pwc-archive/papers-with-abstracts huggingface dataset:
Generalizable Learning for Frequency-Domain Channel Extrapolation under Distribution Shift
Authors: Haoyu Wang, Zhi Sun, Shuangfeng Han, XiaoYun Wang, Zhaocheng Wang
Date: 2025-05-20 00:00:00

Frequency-domain channel extrapolation is effective in reducing pilot overhead for massive multiple-input multiple-output (MIMO) systems. Recently, Deep learning (DL) based channel extrapolator has become a promising candidate for modeling complex frequency-domain dependency. Nevertheless, current DL extrapolators fail to operate in unseen environments under distribution shift, which poses challenges for large-scale deployment. In this paper, environment generalizable learning for channel extrapolation is achieved by realizing distribution alignment from a physics perspective. Firstly, the distribution shift of wireless channels is rigorously analyzed, which comprises the distribution shift of multipath structure and single-path response. Secondly, a physics-based progressive distribution alignment strategy is proposed to address the distribution shift, which includes successive path-oriented design and path alignment. Path-oriented DL extrapolator decomposes multipath channel extrapolation into parallel extrapolations of the extracted path, which can mitigate the distribution shift of multipath structure. Path alignment is proposed to address the distribution shift of single-path response in path-oriented DL extrapolators, which eventually enables generalizable learning for channel extrapolation. In the simulation, distinct wireless environments are generated using the precise ray-tracing tool. Based on extensive evaluations, the proposed path-oriented DL extrapolator with path alignment can reduce extrapolation error by more than 6 dB in unseen environments compared to the state-of-the-arts.
RainfalLTE: A Zero-effect Rainfall Sensing System Utilizing Existing LTE Infrastructure
Authors: Xianbin Jiang, Fei Shang, Haohua Du, Panlong Yang, Xing Guo, Lihong Liang, YuanTing Zhang, Xiang-Yang Li
Date: 2025-05-20 00:00:00
Tasks: Integrated sensing and communication, ISAC

Environmental sensing is an important research topic in the integrated sensing and communication (ISAC) system. Current works often focus on static environments, such as buildings and terrains. However, dynamic factors like rainfall can cause serious interference to wireless signals. In this paper, we propose a system called RainfalLTE that utilizes the downlink signal of LTE base stations for device-independent rain sensing. In articular, it is fully compatible with current communication modes and does not require any additional hardware. We evaluate it with LTE data and rainfall information provided by a weather radar in Badaling Town, Beijing The results show that for 10 classes of rainfall, RainfalLTE achieves over 97% identification accuracy. Our case study shows that the assistance of rainfall information can bring more than 40% energy saving, which provides new opportunities for the design and optimization of ISAC systems.
Non-Invasive Arterial Pulse Detection with Millimeter-wave Radar and Comparison With Photoplethysmography
Authors: Nima Bahmani, Dariush Salami, Hüseyin Yiğitler, Juhapekka Hietala, Tuukka Panula, Stephan Sigg
Date: 2025-05-19 00:00:00

Cardiovascular diseases remain a leading cause of mortality and disability. The convenient measurement of cardiovascular health using smart systems is therefore a key enabler to foster accurate and early detection and diagnosis of cardiovascular diseases and it require accessing a correct pulse morphology similar to arterial pressure wave. This paper investigates the comparison between different sensor modalities, such as mmWave and photoplethysmography from the same physiological site and reference continuous non-invasive blood pressure devide. We have developed a hardware prototype and established an experiment consist of 23 test participants. Both mmWave and PPG are capable of detecting inter-beat intervals. mmWave is providing more accurate arterial pulse waveform than green photoplethysmography.
Distributed Beamforming Using Decentralized Time Synchronization in a Six-Element Array
Authors: Naim Shandi, Jason M. Merlo, Jeffrey A. Nanzer
Date: 2025-05-19 00:00:00
Tasks: Distributed Computing

