pwc-archive/papers-with-abstracts huggingface dataset:
Pinching-Antenna System (PASS) Enhanced Covert Communications
Authors: Hao Jiang, Zhaolin Wang, Yuanwei Liu
Date: 2025-04-14 00:00:00
Tasks: Position

A Pinching-Antenna SyStem (PASS)-assisted convert communication framework is proposed. PASS utilizes dielectric waveguides with freely positioned pinching antennas (PAs) to establish strong line-of-sight links. Capitalizing on this high reconfigurable flexibility of antennas, the potential of PASS for covert communications is investigated. 1)~For the single-waveguide single-PA (SWSP) scenario, a closed-form optimal PA position that maximizes the covert rate is first derived. Subsequently, a one-dimensional power search is employed to enable low-complexity optimization for covert communications. With antenna mobility on a scale of meters, PASS can deal with the challenging situation of the eavesdropper enjoying better channel conditions than the legal user. 2)~For the multi-waveguide multi-PA (MWMP) scenario, the positions of multiple PAs are optimized to enable effective pinching beamforming, thereby enhancing the covert rate. To address the resultant multimodal joint transmit and pinching beamforming problem, a twin particle swarm optimization (TwinPSO) approach is proposed. Numerical results demonstrate that: i)~the proposed approaches can effectively resolve the optimization problems; ii)~PASS achieves a higher covert rate than conventional fixed-position antenna architectures; and iii)~with enhanced flexibility, the MWMP setup outperforms the SWSP counterpart.
The Communication and Computation Trade-off in Wireless Semantic Communications
Authors: Xuyang Chen, Chong Huang, Gaojie Chen, Daquan Feng, Pei Xiao
Date: 2025-04-14 00:00:00
Tasks: Deep Reinforcement Learning, Semantic Communication

Semantic communications have emerged as a crucial research direction for future wireless communication networks. However, as wireless systems become increasingly complex, the demands for computation and communication resources in semantic communications continue to grow rapidly. This paper investigates the trade-off between computation and communication in wireless semantic communications, taking into consideration transmission task delay and performance constraints within the semantic communication framework. We propose a novel tradeoff metric to analyze the balance between computation and communication in semantic transmissions and employ the deep reinforcement learning (DRL) algorithm to minimize this metric, thereby reducing the cost associated with balancing computation and communication. Through simulations, we analyze the tradeoff between computation and communication and demonstrate the effectiveness of optimizing this trade-off metric.
Tx and Rx IQ Imbalance Compensation for JCAS in 5G NR
Authors: Andreas Meingassner, Oliver Lang, Moritz Tockner, Bernhard Plaimer, Matthias Wagner, Günther Lindorfer, Michael Hofstadler, Mario Huemer
Date: 2025-04-14 00:00:00

Beside traditional communications, joint communications and sensing (JCAS) is gaining increasing relevance as a key enabler for next-generation wireless systems. The ability to accurately transmit and receive data is the basis for high-speed communications and precise sensing, where a fundamental requirement is an accurate in-phase (I) and quadrature-phase (Q) modulation. For sensing, imperfections in IQ modulation lead to two critical issues in the range-Doppler-map (RDM) in form of an increased noise floor and the presence of ghost objects, degrading the accuracy and reliability of the information in the RDM. This paper presents a low-complex estimation and compensation method to mitigate the IQ imbalance effects. This is achieved by utilizing, amongst others, the leakage signal, which is the direct signal from the transmitter to the receiver path, and is typically the strongest signal component in the RDM. The parameters of the IQ imbalance suppression structure are estimated based on a mixed complex-/real-valued bilinear filter approach, that considers IQ imbalance in the transmitter and the receiver of the JCAS-capable user equipment (UE). The UE uses a 5G New Radio (NR)-compliant orthogonal frequency-division multiplexing (OFDM) waveform with the system configuration assumed to be predefined from the communication side. To assess the effectiveness of the proposed approach, simulations are conducted, illustrating the performance in the suppression of IQ imbalance introduced distortions in the RDM.
Adaptive Sensor Steering Strategy Using Deep Reinforcement Learning for Dynamic Data Acquisition in Digital Twins
Authors: Collins O. Ogbodo, Timothy J. Rogers, Mattia Dal Borgo, David J. Wagg
Date: 2025-04-14 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning

