arXiv subjects

Gaojie Chen

Publications and source records attributed to Gaojie Chen.

At least 19 recordsLinked to original sources

arXiv2026

Deep Learning-Enhanced Real-Time Wi-Fi Sensing Through Single Transceiver Pair

The advancement of next-generation Wi-Fi technology heavily relies on sensing capabilities, which play a pivotal role in enabling sophisticated applications. In response to the growing demand for large-scale deployments, contemporary Wi-Fi sensing systems strive to achieve high-precision perception while maintaining minimal bandwidth consumption and antenna count requirements. Remarkably, various deep learning-driven perception technologies have demonstrated the ability to surpass conventional resolution limits. However, the theoretical underpinnings of this phenomenon have not been thoroughly investigated in existing research. We find that under hardware-constrained conditions, the performance gains of deep learning in Wi-Fi sensing primarily originate from two aspects: prior information and temporal correlation, which act as specific forms of side information that reduce the estimation error bound. We construct a deep learning-based Wi-Fi sensing system using only a single transceiver pair and design experiments to validate these gains. The system achieves an average human pose estimation error of 0.2189 m and an average localization error of 0.6124 m, while operating in real time at 42 fps on commodity hardware.

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arXiv2026

Uplink-Completion-Triggered Edge-GPU Inference for Multi-Agent Cooperative Perception

This paper investigates the coupling between wireless input completion and graphics processing unit (GPU) execution in centralized multi-agent cooperative perception. Specifically, beyond the conceptual treatment of completion-triggered overlap, a complete execution path is realized and validated on a physical GPU for a cooperative-perception deep neural network (DNN). Each encoder branch is released immediately upon completion of its corresponding input transmission, while the original fusion dependencies and inference mapping are preserved. The resulting release-triggered communication computation coupling (RTCC) propagates validated wireless completion events through host-to-device (H2D) staging, CUDA synchronization, and dependency-preserving branch dispatch, while remaining compatible with causal wireless schedulers. Experiments combining trace-driven wireless arrivals, physical GPU execution, and measured-DAG evaluation show that RTCC reduces complete-detection latency across different communication loads and schedulers, while preserving identical detection outputs and average-precision performance relative to conventional execution.

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arXiv2026

Split-Gate Pooled-Evidence Stochastic-Rollout Scheduling for Timely Progressive Edge Inference

This paper investigates causal radio scheduling for progressive edge inference, with the goal of maximizing timely inference throughput under job-specific deadlines. Specifically, a multi-tenant system is modeled in which each job alternates between wireless transmission and graphics processing unit (GPU) computation. The model captures time-varying uplink service, inter-stage precedence, variant-aware batching, non-preemptive execution on two GPU streams, and heterogeneous deadlines. To account for delayed radio-GPU coupling, a split-gate pooled-evidence stochastic-rollout (SGPE-SR) scheduler is proposed. Candidate and fallback policies are selected on one set of sampled futures, after which admission is evaluated on an independent held-out set; common random numbers are retained within each held-out candidate-fallback comparison. The override is executed only when its pooled nominal/recent-history held-out advantage is sufficiently positive and an independent recent-history replication statistic is nonnegative. System-level simulations using measured GPU profiles and paired random instances show that, in a prespecified long-horizon evaluation over 30 previously unused seeds and 3,379 offered jobs, SGPE-SR improves timely completions over the strongest rate-based baseline by 4.27%, with a mean paired gain of 3.00 jobs per run and a 95% bootstrap interval of [1.60, 4.43].

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arXiv2026

Unlocking Directional Radiation in Pinching-Antenna Systems: Geometry-Aware Design and Experimental Verification

Pinching-antenna systems (PASS) have recently attracted growing interest as a flexible architecture for creating "last-meter" line-of-sight wireless links through dielectric waveguides and reconfigurable radiation points. While modeling pinching antennas (PAs) as isotropic point radiators has enabled tractable analyses and demonstrated the performance gains of PASS, their practical radiation characteristics remain underexplored. This article investigates PASS from the perspective of PA geometry. Starting from the physical coupling principle, we explain why PA shape affects the induced polarization current and incorporate directional gain into the channel model. The full-wave simulations are conducted to show how different PA geometries and orientations reshape the internal field distribution and far-field radiation pattern. A 60 GHz prototype video transmission experiment is further presented to demonstrate the link-level impact of changing PA states. Finally, promising applications enabled by geometry-aware directional PASS are highlighted.