We demonstrate a distributed beamforming and beamsteering from a six-node distributed phased array using fully wireless coordination with decentralized time synchronization. In wireless applications such as distributed beamforming, high-accuracy time synchronization across the array is crucial for high coherent gain. The decentralized time synchronization method employed is based on the average consensus algorithm and the two-way time transfer method presented in our previous work, which achieved picosecond time synchronization with a cabled frequency reference. The system presented in this paper utilizes a centralized wireless frequency transfer method to achieve wireless frequency syntonization in a fully wireless coordination and a distributed computing system architecture. We experimentally evaluate system performance through beamforming and beamsteering to a receiver 16.3 m away from the six-node non-uniformly distributed antenna array, achieving an average coherent gain of 98% of the ideal gain at a carrier frequency of 1.05 GHz. The average time synchronization accuracy achieved was less than 36 ps.
Multi-Reference and Adaptive Nonlinear Transform Source-Channel Coding for Wireless Image Semantic Transmission
Authors: Cheng Yuan, Yufei Jiang, Xu Zhu
Date: 2025-05-19 00:00:00

We propose a multi-reference and adaptive nonlinear transform source-channel coding (MA-NTSCC) system for wireless image semantic transmission to improve rate-distortion (RD) performance by introducing multi-dimensional contexts into the entropy model of the state-of-the-art (SOTA) NTSCC system. Improvements in RD performance of the proposed MA-NTSCC system are particularly significant in high-resolution image transmission under low bandwidth constraints. The proposed multi-reference entropy model leverages correlations within the latent representation in both spatial and channel dimensions. In the spatial dimension, the latent representation is divided into anchors and non-anchors in a checkerboard pattern, where anchors serve as reference to estimate the mutual information between anchors and non-anchors. In the channel dimension, the latent representation is partitioned into multiple groups, and features in previous groups are analyzed to estimate the mutual information between features in previous and current groups. Taking mutual information into account, the entropy model provides an accurate estimation on the entropy, which enables efficient bandwidth allocation and enhances RD performance. Additionally, the proposed lightweight adaptation modules enable the proposed MA-NTSCC model to achieve transmission quality comparable to separately trained models across various channel conditions and bandwidth requirements. In contrast, traditional NTSCC models provide signal-to-noise ratio (SNR)-distortion performance degrading with channel quality deviating from the fixed training SNR, and consume inflexible bandwidth to transmit an image. Comprehensive experiments are conducted to verify the peak signal-to-noise ratio (PSNR) performance and adaptability of the proposed MA-NTSCC model superior to SOTA methods over both additive white Gaussian noise channel and Rayleigh fading channel.
Bridging the Modality Gap: Enhancing Channel Prediction with Semantically Aligned LLMs and Knowledge Distillation
Authors: Zhaoyang Li, Qianqian Yang, Zehui Xiong, Zhiguo Shi, Tony Q. S. Quek
Date: 2025-05-19 00:00:00
Tasks: Knowledge Distillation, Prediction, Time Series Prediction, Word Embeddings

Accurate channel prediction is essential in massive multiple-input multiple-output (m-MIMO) systems to improve precoding effectiveness and reduce the overhead of channel state information (CSI) feedback. However, existing methods often suffer from accumulated prediction errors and poor generalization to dynamic wireless environments. Large language models (LLMs) have demonstrated remarkable modeling and generalization capabilities in tasks such as time series prediction, making them a promising solution. Nevertheless, a significant modality gap exists between the linguistic knowledge embedded in pretrained LLMs and the intrinsic characteristics of CSI, posing substantial challenges for their direct application to channel prediction. Moreover, the large parameter size of LLMs hinders their practical deployment in real-world communication systems with stringent latency constraints. To address these challenges, we propose a novel channel prediction framework based on semantically aligned large models, referred to as CSI-ALM, which bridges the modality gap between natural language and channel information. Specifically, we design a cross-modal fusion module that aligns CSI representations . Additionally, we maximize the cosine similarity between word embeddings and CSI embeddings to construct semantic cues. To reduce complexity and enable practical implementation, we further introduce a lightweight version of the proposed approach, called CSI-ALM-Light. This variant is derived via a knowledge distillation strategy based on attention matrices. Extensive experimental results demonstrate that CSI-ALM achieves a 1 dB gain over state-of-the-art deep learning methods. Moreover, under limited training data conditions, CSI-ALM-Light, with only 0.34M parameters, attains performance comparable to CSI-ALM and significantly outperforms conventional deep learning approaches.
Bistatic Sensing in 5G NR
Authors: Rajeev Gangula, Sakthivel Velumani, Tommaso Melodia
Date: 2025-05-18 00:00:00
Tasks: Integrated sensing and communication, ISAC