This paper introduces a sensor steering methodology based on deep reinforcement learning to enhance the predictive accuracy and decision support capabilities of digital twins by optimising the data acquisition process. Traditional sensor placement techniques are often constrained by one-off optimisation strategies, which limit their applicability for online applications requiring continuous informative data assimilation. The proposed approach addresses this limitation by offering an adaptive framework for sensor placement within the digital twin paradigm. The sensor placement problem is formulated as a Markov decision process, enabling the training and deployment of an agent capable of dynamically repositioning sensors in response to the evolving conditions of the physical structure as represented by the digital twin. This ensures that the digital twin maintains a highly representative and reliable connection to its physical counterpart. The proposed framework is validated through a series of comprehensive case studies involving a cantilever plate structure subjected to diverse conditions, including healthy and damaged conditions. The results demonstrate the capability of the deep reinforcement learning agent to adaptively reposition sensors improving the quality of data acquisition and hence enhancing the overall accuracy of digital twins.
Simulation and Experimental Validation of Optical Camera Communication
Authors: Srivathsan Chakaravarthi Narasimman, Arokiaswami Alphones
Date: 2025-04-14 00:00:00

While simulation tools for visible light communication (VLC) with photo detectors (PDs) have been widely investigated, similar tools for optical camera communication (OCC) with complementary metal oxide semiconductor (CMOS) sensors are lacking in this regard. Camera based VLC systems have much lower data rates owing to camera exposure times. Among the few extant OCC simulation tools, none allow for simulation of images when exposure time is greater than the signal period. An accurate simulation of the OCC system can be used to improve the data rate and quality of performance. We propose a simple simulation technique for OCC which allows to test for system performance at frequencies beyond the camera shutter speed. This will allow much needed data rate improvement by operating at the actual frequency a decoding algorithm ceases detection instead of the exposure limit used now. We have tested the accuracy of simulation by comparing the detection success rates of simulated images with experimental images. The proposed simulation technique was shown to be accurate through experimental validation for two different cameras.
Uncertainty Propagation in the Fast Fourier Transform
Authors: Luca Schmid, Charlotte Muth, Laurent Schmalen
Date: 2025-04-14 00:00:00
Tasks: Bayesian Inference

We address the problem of uncertainty propagation in the discrete Fourier transform by modeling the fast Fourier transform as a factor graph. Building on this representation, we propose an efficient framework for approximate Bayesian inference using belief propagation (BP) and expectation propagation, extending its applicability beyond Gaussian assumptions. By leveraging an appropriate BP message representation and a suitable schedule, our method achieves stable convergence with accurate mean and variance estimates. Numerical experiments in representative scenarios from communications demonstrate the practical potential of the proposed framework for uncertainty-aware inference in probabilistic systems operating across both time and frequency domain.
Joint Localization and Synchronization in Downlink Distributed MIMO
Authors: Sauradeep Dey, Musa Furkan Keskin, Dario Tagliaferri, Gonzalo Seco-Granados, Henk Wymeersch
Date: 2025-04-14 00:00:00
Tasks: Position

We investigate joint localization and synchronization in the downlink of a distributed multiple-input-multiple-output (D-MIMO) system, aiming to estimate the position and phase offset of a single-antenna user equipment (UE) using downlink transmissions of multiple phase-synchronized, multi-antenna access points (APs). We propose two transmission protocols: sequential (P1) and simultaneous (P2) AP transmissions, together with the ML estimators that either leverage (coherent estimator) or disregard phase information (non-coherent estimator). Simulation results reveal that downlink D-MIMO holds significant potential for high-accuracy localization while showing that P2 provides superior localization performance and reduced transmission latency.
Parametric Near-Field MMSE Channel Estimation for sub-THz XL-MIMO Systems
Authors: Wen-Xuan Long, Marco Moretti, Michele Morelli, Luca Sanguinetti, Rui Chen
Date: 2025-04-14 00:00:00

Accurate channel estimation is essential for reliable communication in sub-THz extremely large (XL) MIMO systems. Deploying XL-MIMO in high-frequency bands not only increases the number of antennas, but also fundamentally alters channel propagation characteristics, placing the user equipments (UE) in the radiative near-field of the base station. This paper proposes a parametric estimation method using the multiple signal classification (MUSIC) algorithm to extract UE location data from uplink pilot signals. These parameters are used to reconstruct the spatial correlation matrix, followed by an approximation of the minimum mean square error (MMSE) channel estimator. Numerical results show that the proposed method outperforms the least-squares (LS) estimator in terms of the normalized mean-square error (NMSE), even without prior UE location knowledge.
Learning to Beamform for Cooperative Localization and Communication: A Link Heterogeneous GNN-Based Approach
Authors: Lixiang Lian, Chuanqi Bai, Yihan Xu, Huanyu Dong, Rui Cheng, Shunqing Zhang
Date: 2025-04-14 00:00:00
Tasks: Graph Attention, Graph Neural Network, Integrated sensing and communication, ISAC, parameter estimation, Position

