Guanding Yu’s research while affiliated with Zhejiang University and other places

With the widespread deployment of WiFi devices, utilizing beamforming feedback for sensing has become a popular trend in WiFi systems. However, the singular value decomposition (SVD)-based feedback method adopted in existing WiFi standards performs poorly in sensing performance since it only aims at maximizing the communication performance. To address this, we propose an integrated sensing and communication (ISAC)-oriented beamforming feedback protocol, which provides different sensing information based on the sensing indicator. Accordingly, we develop different ISAC-oriented CSI compression methods for different sensing applications requiring different feedback information. Taking the angle of departure (AoD) as an example, an optimization problem is formulated to maximize the data rate while preserving the complete AoD information. To resolve it, we propose an iterative algorithm to obtain the sub-optimal solution and a heuristic algorithm to reduce the computational complexity. We further extend the proposed compression method for the AoD information to the compression of the angle of arrival (AoA) and time of flight (ToF). Test results show that our proposal achieves both excellent communication and sensing performance and can be applied to various sensing applications, such as localization and action recognition.

Integrated sensing, communication, and computation (ISCC) has been regarded as a prospective technology for the next-generation wireless network, supporting humancentric intelligent applications. However, the delay sensitivity of these computation-intensive applications, especially in a multidevice ISCC system with limited resources, highlights the urgent need for efficient sensing task execution frameworks. To address this, we propose a resource-efficient sensing framework in this paper. Different from existing solutions, it features a novel action detection module deployed at each device to detect the onset of an action. Only time windows filled with signals of interest are offloaded to the edge server and processed by the edge recognition module, thus reducing overhead. Furthermore, we quantitatively analyze the sensing performance of the proposed sensing framework and formulate a sensing accuracy maximization problem under power, delay, and resource limitations for the multi-device ISCC system. By decomposing it into two subproblems, we develop an alternating direction method of multipliers (ADMM)-based distributed algorithm. It alternatively solves a sensing accuracy maximization subproblem at each device and employs a closed-form computation resource allocation strategy at the edge server till convergence. Finally, a real-world test is conducted using commodity wireless devices to validate the sensing performance analysis. Extensive test results demonstrate that our proposal achieves higher sensing accuracy under the limited resource compared to two baselines.

The millimeter wave (mmWave) communication becomes a key enabler for the future Internet of Things (IoT) due to its capability for supporting high rate and low-latency traffic. However, beamforming in the mmWave band faces issues of low efficiency since the narrow beam of mmWave devices would increase the search delay and overhead. Inspired by this, we utilize localization over sub-6 GHz band to assist the mmWave base station in performing fast and robust adaptive beamforming. Different from existing works, we focus on the indoor scenario and consider the effects of several practical issues, including localization errors and hardware defects. Specifically, a novel two-step access scheme is proposed. During the first step, we design a novel localization method customized for indoor scenarios, jointly considering the time of flight and angle of arrival. The localization error is further analyzed to determine the mmWave scanning angle and an optimal beamwidth expression is derived in closed-form to maximize system throughput with the considerations of the search delay. Moreover, considering the mismatch of the steering vector caused by the hardware defects, we propose a robust adaptive beamforming method in closed-form. Simulation results demonstrate that the proposed scheme can effectively reduce the search delay and realize robust beamforming to enhance the mmWave communication performance.

In this paper, we introduce an innovative hierarchical joint source-channel coding (HJSCC) framework for image transmission, utilizing a hierarchical variational autoencoder (VAE). Our approach leverages a combination of bottom-up and top-down paths at the transmitter to autoregressively generate multiple hierarchical representations of the original image. These representations are then directly mapped to channel symbols for transmission by the JSCC encoder. We extend this framework to scenarios with a feedback link, modeling transmission over a noisy channel as a probabilistic sampling process and deriving a novel generative formulation for JSCC with feedback. Compared with existing approaches, our proposed HJSCC provides enhanced adaptability by dynamically adjusting transmission bandwidth, encoding these representations into varying amounts of channel symbols. Additionally, we introduce a rate attention module to guide the JSCC encoder in optimizing its encoding strategy based on prior information. Extensive experiments on images of varying resolutions demonstrate that our proposed model outperforms existing baselines in rate-distortion performance and maintains robustness against channel noise.

Lithium-ion battery health management has become increasingly important as the application of batteries expands. Precise forecasting of capacity degradation is critical for ensuring the healthy usage of batteries. In this paper, we innovatively propose MSPMLP, a multi-scale capacity prediction model utilizing the mixture of experts (MoE) architecture and patch-based multi-layer perceptron (MLP) blocks, to capture both the long-term degradation trend and local capacity regeneration phenomena. Specifically, we utilize patch-based MLP blocks with varying patch sizes to extract multi-scale features from the capacity sequence. Leveraging the MoE architecture, the model adaptively integrates the extracted features, thereby enhancing its capacity and expressiveness. Finally, the future battery capacity is predicted based on the integrated features, achieving high prediction accuracy and generalization. Experimental results on the public NASA dataset indicate that MSPMLP achieves a mean absolute error (MAE) of 0.0078, improving by 41.8\% compared to existing methods. These findings highlight that MSPMLP, owing to its multi-scale modeling capability and generalizability, provides a promising solution to the battery capacity prediction challenges caused by capacity regeneration phenomena and complex usage conditions. The code of this work is provided at https://github.com/LeiYuzhu/CapacityPredict.

