arXiv subjects

Zhiqin Wang

Publications and source records attributed to Zhiqin Wang.

15 recordsLinked to original sources

arXiv2026

DeepStream: Prototyping Deep Joint Source-Channel Coding for Real-Time Multimedia Transmissions

Deep learning-based joint source-channel coding (DeepJSCC) has emerged as a promising technique in 6G for enhancing the efficiency and reliability of data transmission across diverse modalities, particularly in low signal-to-noise ratio (SNR) environments. This advantage is realized by leveraging powerful neural networks to learn an optimal end-to-end mapping from the source data directly to the transmit symbol sequence, eliminating the need for separate source coding, channel coding, and modulation. Although numerous efforts have been made towards efficient DeepJSCC, they have largely stayed at numerical simulations that can be far from practice, leaving the real-world viability of DeepJSCC largely unverified. To this end, we prototype DeepStream upon orthogonal frequency division multiplexing (OFDM) technology to offer efficient and robust DeepJSCC for multimedia transmission. In conforming to OFDM, we develop both a feature-to-symbol mapping method and a cross-subcarrier precoding method to improve the subcarrier independence and reduce peak-to-average power ratio. To reduce system complexity and enable flexibility in accommodating varying quality of service requirements, we further propose a progressive coding strategy that adjusts the compression ratio based on latency with minimal performance loss. We implement DeepStream for real-time image transmission and video streaming using software-defined radio. Extensive evaluations verify that DeepStream outperforms both the standard scheme and the direct deployment scheme. Particularly, at an SNR of 10 dB, DeepStream achieves a PSNR of 35 dB for image transmission and an MS-SSIM of 20 dB for video streaming, whereas the standard scheme fails to recover meaningful information.

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arXiv2026

Pinching Antennas-Aided Integrated Sensing and Multicast Communication Systems

A pinching antennas (PAs)-aided integrated sensing and multicast communication framework is proposed. In this framework, the communication performance is measured by the multicast rate considering max-min fairness. Moreover, the sensing performance is quantified by the Bayesian Cramér-Rao bound (BCRB), where a Gauss-Hermite quadrature-based approach is proposed to compute the Bayesian Fisher information matrix. Based on these metrics, PA placement is optimized under three criteria: communications-centric (C-C), sensing-centric (S-C), and Pareto-optimal designs. These designs are investigated in two scenarios: the single-PA case and the multi-PA case. 1) For the single-PA case, a closed-form solution is derived for the location of the C-C transmit PA, while the S-C design yields optimal transmit and receive PA placements that are symmetric about the target location. Leveraging this geometric insight, the Pareto-optimal design is solved by enforcing this PA placement symmetry, thereby reducing the joint transmit and receive PA placement to the transmit PA optimization. 2) For the general multi-PA case, the PA placements constitute a highly non-convex optimization problem. To solve this, an element-wise alternating optimization-based method is proposed to sequentially optimize all PA placements for the S-C design, and is further incorporated into an augmented Lagrangian (AL) framework and a rate-profile formulation to solve the C-C and Pareto-optimal design problems, respectively. Numerical results show that: i) PASS substantially outperforms fixed-antenna baselines in both multicast rate and sensing accuracy; ii) the multicasting gain becomes more pronounced as the user density increases; and iii) the sensing accuracy improves with the number of deployed PAs.

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arXiv2026

AI-Native 6G Physical Layer with Cross-Module Optimization and Cooperative Control Agents

In this article, a framework of AI-native cross-module optimized physical layer with cooperative control agents is proposed, which involves optimization across global AI/ML modules of the physical layer with innovative design of multiple enhancement mechanisms and control strategies. Specifically, it achieves simultaneous optimization across global modules of uplink AI/ML-based joint source-channel coding with modulation, and downlink AI/ML-based modulation with precoding and corresponding data detection, reducing traditional inter-module information barriers to facilitate end-to-end optimization toward global objectives. Moreover, multiple enhancement mechanisms are also proposed, including i) an AI/ML-based cross-layer modulation approach with theoretical analysis for downlink transmission that breaks the isolation of inter-layer features to expand the solution space for determining improved constellation, ii) a utility-oriented precoder construction method that shifts the role of the AI/ML-based CSI feedback decoder from recovering the original CSI to directly generating precoding matrices aiming to improve end-to-end performance, and iii) incorporating modulation into AI/ML-based CSI feedback to bypass bit-level bottlenecks that introduce quantization errors, non-differentiable gradients, and limitations in constellation solution spaces. Furthermore, AI/ML based control agents for optimized transmission schemes are proposed that leverage AI/ML to perform model switching according to channel state, thereby enabling integrated control for global throughput optimization. Finally, simulation results demonstrate the superiority of the proposed solutions in terms of BLER and throughput. These extensive simulations employ more practical assumptions that are aligned with the requirements of the 3GPP, which hopefully provides valuable insights for future standardization discussions.