Integrated sensing and communication (ISAC) has emerged as a key enabler for next-generation wireless networks, supporting advanced applications such as high-precision localization and environment reconstruction. Cooperative ISAC (CoISAC) further enhances these capabilities by enabling multiple base stations (BSs) to jointly optimize communication and sensing performance through coordination. However, CoISAC beamforming design faces significant challenges due to system heterogeneity, large-scale problem complexity, and sensitivity to parameter estimation errors. Traditional deep learning-based techniques fail to exploit the unique structural characteristics of CoISAC systems, thereby limiting their ability to enhance system performance. To address these challenges, we propose a Link-Heterogeneous Graph Neural Network (LHGNN) for joint beamforming in CoISAC systems. Unlike conventional approaches, LHGNN models communication and sensing links as heterogeneous nodes and their interactions as edges, enabling the capture of the heterogeneous nature and intricate interactions of CoISAC systems. Furthermore, a graph attention mechanism is incorporated to dynamically adjust node and link importance, improving robustness to channel and position estimation errors. Numerical results demonstrate that the proposed attention-enhanced LHGNN achieves superior communication rates while maintaining sensing accuracy under power constraints. The proposed method also exhibits strong robustness to communication channel and position estimation error.
Uniform Planar Array Based Weighted Cooperative Spectrum Sensing for Cognitive Radio Networks
Authors: Charith Dissanayake, Saman Atapattu, Prathapasinghe Dharmawansa, Jing Fu, Sumei Sun, Kandeepan Sithamparanathan
Date: 2025-04-14 00:00:00

Cooperative spectrum sensing (CSS) is essential for improving the spectrum efficiency and reliability of cognitive radio applications. Next-generation wireless communication networks increasingly employ uniform planar arrays (UPA) due to their ability to steer beamformers towards desired directions, mitigating interference and eavesdropping. However, the application of UPA-based CSS in cognitive radio remains largely unexplored. This paper proposes a multi-beam UPA-based weighted CSS (WCSS) framework to enhance detection reliability, applicable to various cognitive radio networks, including cellular, vehicular, and satellite communications. We first propose a weighting factor for commonly used energy detection (ED) and eigenvalue detection (EVD) techniques, based on the spatial variation of signal strengths resulting from UPA antenna beamforming. We then analytically characterize the performance of both weighted ED and weighted EVD by deriving closed-form expressions for false alarm and detection probabilities. Our numerical results, considering both static and dynamic user behaviors, demonstrate the superiority of WCSS in enhancing sensing performance compared to uniformly weighted detectors.
Parameter Convergence Detector Based on VAMP Deep Unfolding: A Novel Radar Constant False Alarm Rate Detection Algorithm
Authors: Haoyun Zhang, Jianghong Han, Xueqian Wang, Gang Li, Xiao-Ping Zhang
Date: 2025-04-14 00:00:00
Tasks: compressed sensing

The sub-Nyquist radar framework exploits the sparsity of signals, which effectively alleviates the pressure on system storage and transmission bandwidth. Compressed sensing (CS) algorithms, such as the VAMP algorithm, are used for sparse signal processing in the sub-Nyquist radar framework. By combining deep unfolding techniques with VAMP, faster convergence and higher accuracy than traditional CS algorithms are achieved. However, deep unfolding disrupts the parameter constrains in traditional VAMP algorithm, leading to the distribution of non-sparse noisy estimation in VAMP deep unfolding unknown, and its distribution parameter unable to be obtained directly using method of traditional VAMP, which prevents the application of VAMP deep unfolding in radar constant false alarm rate (CFAR) detection. To address this problem, we explore the distribution of the non-sparse noisy estimation and propose a parameter convergence detector (PCD) to achieve CFAR detection based on VAMP deep unfolding. Compared to the state-of-the-art methods, PCD leverages not only the sparse solution, but also the non-sparse noisy estimation, which is used to iteratively estimate the distribution parameter and served as the test statistic in detection process. In this way, the proposed algorithm takes advantage of both the enhanced sparse recovery accuracy from deep unfolding and the distribution property of VAMP, thereby achieving superior CFAR detection performance. Additionally, the PCD requires no information about the power of AWGN in the environment, which is more suitable for practical application. The convergence performance and effectiveness of the proposed PCD are analyzed based on the Banach Fixed-Point Theorem. Numerical simulations and practical data experiments demonstrate that PCD can achieve better false alarm control and target detection performance.
A Novel Radar Constant False Alarm Rate Detection Algorithm Based on VAMP Deep Unfolding
Authors: Haoyun Zhang, Chengyang Zhang, Xueqian Wang, Gang Li, Xiao-Ping Zhang
Date: 2025-04-14 00:00:00
Tasks: Compressive Sensing