In this work, we propose and evaluate the performance of a 5th generation (5G) New Radio (NR) bistatic Integrated Sensing and Communication (ISaC) system. Unlike the full-duplex monostatic ISaC systems, the bistatic approach enables sensing in the current cellular networks without significantly modifying the transceiver design. The sensing utilizes data channels, such as the Physical Uplink Shared Channel (PUSCH), which carries information on the air interface. We provide the maximum likelihood estimator for the delay and Doppler parameters and derive a lower bound on the Mean Square Error (MSE) for a single target scenario. Link-level simulations show that it is possible to achieve significant throughput while accurately estimating the sensing parameters with PUSCH. Moreover, the results reveal an interesting tradeoff between the number of reference symbols, sensing performance, and throughput in the proposed 5G NR bistatic ISaC system.
Toward Near-Space Communication Network in the 6G and Beyond Era
Authors: Xinhua Liu, Zhen Gao, Ziwei Wan, Zhonghuai Wu, Tuan Li, Tianqi Mao, Xiao Liang, Dezhi Zheng, Jun Zhang
Date: 2025-05-18 00:00:00

Near-space communication network (NS-ComNet), as an indispensable component of sixth-generation (6G) and beyond mobile communication systems and the space-air-ground-sea integrated network (SAGSIN), demonstrates unique advantages in wide-area coverage, long-endurance high-altitude operation, and highly flexible deployment. This paper presents a comprehensive review of NS-ComNet for 6G and beyond era. Specifically, by contrasting satellite, low-altitude unmanned-aerial-vehicle (UAV), and terrestrial communications, we first elucidate the background and motivation for integrating NS-ComNet into 6G network architectures. Subsequently, we review the developmental status of near-space platforms, including high-altitude balloons, solar-powered UAVs, and stratospheric airships, and analyze critical challenges faced by NS-ComNet. To address these challenges, the research focuses on key enabling technologies such as topology design, resource and handover management, multi-objective joint optimization, etc., with particular emphasis on artificial intelligence techniques for NS-ComNet. Finally, envisioning future intelligent collaborative networks that integrate NS-ComNet with satellite-UAV-terrestrial systems, we explore promising directions. This paper aims to provide technical insights and research foundations for the systematic construction of NS-ComNet and its deep deployment in the 6G and beyond era.
Simultaneously Exposing and Jamming Covert Communications via Disco Reconfigurable Intelligent Surfaces
Authors: Huan Huang, Hongliang Zhang, Yi Cai, Dusit Niyato, A. Lee Swindlehurst, Zhu Han
Date: 2025-05-18 00:00:00

Covert communications provide a stronger privacy protection than cryptography and physical-layer security (PLS). However, previous works on covert communications have implicitly assumed the validity of channel reciprocity, i.e., wireless channels remain constant or approximately constant during their coherence time. In this work, we investigate covert communications in the presence of a disco RIS (DRIS) deployed by the warden Willie, where the DRIS with random and time-varying reflective coefficients acts as a "disco ball", introducing timevarying fully-passive jamming (FPJ). Consequently, the channel reciprocity assumption no longer holds. The DRIS not only jams the covert transmissions between Alice and Bob, but also decreases the error probabilities of Willie's detections, without either Bob's channel knowledge or additional jamming power. To quantify the impact of the DRIS on covert communications, we first design a detection rule for the warden Willie in the presence of time-varying FPJ introduced by the DRIS. Then, we define the detection error probabilities, i.e., the false alarm rate (FAR) and the missed detection rate (MDR), as the monitoring performance metrics for Willie's detections, and the signal-to-jamming-plusnoise ratio (SJNR) as a communication performance metric for the covert transmissions between Alice and Bob. Based on the detection rule, we derive the detection threshold for the warden Willie to detect whether communications between Alice and Bob is ongoing, considering the time-varying DRIS-based FPJ. Moreover, we conduct theoretical analyses of the FAR and the MDR at the warden Willie, as well as SJNR at Bob, and then present unique properties of the DRIS-based FPJ in covert communications. We present numerical results to validate the derived theoretical analyses and evaluate the impact of DRIS on covert communications.
UAV-Enabled Joint Sensing, Communication, Powering and Backhaul Transmission in Maritime Monitoring Networks
Authors: Bohan Li, Jiahao Liu, Yujun Liang, Qian Li, Haochen Liu, Yaoyuan Zhang, Junsheng Mu, Shahid Mumtaz, Sheng Chen
Date: 2025-05-18 00:00:00
Tasks: Scheduling