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arXiv2025

DeepGuard: Defending Deep Joint Source-Channel Coding Against Eavesdropping at Physical-Layer

Deep joint source-channel coding (DeepJSCC) has emerged as a promising paradigm for efficient and robust information transmission. However, its intrinsic characteristics also pose new security challenges, notably an increased vulnerability to eavesdropping attacks. Existing studies on defending against eavesdropping attacks in DeepJSCC, while demonstrating certain effectiveness, often incur considerable computational overhead or introduce performance trade-offs that may adversely affect legitimate users. In this paper, we present DeepGuard, to the best of our knowledge, the first physical-layer defense framework for DeepJSCC against eavesdropping attacks, validated through over-the-air experiments using software-defined radios (SDRs). Considering that existing eavesdropping attacks against DeepJSCC are limited to simulation under ideal channels, we take a step further by identifying and implementing four representative types of attacks under various configurations in orthogonal frequency-division multiplexing systems. These attacks are evaluated over-the-air under diverse scenarios, allowing us to comprehensively characterize the real-world threat landscape. To mitigate these threats, DeepGuard introduces a novel preamble perturbation mechanism that modifies the preamble shared only between legitimate transceivers. To realize it, we first conduct a theoretical analysis of the perturbation's impact on the signals intercepted by the eavesdropper. Building upon this, we develop an end-to-end perturbation optimization algorithm that significantly degrades eavesdropping performance while preserving reliable communication for legitimate users. We prototype DeepGuard using SDRs and conduct extensive over-the-air experiments in practical scenarios. Extensive experiments demonstrate that DeepGuard effectively mitigates eavesdropping threats.

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arXiv2025

Low-Complexity Null-Space-Based Simultaneous Wireless Information and Power Transfer Scheme

Simultaneous wireless information and power transfer (SWIPT) has attracted sustained interest. We propose a null-space-based transmission scheme for multiuser SWIPT serving both energy users (EUs) and information users (IUs). Under a practical nonlinear energy-harvesting (EH) model and multiple waveform options, we revisit the role of dedicated energy beams (EBs). We show that, in general, dedicated EBs are unnecessary because information beams (IBs) with Gaussian signaling can simultaneously support wireless energy transfer (WET) and wireless information transfer (WIT), unless special energy-centric waveforms (e.g., deterministic sinusoidal waveforms) are employed and provide sufficient gains. Guided by these insights, we formulate an optimization problem for EB design to enable dedicated waveform transmission for WET, and we develop a low-complexity algorithm that reduces computation by ignoring the WET contribution of IBs during optimization. Numerical results corroborate that deterministic sinusoidal waveforms outperform Gaussian signaling when the received RF power lies in the EH high-efficiency region, making dedicated EBs beneficial. The proposed scheme achieves computational complexity reductions of 91.43\% and 98.54\% for the cases $M=8,,K^I=K^E=2$ and $M=16,,K^I=K^E=4$, respectively, with negligible performance loss, thereby validating the efficiency of the low-complexity algorithm.

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arXiv2024

Wireless-Powered Mobile Crowdsensing Enhanced by UAV-Mounted RIS: Joint Transmission, Compression, and Trajectory Design

Mobile crowdsensing (MCS) enables data collection from massive devices to achieve a wide sensing range. Wireless power transfer (WPT) is a promising paradigm for prolonging the operation time of MCS systems by sustainably transferring power to distributed devices. However, the efficiency of WPT significantly deteriorates when the channel conditions are poor. Unmanned aerial vehicles (UAVs) and reconfigurable intelligent surfaces (RISs) can serve as active or passive relays to enhance the efficiency of WPT in unfavourable propagation environments. Therefore, to explore the potential of jointly deploying UAVs and RISs to enhance transmission efficiency, we propose a novel transmission framework for the WPT-assisted MCS systems, which is enhanced by a UAV-mounted RIS. Subsequently, under different compression schemes, two optimization problems are formulated to maximize the weighted sum of the data uploaded by the user equipments (UEs) by jointly designing the WPT and uploading time, the beamforming matrics, the CPU cycles, and the UAV trajectory. A block coordinate descent (BCD) algorithm based on the closed-form beamforming designs and the successive convex approximation (SCA) algorithm is proposed to solve the formulated problems. Furthermore, to highlight the insight of the gains brought by the compression schemes, we analyze the energy efficiencies of compression schemes and confirm that the gains gradually reduce with the increasing power used for compression. Simulation results demonstrate that the amount of collected data can be effectively increased in wireless-powered MCS systems.