Consider an edge computing setting in which a user submits queries for the solution of a linear system to an edge processor, which is subject to time-varying computing availability. The edge processor applies a probabilistic linear solver (PLS) so as to be able to respond to the user's query within the allotted time and computing budget. Feedback to the user is in the form of an uncertainty set. Due to model misspecification, the uncertainty set obtained via a direct application of PLS does not come with coverage guarantees with respect to the true solution of the linear system. This work introduces a new method to calibrate the uncertainty sets produced by PLS with the aim of guaranteeing long-term coverage requirements. The proposed method, referred to as online conformal prediction-PLS (OCP-PLS), assumes sporadic feedback from cloud to edge. This enables the online calibration of uncertainty thresholds via online conformal prediction (OCP), an online optimization method previously studied in the context of prediction models. The validity of OCP-PLS is verified via experiments that bring insights into trade-offs between coverage, prediction set size, and cloud usage.

In massive multi-input multi-output (MIMO) systems, the main bottlenecks of location- and orientation-assisted beam alignment using deep neural networks (DNNs) are large training overhead and significant performance degradation. This paper proposes a graph neural network (GNN)-based beam selection approach that reduces the training overhead and improves the alignment accuracy, by capitalizing on the strong expressive ability and few trainable parameters of GNN. The channels of beams are correlated according to the beam direction. Therefore, we establish a graph according to the angular correlation between beams and use GNN to capture the channel correlation between adjacent beams, which helps accelerate the learning process and enhance the beam alignment performance. Compared to existing DNN-based algorithms, the proposed method requires only 20\% of the dataset size to achieve equivalent accuracy and improves the Top-1 accuracy by 10\% when using the same dataset.

Deep learning-based joint source-channel coding (JSCC) is emerging as a promising technology for effective image transmission. However, most existing approaches focus on transmitting clear images, overlooking real-world challenges such as motion blur caused by camera shaking or fast-moving objects. Motion blur often degrades image quality, making transmission and reconstruction more challenging. Event cameras, which asynchronously record pixel intensity changes with extremely low latency, have shown great potential for motion deblurring tasks. However, the efficient transmission of the abundant data generated by event cameras remains a significant challenge. In this work, we propose a novel JSCC framework for the joint transmission of blurry images and events, aimed at achieving high-quality reconstructions under limited channel bandwidth. This approach is designed as a deblurring task-oriented JSCC system. Since RGB cameras and event cameras capture the same scene through different modalities, their outputs contain both shared and domain-specific information. To avoid repeatedly transmitting the shared information, we extract and transmit their shared information and domain-specific information, respectively. At the receiver, the received signals are processed by a deblurring decoder to generate clear images. Additionally, we introduce a multi-stage training strategy to train the proposed model. Simulation results demonstrate that our method significantly outperforms existing JSCC-based image transmission schemes, addressing motion blur effectively.

Recently, semantic communication (SC) has garnered increasing attention for its efficiency, yet it remains vulnerable to semantic jamming attacks. These attacks entail introducing crafted perturbation signals to legitimate signals over the wireless channel, thereby misleading the receivers' semantic interpretation. This paper investigates the above issue from a practical perspective. Contrasting with previous studies focusing on power-fixed attacks, we extensively consider a more challenging scenario of power-variable attacks by devising an innovative attack model named Adjustable Perturbation Generator (APG), which is capable of generating semantic jamming signals of various power levels. To combat semantic jamming attacks, we propose a novel framework called Robust Model Ensembling (ROME) for secure semantic communication. Specifically, ROME can detect the presence of semantic jamming attacks and their power levels. When high-power jamming attacks are detected, ROME adapts to raise its robustness at the cost of generalization ability, and thus effectively accommodating the attacks. Furthermore, we theoretically analyze the robustness of the system, demonstrating its superiority in combating semantic jamming attacks via adaptive robustness. Simulation results show that the proposed ROME approach exhibits significant adaptability and delivers graceful robustness and generalization ability under power-variable semantic jamming attacks.

Recently, deep learning (DL)-based semantic communication has emerged as a promising technique for enhancing transmission efficiency in various tasks. By transmitting the extracted semantic features instead of the original data, the communication delay can be significantly reduced. In this work, we design a semantic communication technique for low-latency device-edge co-inference systems which supports multi-stage semantic feature transmission. To reduce the average inference delay, we develop a multi-stage edge inference scheme and the corresponding early stopping strategy with the proposed semantic communication network. The edge inference would be directly terminated if the inference result at any stage is regarded as reliable. We also develop an algorithm to jointly optimize the transmission duration and decision threshold for both the two-stage and multi-stage inference schemes, aiming at minimizing the average inference delay while satisfying the inference accuracy requirement. Simulation results show that the proposed multi-stage edge inference scheme with early stopping strategy can effectively reduce the average edge inference delay.