial communication devices often adopt wider antenna spacings to enhance communication performance, which can lead to grating lobes and reduce DoA accuracy in mobile environments. To address this issue, we investigate the variation of grating lobes across OFDM subcarrier frequencies and propose a differential frequency array (DFA) model to suppress grating lobes through subcarrier cooperation. Furthermore, we develop an off-grid DoA estimation algorithm based on sparse Bayesian learning, tailored to the DFA structure. Simulation results show that the proposed method effectively suppresses grating lobes and significantly improves DoA estimation accuracy. Prototype experiments based on 5G picocells further confirm its feasibility in practical mobile ISAC scenarios.
LLM-Enabled LAWNs: Toward Integrated Sensing, Communication, and Control Qingqing Cheng, Zhenguo Shi, Weijie Yuan, Yiyan Ma, Jiacheng Wang, Geng Sun IEEE Network, 2026 Low-altitude wireless networks (LAWNs) play an important role in supporting unmanned aerial vehicles (UAVs) and related services. However, achieving seamless integration of sensing, communication, and control remains a core challenge in dynamic environments, where traditional methods fall short in adaptability and efficiency. This paper investigates the integration of large language models (LLMs) and LAWNs to facilitate adaptive integrated sensing, communication, and control performance. We first outline foundational concepts of LAWNs and LLMs, including their main applications and strengths. Next, we examine the benefits and challenges of integrating LLMs and LAWNs. Additionally, we propose a novel LLM-enabled LAWN that dynamically integrates sensing for environmental perception, communication for robust data exchange, and control for precise trajectory management. A case study on urban UAV operations demonstrates clear performance gains: the proposed method reduces total route length by nearly 20% and achieves the highest average data rate among all benchmark planners while maintaining 95% sensing accuracy. These results show that LLM-driven decision support can improve both mission efficiency and communication quality in practical LAWNs.
Resolution Limits of Non-Adaptive 20 Questions Estimation for Tracking Multiple Moving Targets Chunsong Sun, Lin Zhou, Jingjing Wang, Weijie Yuan, Chunxiao Jiang, Alfred Hero IEEE Transactions on Information Theory, 2026 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.
Fluid Antenna Systems Meet Low-Altitude Wireless Networks: Fundamentals, Opportunities, and Future Directions Wenchao Liu, Xuhui Zhang, Chunjie Wang, Jinke Ren, Weijie Yuan, Changsheng You IEEE Internet of Things Magazine, 2026 Low-altitude wireless networks (LAWNs) are widely regarded as a cornerstone of the emerging low-altitude economy, yet they face significant challenges, including rapidly varying channels, diverse functional requirements, and dynamic interference environments. Fluid antenna (FA) systems introduce spatial reconfigurability that complements and extends conventional beamforming, enabling flexible exploitation of spatial diversity and adaptive response to channel variations. This paper proposes a novel architecture for FA-empowered LAWNs and presents a case study demonstrating substantial improvements in communication, sensing, and control performance compared to fixed-position antenna (FPA) systems. Key practical deployment considerations are examined, including mechanical design, position control, energy efficiency, and compliance with emerging industry standards. In addition, several future research directions are highlighted, including intelligent optimization, multi-function integration, and the exploration of novel low-altitude applications. By integrating theoretical analysis with practical deployment perspectives, this paper establishes FA systems as a key enabler for adaptive, efficient, and resilient network infrastructures in next-generation LAWNs.
Large AI Model-Enabled Secure Communications in Low-Altitude Wireless Networks: Concepts, Perspectives and Case Study Chuang Zhang, Geng Sun, Yijing Lin, Weijie Yuan, Sinem Coleri, Dusit Niyato IEEE Communications Magazine, 2026 Low-altitude wireless networks (LAWNs) have the potential to revolutionize communications by supporting a range of applications, including urban parcel delivery, aerial inspections and air taxis. However, compared with traditional wireless networks, LAWNs face unique security challenges due to low-altitude operations, frequent mobility and reliance on unlicensed spectrum, making it more vulnerable to some malicious attacks. In this article, we investigate some large artificial intelligence model (LAM)-enabled solutions for secure communications in LAWNs. Specifically, we first explore the amplified security risks and important limitations of traditional AI methods in LAWNs. Then, we introduce the basic concepts of LAMs and delve into the role of LAMs in addressing these challenges. To demonstrate the practical benefits of LAMs for secure communications in LAWNs, we propose a novel LAM-based optimization framework. This framework uses chain-of-thought-enabled large language models (LLMs) to enhance state features derived from handcrafted representations and design intrinsic rewards based on these enhanced features. This approach improves reinforcement learning performance for secure communication tasks. Through a typical case study, simulation results validate the effectiveness of the proposed framework. Finally, we outline future directions for integrating LAMs into secure LAWN applications.
N2LoS: Single-Tag mmWave Backscatter for Robust Non-Line-of-Sight Localization Zhenguo Shi, Yihe Yan, Yanxiang Wang, Wen Hu, Chun Tung Chou, Qingqing Cheng, Weijie Yuan IEEE Transactions on Mobile Computing, 2026 The accuracy of traditional localization methods significantly degrades when the direct path between the wireless transmitter and the target is blocked or non-penetrable. This paper proposes <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>N LoS</i></b>, a novel approach for precise non-line-of-sight (NLoS) localization using a single mmWave radar and a backscatter tag. <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>N LoS</i></b> leverages multipath reflections from both the tag and surrounding reflectors to accurately estimate the target's position. <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>N LoS</i></b> introduces several key innovations. First, we design <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">HFD</b> (Hybrid Frequency-Hopping and Direct Sequence Spread Spectrum) to detect and differentiate reflectors from the target. Second, we enhance signal-to-noise ratio (SNR) by exploiting the correlation properties of the designed signals, improving detection robustness in complex environments. Third, we propose <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FS-MUSIC</b> (Frequency-Spatial Multiple Signal Classification), a super-resolution algorithm that extends the traditional MUSIC method by constructing a higher-rank signal matrix, enabling the resolution of additional multipath components. We evaluate <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>N LoS</i></b> using a 24 GHz mmWave radar with 250 MHz bandwidth in three diverse environments: a laboratory, an office, and an around-the-corner corridor. Experimental results demonstrate that <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>N LoS</i></b> achieves median localization errors of <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">10.69 cm (X)</b> and <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">11.98 cm (Y)</b> at a 5 m range in the laboratory setting, showcasing its effectiveness for real-world NLoS localization.
Unveiling the Potential of NOMA: A Journey to Next-Generation Multiple Access Adeel Ahmed, Wang Xingfu, Ammar Hawbani, Weijie Yuan, Hina Tabassum, Yuanwei Liu, Muhammad Umar Farooq Qaisar, Zhiguo Ding, Naofal Al-Dhahir, Arumugam Nallanathan, Derrick Wing Kwan Ng IEEE Communications Surveys and Tutorials, 2025
Secure Federated Learning with Model Compression Yahao Ding, Mohammad Shikh-Bahaei, Chongwen Huang, Weijie Yuan 2023 IEEE International Conference on Communications Workshops Sustainable Communications for Renaissance Icc Workshops 2023, 2023