This paper addresses the challenge of energy-constrained maritime monitoring networks by proposing an unmanned aerial vehicle (UAV)-enabled integrated sensing, communication, powering and backhaul transmission scheme with a tailored time-division duplex frame structure. Within each time slot, the UAV sequentially implements sensing, wireless charging and uplink receiving with buoys, and lastly forwards part of collected data to the central ship via backhaul links. Considering the tight coupling among these functions, we jointly optimize time allocation, UAV trajectory, UAV-buoy association, and power scheduling to maximize the performance of data collection, with the practical consideration of sea clutter effects during UAV sensing. A novel optimization framework combining alternating optimization, quadratic transform and augmented first-order Taylor approximation is developed, which demonstrates good convergence behavior and robustness. Simulation results show that under sensing quality-of-service constraint, buoys are able to achieve an average data rate over 22bps/Hz using around 2mW harvested power per active time slot, validating the scheme's effectiveness for open-sea monitoring. Additionally, it is found that under the influence of sea clutters, the optimal UAV trajectory always keeps a certain distance with buoys to strike a balance between sensing and other multi-functional transmissions.
OFDM Based Bistatic Integrated Sensing and Communication: Sensing Beyond CP Limit
Authors: Cuneyd Ozturk, Cagri Goken
Date: 2025-05-17 00:00:00
Tasks: Integrated sensing and communication, ISAC

This work investigates a bistatic OFDM-based integrated sensing and communication (ISAC) system under a single-target scenario, considering both line-of-sight (LOS) presence and LOS blockage cases. A sliding window-based sensing receiver architecture is proposed to extend the intersymbol interference (ISI)-free sensing range beyond the cyclic prefix (CP) duration by exploiting pilot symbols embedded in the time-frequency grid. The performance of the proposed receiver is evaluated in terms of range and velocity estimation accuracy and is compared against the Cramer-Rao bounds (CRBs) for the bi-static ISAC setting. Numerical results confirm that the proposed method achieves estimation performance that closely approaches the CRBs in the high signal-to-noise ratio (SNR) regime.
Fine-Grained ECG-Text Contrastive Learning via Waveform Understanding Enhancement
Authors: Haitao Li, Che Liu, Zhengyao Ding, Ziyi Liu, Zhengxing Huang
Date: 2025-05-17 00:00:00
Tasks: Contrastive Learning, Diagnostic, Semantic Similarity, Semantic Textual Similarity

Electrocardiograms (ECGs) are essential for diagnosing cardiovascular diseases. While previous ECG-text contrastive learning methods have shown promising results, they often overlook the incompleteness of the reports. Given an ECG, the report is generated by first identifying key waveform features and then inferring the final diagnosis through these features. Despite their importance, these waveform features are often not recorded in the report as intermediate results. Aligning ECGs with such incomplete reports impedes the model's ability to capture the ECG's waveform features and limits its understanding of diagnostic reasoning based on those features. To address this, we propose FG-CLEP (Fine-Grained Contrastive Language ECG Pre-training), which aims to recover these waveform features from incomplete reports with the help of large language models (LLMs), under the challenges of hallucinations and the non-bijective relationship between waveform features and diagnoses. Additionally, considering the frequent false negatives due to the prevalence of common diagnoses in ECGs, we introduce a semantic similarity matrix to guide contrastive learning. Furthermore, we adopt a sigmoid-based loss function to accommodate the multi-label nature of ECG-related tasks. Experiments on six datasets demonstrate that FG-CLEP outperforms state-of-the-art methods in both zero-shot prediction and linear probing across these datasets.
S-Crescendo: A Nested Transformer Weaving Framework for Scalable Nonlinear System in S-Domain Representation
Authors: Junlang Huang, Hao Chen, Li Luo, Yong Cai, Lexin Zhang, Tianhao Ma, Yitian Zhang, Zhong Guan
Date: 2025-05-17 00:00:00