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arXiv2026

Joint 3D Trajectory Design and Resource Allocation for Secure Dual-UAV-aided Underlay Systems

Uncrewed aerial vehicles (UAVs) are increasingly being employed for data collection tasks, thanks to their high mobility and easy deployment, acting as aerial platforms to collect data from ground devices (GDs). This study considers a secure underlay data collection system assisted by dual UAVs and focuses on the joint design of the UAVs' three-dimensional (3D) flight paths, the power of the jamming UAV, the power of GDs, and the scheduling of the underlay GDs in the context of an aerial eavesdropper. The highly coupled objective function and non-convex constraints make the formulated problem more complicated to solve. We first utilize an approximate lower bound on the expected spectral efficiency to streamline the solution process. The average secrecy spectral efficiency (ASSE) is maximized by jointly designing the 3D trajectory of the UAVs, the transmit power of GDs, and the user scheduling. The optimization problem is decomposed into four subproblems using block coordinate descent, with each of them into manageable convex optimization tasks by incorporating slack variables and employing successive convex approximation methods. The numerical results validate the effectiveness of our proposed approach, demonstrating that the design of UAV 3D trajectories remarkably improves the ASSE of the considered system.

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arXiv2026

Joint Beamforming and Position Optimization for FIRES-NOMA Assisted Wireless Communication Systems

To address the limitations of conventional reconfigurable intelligent surfaces (RIS) in spatial control capability, this paper proposes a fluid integrated reflecting and emitting surface (FIRES) assisted non-orthogonal multiple access (NOMA) multi-user communication system. In this system, each FIRES element can continuously and flexibly adjust its position in response to environmental variations, enabling simultaneous service to users in both transmission and reflection zones. This significantly enhances the system's spatial degrees of freedom (DoF) and service adaptability. To maximize the system's total sum rate, we formulate a non-convex optimization problem that jointly optimizes the base station beamforming, the transmission/reflection coefficients of the FIRES, and the element positions. An alternating optimization (AO) algorithm is developed, incorporating successive convex approximation (SCA), semi-definite relaxation (SDR), and majorization-minimization (MM) techniques. In particular, to address the complex channel coupling introduced by the coexistence of direct and FIRES paths, the MM framework is employed in the element position optimization subproblem, enabling an efficient iterative solution strategy. Simulation results validate that the proposed system achieves up to a 27% increase in total sum rate compared to conventional STAR-RIS systems and requires approximately 50% fewer RIS elements to attain the same performance, highlighting its effectiveness for cost-efficient large-scale deployment.

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arXiv2026

Semantic Transmission Framework in Direct Satellite Communications

Insufficient link budget has become a bottleneck problem for direct access in current satellite communications. In this paper, we develop a semantic transmission framework for direct satellite communications as an effective and viable solution to tackle this problem. To measure the tradeoffs between communication, computation, and generation quality, we introduce a semantic efficiency metric with optimized weights. The optimization aims to maximize the average semantic efficiency metric by jointly optimizing transmission mode selection, satellite-user association, ISL task migration, denoising steps, and adaptive weights, which is a complex nonlinear integer programming problem. To maximize the average semantic efficiency metric, we propose a decision-assisted REINFORCE++ algorithm that utilizes feasibility-aware action space and a critic-free stabilized policy update. Numerical results show that the proposed algorithm achieves higher semantic efficiency than baselines.