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arXiv2022

Sensing as a Service in 6G Perceptive Networks: A Unified Framework for ISAC Resource Allocation

In the upcoming next-generation (5G-Advanced and 6G) wireless networks, sensing as a service will play a more important role than ever before. Recently, the concept of perceptive network is proposed as a paradigm shift that provides sensing and communication (S&C) services simultaneously. This type of technology is typically referred to as Integrated Sensing and Communications (ISAC). In this paper, we propose the concept of sensing quality of service (QoS) in terms of diverse applications. Specifically, the probability of detection, the Cramer-Rao bound (CRB) for parameter estimation and the posterior CRB for moving target indication are employed to measure the sensing QoS for detection, localization, and tracking, respectively. Then, we establish a unified framework for ISAC resource allocation, where the fairness and the comprehensiveness optimization criteria are considered for the aforementioned sensing services. The proposed schemes can flexibly allocate the limited power and bandwidth resources according to both S&C QoSs. Finally, we study the performance trade-off between S&C services in different resource allocation schemes by numerical simulations.

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arXiv2022

Rethinking the Performance of ISAC System: From Efficiency and Utility Perspectives

Integrated sensing and communications (ISAC) is an essential technology for the 6G communication system, which enables the conventional wireless communication network capable of sensing targets around. The shared use of pilots is a promising strategy to achieve ISAC. It brings a trade-off between communication and sensing, which is still unclear under the imperfect channel estimation condition. To provide some insights, the trade-off between ergodic capacity with imperfect channel estimation and ergodic Cramer-Rao bound (CRB) of range sensing is investigated. Firstly, the closedform expressions of ergodic capacity and ergodic range CRB are derived, which are associated with the number of pilots. Secondly, two novel metrics named efficiency and utility are firstly proposed to evaluate the joint performance of capacity and range sensing error. Specifically, efficiency is used to evaluate the achievable capacity per unit of the sensing error, and utility is designed to evaluate the utilization degree of ISAC. Moreover, an algorithm of pilot length optimization is designed to achieve the best efficiency. Finally, simulation results are given to verify the accuracy of analytical results, and provide some insights on designing the slot structure.

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arXiv2022

AI Enlightens Wireless Communication: A Transformer Backbone for CSI Feedback

This paper is based on the background of the 2nd Wireless Communication Artificial Intelligence (AI) Competition (WAIC) which is hosted by IMT-2020(5G) Promotion Group 5G+AIWork Group, where the framework of the eigenvector-based channel state information (CSI) feedback problem is firstly provided. Then a basic Transformer backbone for CSI feedback referred to EVCsiNet-T is proposed. Moreover, a series of potential enhancements for deep learning based (DL-based) CSI feedback including i) data augmentation, ii) loss function design, iii) training strategy, and iv) model ensemble are introduced. The experimental results involving the comparison between EVCsiNet-T and traditional codebook methods over different channels are further provided, which show the advanced performance and a promising prospect of Transformer on DL-based CSI feedback problem.

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arXiv2022

Performance and Optimization of Reconfigurable Intelligent Surface Aided THz Communications

TeraHertz (THz) communications can satisfy the high data rate demand with massive bandwidth. However, severe path attenuation and hardware imperfection greatly alleviate its performance. Therefore, we utilize the reconfigurable intelligent surface (RIS) technology and investigate the RIS-aided THz communications. We first prove that the small-scale amplitude fading of THz signals can be accurately modeled by the fluctuating two-ray distribution based on two THz signal measurement experiments conducted in a variety of different scenarios. To optimize the phase-shifts at the RIS elements, we propose a novel swarm intelligence-based method that does not require full channel estimation. We then derive exact statistical characterizations of end-to-end signal-to-noise plus distortion ratio (SNDR) and signal-to-noise ratio (SNR). Moreover, we present asymptotic analysis to obtain more insights when the SNDR or the number of RIS's elements is high. Finally, we derive analytical expressions for the outage probability and ergodic capacity. The tight upper bounds of ergodic capacity for both ideal and nonideal radio frequency chains are obtained. It is interesting to find that increasing the number of RIS's elements can significantly improve the THz communications system performance. For example, the ergodic capacity can increase up to 25% when the number of elements increases from 40 to 80, which incurs only insignificant costs to the system.