Simulation of high-order nonlinear system requires extensive computational resources, especially in modern VLSI backend design where bifurcation-induced instability and chaos-like transient behaviors pose challenges. We present S-Crescendo - a nested transformer weaving framework that synergizes S-domain with neural operators for scalable time-domain prediction in high-order nonlinear networks, alleviating the computational bottlenecks of conventional solvers via Newton-Raphson method. By leveraging the partial-fraction decomposition of an n-th order transfer function into first-order modal terms with repeated poles and residues, our method bypasses the conventional Jacobian matrix-based iterations and efficiently reduces computational complexity from cubic $O(n^3)$ to linear $O(n)$.The proposed architecture seamlessly integrates an S-domain encoder with an attention-based correction operator to simultaneously isolate dominant response and adaptively capture higher-order non-linearities. Validated on order-1 to order-10 networks, our method achieves up to 0.99 test-set ($R^2$) accuracy against HSPICE golden waveforms and accelerates simulation by up to 18(X), providing a scalable, physics-aware framework for high-dimensional nonlinear modeling.
Joint Precoding and Probabilistic Constellation Shaping using Arithmetic Distribution Matching
Authors: Babaee Ramin, Oveis Gharan Shahab, Bouchard Martin
Date: 2025-05-16 00:00:00

The problem of joint shaping and precoding is studied in this paper. We introduce statistical dependencies among consecutive symbols to shape the constellation while minimizing the total transmit power when the signal goes through the precoding filter. We propose a stationary Markovian model for optimizing the transition probability of transmit symbols to avoid high-energy sequences when convolved with the precoding filter. A new algorithm based on arithmetic coding is proposed to generate a shaped sequence of symbols with the given Markov model transition probabilities.
Joint Graph Estimation and Signal Restoration for Robust Federated Learning
Authors: Tsutahiro Fukuhara, Junya Hara, Hiroshi Higashi, Yuichi Tanaka
Date: 2025-05-16 00:00:00
Tasks: Federated Learning, Graph Learning, Missing Values

We propose a robust aggregation method for model parameters in federated learning (FL) under noisy communications. FL is a distributed machine learning paradigm in which a central server aggregates local model parameters from multiple clients. These parameters are often noisy and/or have missing values during data collection, training, and communication between the clients and server. This may cause a considerable drop in model accuracy. To address this issue, we learn a graph that represents pairwise relationships between model parameters of the clients during aggregation. We realize it with a joint problem of graph learning and signal (i.e., model parameters) restoration. The problem is formulated as a difference-of-convex (DC) optimization, which is efficiently solved via a proximal DC algorithm. Experimental results on MNIST and CIFAR-10 datasets show that the proposed method outperforms existing approaches by up to $2$--$5\%$ in classification accuracy under biased data distributions and noisy conditions.
Benchmarking Spatiotemporal Reasoning in LLMs and Reasoning Models: Capabilities and Challenges
Authors: Pengrui Quan, Brian Wang, Kang Yang, Liying Han, Mani Srivastava
Date: 2025-05-16 00:00:00
Tasks: Benchmarking, State Estimation, World Knowledge

Spatiotemporal reasoning plays a key role in Cyber-Physical Systems (CPS). Despite advances in Large Language Models (LLMs) and Large Reasoning Models (LRMs), their capacity to reason about complex spatiotemporal signals remains underexplored. This paper proposes a hierarchical SpatioTemporal reAsoning benchmaRK, STARK, to systematically evaluate LLMs across three levels of reasoning complexity: state estimation (e.g., predicting field variables, localizing and tracking events in space and time), spatiotemporal reasoning over states (e.g., inferring spatial-temporal relationships), and world-knowledge-aware reasoning that integrates contextual and domain knowledge (e.g., intent prediction, landmark-aware navigation). We curate 26 distinct spatiotemporal tasks with diverse sensor modalities, comprising 14,552 challenges where models answer directly or by Python Code Interpreter. Evaluating 3 LRMs and 8 LLMs, we find LLMs achieve limited success in tasks requiring geometric reasoning (e.g., multilateration or triangulation), particularly as complexity increases. Surprisingly, LRMs show robust performance across tasks with various levels of difficulty, often competing or surpassing traditional first-principle-based methods. Our results show that in reasoning tasks requiring world knowledge, the performance gap between LLMs and LRMs narrows, with some LLMs even surpassing LRMs. However, the LRM o3 model continues to achieve leading performance across all evaluated tasks, a result attributed primarily to the larger size of the reasoning models. STARK motivates future innovations in model architectures and reasoning paradigms for intelligent CPS by providing a structured framework to identify limitations in the spatiotemporal reasoning of LLMs and LRMs.