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arXiv2026

Joint Optimization of Flexible Antenna Array Shape and Beamforming for Secure Communication

Flexible antenna arrays (FAAs) can physically reshape their geometry to add new spatial degrees of freedom, whereas transmit beamforming adjusts the complex element weights to electronically steer and shape the array's radiation pattern, thereby significantly improving communication performance. This paper is the first to explore the integration of FAA geometry control and beamforming for physical layer security enhancement, where a base station equipped with an FAA communicates with a legitimate user in the presence of passive eavesdroppers. To safeguard confidential transmissions, we formulate a new secrecy rate maximization problem that jointly optimizes the transmit beamforming vector and a continuous FAA shape control parameter. Due to the non convex nature of the problem, an alternating optimization algorithm is developed to decompose the joint design into tractable subproblems, which are solved iteratively to refine both the FAA geometry and beamforming strategy. Simulation results confirm that the proposed joint optimization framework significantly outperforms conventional fixed shape or beamforming only schemes, demonstrating the potential of FAA enabled reconfigurability for secure wireless communications.

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arXiv2026

Geometry-Dependent Radiation of Pinching Antennas: Theory, Simulation, and Measurement

Most existing studies achieve beamforming by adjusting the positions of pinching antennas (PAs) and typically model PAs as isotropic radiators. However, under the dielectric scatterer model, the PA radiation pattern depends on its geometry. This letter investigates the radiation patterns of PAs with different geometries through full-wave simulations and measurements, and demonstrates how geometry influences the radiation directivity. In addition, an arc-shaped PA is introduced to enable transmit-direction control in PA systems. A PA system prototype consisting of a dielectric waveguide, waveguide transitions, and a PA element is proposed. Prototype measurements are used to validate the simulations and to characterize the directivity of square and triangular PAs, and the measurement procedure can be applied to obtain radiation patterns for PAs with general geometries. The simulation and measurement results jointly demonstrate that PA geometry is critical in PA systems because it influences the radiation characteristics significantly.

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arXiv2026

QoE-Driven Multi-Task Offloading for Semantic-Aware Edge Computing Systems

Mobile edge computing (MEC) provides low-latency offloading solutions for computationally intensive tasks, effectively improving the computing efficiency and battery life of mobile devices. However, for data-intensive tasks or scenarios with limited uplink bandwidth, network congestion might occur due to massive simultaneous offloading nodes, increasing transmission latency and affecting task performance. In this paper, we propose a semantic-aware multi-modal task offloading framework to address the challenges posed by limited uplink bandwidth. By introducing a semantic extraction factor, we balance the relationship among transmission latency, computation energy consumption, and task performance. To measure the offloading performance of multi-modal tasks, we design a unified and fair quality of experience (QoE) metric that includes execution latency, energy consumption, and task performance. Lastly, we formulate the optimization problem as a Markov decision process (MDP) and exploit the multi-agent proximal policy optimization (MAPPO) reinforcement learning algorithm to jointly optimize the semantic extraction factor, communication resources, and computing resources to maximize overall QoE. Experimental results show that the proposed method achieves a reduction in execution latency and energy consumption of 18.1% and 12.9%, respectively compared with the semantic-unaware approach. Moreover, the proposed approach can be easily extended to models with different user preferences.

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arXiv2026

Large Artificial Intelligence Models for Future Wireless Communications

The anticipated integration of large artificial intelligence (AI) models with wireless communications is estimated to usher a transformative wave in the forthcoming information age. As wireless networks grow in complexity, the traditional methodologies employed for optimization and management face increasingly challenges. Large AI models have extensive parameter spaces and enhanced learning capabilities and can offer innovative solutions to these challenges. They are also capable of learning, adapting and optimizing in real-time. We introduce the potential and challenges of integrating large AI models into wireless communications, highlighting existing AIdriven applications and inherent challenges for future large AI models. In this paper, we propose the architecture of large AI models for future wireless communications, introduce their advantages in data analysis, resource allocation and real-time adaptation, discuss the potential challenges and corresponding solutions of energy, architecture design, privacy, security, ethical and regulatory. In addition, we explore the potential future directions of large AI models in wireless communications, laying the groundwork for forthcoming research in this area.