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arXiv2021

Training Time Minimization for Federated Edge Learning with Optimized Gradient Quantization and Bandwidth Allocation

Training a machine learning model with federated edge learning (FEEL) is typically time-consuming due to the constrained computation power of edge devices and limited wireless resources in edge networks. In this paper, the training time minimization problem is investigated in a quantized FEEL system, where the heterogeneous edge devices send quantized gradients to the edge server via orthogonal channels. In particular, a stochastic quantization scheme is adopted for compression of uploaded gradients, which can reduce the burden of per-round communication but may come at the cost of increasing number of communication rounds. The training time is modeled by taking into account the communication time, computation time and the number of communication rounds. Based on the proposed training time model, the intrinsic trade-off between the number of communication rounds and per-round latency is characterized. Specifically, we analyze the convergence behavior of the quantized FEEL in terms of the optimality gap. Further, a joint data-and-model-driven fitting method is proposed to obtain the exact optimality gap, based on which the closed-form expressions for the number of communication rounds and the total training time are obtained. Constrained by total bandwidth, the training time minimization problem is formulated as a joint quantization level and bandwidth allocation optimization problem. To this end, an algorithm based on alternating optimization is proposed, which alternatively solves the subproblem of quantization optimization via successive convex approximation and the subproblem of bandwidth allocation via bisection search. With different learning tasks and models, the validation of our analysis and the near-optimal performance of the proposed optimization algorithm are demonstrated by the experimental results.

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arXiv2021

Multiple Residual Dense Networks for Reconfigurable Intelligent Surfaces Cascaded Channel Estimation

Reconfigurable intelligent surface (RIS) constitutes an essential and promising paradigm that relies programmable wireless environment and provides capability for space-intensive communications, due to the use of low-cost massive reflecting elements over the entire surfaces of man-made structures. However, accurate channel estimation is a fundamental technical prerequisite to achieve the huge performance gains from RIS. By leveraging the low rank structure of RIS channels, three practical residual neural networks, named convolutional blind denoising network, convolutional denoising generative adversarial networks and multiple residual dense network, are proposed to obtain accurate channel state information, which can reflect the impact of different methods on the estimation performance. Simulation results reveal the evolution direction of these three methods and reveal their superior performance compared with existing benchmark schemes.

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arXiv2021

Optimized Power Control Design for Over-the-Air Federated Edge Learning

This paper investigates the transmission power control in over-the-air federated edge learning (Air-FEEL) system. Different from conventional power control designs (e.g., to minimize the individual mean squared error (MSE) of the over-the-air aggregation at each round), we consider a new power control design aiming at directly maximizing the convergence speed. Towards this end, we first analyze the convergence behavior of Air-FEEL (in terms of the optimality gap) subject to aggregation errors at different communication rounds. It is revealed that if the aggregation estimates are unbiased, then the training algorithm would converge exactly to the optimal point with mild conditions; while if they are biased, then the algorithm would converge with an error floor determined by the accumulated estimate bias over communication rounds. Next, building upon the convergence results, we optimize the power control to directly minimize the derived optimality gaps under both biased and unbiased aggregations, subject to a set of average and maximum power constraints at individual edge devices. We transform both problems into convex forms, and obtain their structured optimal solutions, both appearing in a form of regularized channel inversion, by using the Lagrangian duality method. Finally, numerical results show that the proposed power control policies achieve significantly faster convergence for Air-FEEL, as compared with benchmark policies with fixed power transmission or conventional MSE minimization.

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arXiv2021

Symbiotic Sensing and Communications Towards 6G: Vision, Applications, and Technology Trends

Driven by the vision of intelligent connection of everything and digital twin towards 6G, a myriad of new applications, such as immersive extended reality, autonomous driving, holographic communications, intelligent industrial internet, will emerge in the near future, holding the promise to revolutionize the way we live and work. These trends inspire a novel technical design principle that seamlessly integrates two originally decoupled functionalities, i.e., wireless communication and sensing, into one system in a symbiotic way, which is dubbed symbiotic sensing and communications (SSaC), to endow the wireless network with the capability to "see" and "talk" to the physical world simultaneously. Noting that the term SSaC is used instead of ISAC (integrated sensing and communications) because the word ``symbiotic/symbiosis" is more inclusive and can better accommodate different integration levels and evolution stages of sensing and communications. Aligned with this understanding, this article makes the first attempts to clarify the concept of SSaC, illustrate its vision, envision the three-stage evolution roadmap, namely neutralism, commensalism, and mutualism of SaC. Then, three categories of applications of SSaC are introduced, followed by detailed description of typical use cases in each category. Finally, we summarize the major performance metrics and key enabling technologies for SSaC.

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arXiv2021

AI Enlightens Wireless Communication: Analyses, Solutions and Opportunities on CSI Feedback

In this paper, we give a systematic description of the 1st Wireless Communication Artificial Intelligence (AI) Competition (WAIC) which is hosted by IMT-2020(5G) Promotion Group 5G+AI Work Group. Firstly, the framework of full channel state information (F-CSI) feedback problem and its corresponding channel dataset are provided. Then the enhancing schemes for DL-based F-CSI feedback including i) channel data analysis and preprocessing, ii) neural network design and iii) quantization enhancement are elaborated. The final competition results composed of different enhancing schemes are presented. Based on the valuable experience of 1st WAIC, we also list some challenges and potential study areas for the design of AI-based wireless communication systems.

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