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arXiv2025

Flexible Reconfigurable Intelligent Surface-Aided Covert Communications in UAV Networks

In recent years, unmanned aerial vehicles (UAVs) have become a key role in wireless communication networks due to their flexibility and dynamic adaptability. However, the openness of UAV-based communications leads to security and privacy concerns in wireless transmissions. This paper investigates a framework of UAV covert communications which introduces flexible reconfigurable intelligent surfaces (F-RIS) in UAV networks. Unlike traditional RIS, F-RIS provides advanced deployment flexibility by conforming to curved surfaces and dynamically reconfiguring its electromagnetic properties to enhance the covert communication performance. We establish an electromagnetic model for F-RIS and further develop a fitted model that describes the relationship between F-RIS reflection amplitude, reflection phase, and incident angle. To maximize the covert transmission rate among UAVs while meeting the covert constraint and public transmission constraint, we introduce a strategy of jointly optimizing UAV trajectories, F-RIS reflection vectors, F-RIS incident angles, and non-orthogonal multiple access (NOMA) power allocation. Considering this is a complicated non-convex optimization problem, we propose a deep reinforcement learning (DRL) algorithm-based optimization solution. Simulation results demonstrate that our proposed framework and optimization method significantly outperform traditional benchmarks, and highlight the advantages of F-RIS in enhancing covert communication performance within UAV networks.

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arXiv2025

Uplink SCMA-empowered Uncoordinated Random Access for Future mMTC

In this paper, a novel uncoordinated random access (URA) protocol is presented to address the pressing demand for massive connectivity with low access latency in future massive machine type communication (mMTC) scenarios. The proposed URA scheme integrates the classical slotted ALOHA (S-ALOHA) protocol with sparse code multiple access (SCMA) technique, referred to as SCMA-empowered URA. Specifically, active users randomly choose an SCMA codebook to access the communication network in an arbitrary time slot whenever they want without scheduling. However, due to the lack of central coordination in the proposed URA scheme, SCMA codebook collisions become inevitable, making decoding challenging and leading to increased access failures. To cope with the decoding issue, an interference-canceling (IC) first decoding strategy is proposed at the access point (AP), which can partially tackles collision problems, contributing to a higher system throughput. Taking the proposed IC-first decoding strategy into account, a closed-form theoretical expression of the throughput is derived. Moreover, to alleviate the throughput degradation under the congested user traffic, a user barring mechanism is introduced to manage the traffic load. Firstly, a closed-form expression of idle codebook probability is developed to help indicate the system state, i.e., congested or not. Then, in addition to the estimated real-time load, the AP adaptively adjusts the access probability and redistributes the actual access load. Finally, simulation results demonstrate that the proposed SCMA-empowered URA scheme enjoys higher maximum throughput, compared to the conventional orthogonal multiple access (OMA) based URA scheme. Moreover, the accuracy of the presented theoretical analysis and the effectiveness of the user barring mechanism are verified.

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arXiv2025

Radio Map-Enabled 3D Trajectory and Communication Optimization for Low-Altitude Air-Ground Cooperation

Low-altitude economy includes the application of unmanned aerial vehicles (UAVs) serving ground robots. This paper investigates the 3-dimensional (3D) trajectory and communication optimization for low-altitude air-ground cooperation systems, where mobile unmanned ground vehicles (UGVs) upload data to UAVs. We propose a joint optimization algorithm to maximize the minimal sum-rate of UGVs while ensuring quality of service and navigation constraints. The proposed algorithm integrates a successive convex approximation (SCA)-penalty method for UGV-UAV scheduling, an SCA-based approach for UGV transmit power control, and a novel warm-start particle swarm optimization with cross mutation (WS-PSO-CM). The WS-PSO-CM leverages convex optimization results from a statistical channel model to initialize particle swarm, significantly improving the performance, compared with celebrated PSO-CM. Simulation results demonstrate that the proposed algorithm achieves a $45.8$\% higher minimal sum-rate compared to the baseline PSO-CM under the same iterations. This gain can be translated to reducing computational time by $46.7$\% of PSO-CM. Furthermore, our simulation results reveal that UAVs dynamically adjust trajectories to avoid interference by buildings, and maintain proximity to UGVs to mitigate path-loss.

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arXiv2025

Hybrid Generative Semantic and Bit Communications in Satellite Networks: Trade-offs in Latency, Generation Quality, and Computation

As satellite communications play an increasingly important role in future wireless networks, the issue of limited link budget in satellite systems has attracted significant attention in current research. Although semantic communications emerge as a promising solution to address these constraints, it introduces the challenge of increased computational resource consumption in wireless communications. To address these challenges, we propose a multi-layer hybrid bit and generative semantic communication framework which can adapt to the dynamic satellite communication networks. Furthermore, to balance the semantic communication efficiency and performance in satellite-to-ground transmissions, we introduce a novel semantic communication efficiency metric (SEM) that evaluates the trade-offs among latency, computational consumption, and semantic reconstruction quality in the proposed framework. Moreover, we utilize a novel deep reinforcement learning (DRL) algorithm group relative policy optimization (GRPO) to optimize the resource allocation in the proposed network. Simulation results demonstrate the flexibility of our proposed transmission framework and the effectiveness of the proposed metric SEM, illustrate the relationships among various semantic communication metrics.

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arXiv2025

Ambiguity Function Analysis of AFDM Signals for Integrated Sensing and Communications

Affine frequency division multiplexing (AFDM) is a promising chirp-based waveform with high flexibility and resilience, making it well-suited for next-generation wireless networks, particularly in high-mobility scenarios. In this paper, we investigate the ambiguity functions (AFs) of AFDM signals, which fundamentally characterize their range and velocity estimation capabilities in both monostatic and bistatic settings. Specifically, we first derive the auto-ambiguity function (AAF) of an AFDM chirp subcarrier, revealing its "spike-like" local property and "periodic-like" global property along the rotated delay and Doppler dimensions. This structure naturally forms a parallelogram for each localized pulse of the AAF of the AFDM chirp subcarrier, enabling unambiguous target sensing. Then, we study the cross-ambiguity function (CAF) between two different AFDM chirp subcarriers, which exhibits the same local and global properties as the AAF but with an additional shift along the Doppler dimension. We then extend our analysis to the AF of various typical AFDM frames, considering both deterministic pilot and random data symbols. In particular, we demonstrate that inserting guard symbols in AFDM facilitates interference-free sensing. Simulation results validate our theoretical findings, highlighting AFDM's strong potential for ISAC applications.

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arXiv2025

Widely Linear Augmented Extreme Learning Machine Based Impairments Compensation for Satellite Communications

Satellite communications are crucial for the evolution beyond fifth-generation networks. However, the dynamic nature of satellite channels and their inherent impairments present significant challenges. In this paper, a novel post-compensation scheme that combines the complex-valued extreme learning machine with augmented hidden layer (CELMAH) architecture and widely linear processing (WLP) is developed to address these issues by exploiting signal impropriety in satellite communications. Although CELMAH shares structural similarities with WLP, it employs a different core algorithm and does not fully exploit the signal impropriety. By incorporating WLP principles, we derive a tailored formulation suited to the network structure and propose the CELM augmented by widely linear least squares (CELM-WLLS) for post-distortion. The proposed approach offers enhanced communication robustness and is highly effective for satellite communication scenarios characterized by dynamic channel conditions and non-linear impairments. CELM-WLLS is designed to improve signal recovery performance and outperform traditional methods such as least square (LS) and minimum mean square error (MMSE). Compared to CELMAH, CELM-WLLS demonstrates approximately 0.8 dB gain in BER performance, and also achieves a two-thirds reduction in computational complexity, making it a more efficient solution.

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arXiv2025

Link Priority Buffer-Aided Relay Selection with Energy Storage from Energy Harvest

This paper proposes a novel relay selection scheme for buffer-aided wireless networks with relays equipped with both data buffers and energy storage. While buffer-aided relay networks have demonstrated significantly improved performance, energy harvesting has become an attractive solution in many wireless systems, garnering considerable attention when applied to buffer-aided relay networks. It is known that state-dependent selection rules must be used to achieve full diversity order in buffer-aided relay networks, requiring link priorities for data transmission to be set based on system states. This task becomes challenging when both data buffers and energy storage are involved. In this paper, we introduce a novel method for setting link priorities, which forms the basis for a new selection rule. The outage probability of the proposed selection scheme is derived. The simulation results demonstrate the superiority of our proposed algorithm which achieves full diversity in buffer-aided relay selection with energy storage, and consistently outperforms baseline approaches across various metrics.

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