The combination of deep unfolding with vector approximate message passing (VAMP) algorithm, results in faster convergence and higher sparse recovery accuracy than traditional compressive sensing approaches. However, deep unfolding alters the parameters in traditional VAMP algorithm, resulting in the unattainable distribution parameter of the recovery error of non-sparse noisy estimation via traditional VAMP, which hinders the utilization of VAMP deep unfolding in constant false alarm rate (CFAR) detection in sub-Nyquist radar system. Based on VAMP deep unfolding, we provide a parameter convergence detector (PCD) to estimate the recovery error distribution parameter and implement CFAR detection. Compared to the state-of-the-art approaches, both the sparse solution and non-sparse noisy estimation are utilized to estimate the distribution parameter and implement CFAR detection in PCD, which leverages both the VAMP distribution property and the improved sparse recovery accuracy provided by deep unfolding. Simulation results indicate that PCD offers improved false alarm rate control performance and higher target detection rate.
Fusing Bluetooth with Pedestrian Dead Reckoning: A Floor Plan-Assisted Positioning Approach
Authors: Wenxuan Pan, Yang Yang, Mingzhe Chen, Dong Wei, Caili Guo, Shiwen Mao
Date: 2025-04-14 00:00:00
Tasks: Position

Floor plans can provide valuable prior information that helps enhance the accuracy of indoor positioning systems. However, existing research typically faces challenges in efficiently leveraging floor plan information and applying it to complex indoor layouts. To fully exploit information from floor plans for positioning, we propose a floor plan-assisted fusion positioning algorithm (FP-BP) using Bluetooth low energy (BLE) and pedestrian dead reckoning (PDR). In the considered system, a user holding a smartphone walks through a positioning area with BLE beacons installed on the ceiling, and can locate himself in real time. In particular, FP-BP consists of two phases. In the offline phase, FP-BP programmatically extracts map features from a stylized floor plan based on their binary masks, and constructs a mapping function to identify the corresponding map feature of any given position on the map. In the online phase, FP-BP continuously computes BLE positions and PDR results from BLE signals and smartphone sensors, where a novel grid-based maximum likelihood estimation (GML) algorithm is introduced to enhance BLE positioning. Then, a particle filter is used to fuse them and obtain an initial estimate. Finally, FP-BP performs post-position correction to obtain the final position based on its specific map feature. Experimental results show that FP-BP can achieve a real-time mean positioning accuracy of 1.19 m, representing an improvement of over 28% compared to existing floor plan-fused baseline algorithms.
CKMImageNet: A Dataset for AI-Based Channel Knowledge Map Towards Environment-Aware Communication and Sensing
Authors: Zijian Wu, Di wu, Shen Fu, Yuelong Qiu, Yong Zeng
Date: 2025-04-14 00:00:00

With the increasing demand for real-time channel state information (CSI) in sixth-generation (6G) mobile communication networks, channel knowledge map (CKM) emerges as a promising technique, offering a site-specific database that enables environment-awareness and significantly enhances communication and sensing performance by leveraging a priori wireless channel knowledge. However, efficient construction and utilization of CKMs require high-quality, massive, and location-specific channel knowledge data that accurately reflects the real-world environments. Inspired by the great success of ImageNet dataset in advancing computer vision and image understanding in artificial intelligence (AI) community, we introduce CKMImageNet, a dataset developed to bridge AI and environment-aware wireless communications and sensing by integrating location-specific channel knowledge data, high-fidelity environmental maps, and their visual representations. CKMImageNet supports a wide range of AI-driven approaches for CKM construction with spatially consistent and location-specific channel knowledge data, including both supervised and unsupervised, as well as discriminative and generative AI methods.
Research and Experimental Validation for 3GPP ISAC Channel Modeling Standardization
Authors: Yuxiang Zhang, Jianhua Zhang, Jiwei Zhang, Yuanpeng Pei, Yameng Liu, Lei Tian, Tao Jiang, Guangyi Liu
Date: 2025-04-14 00:00:00
Tasks: Integrated sensing and communication, ISAC

Integrated Sensing and Communication (ISAC) is considered a key technology in 6G networks. An accurate sensing channel model is crucial for the design and sensing performance evaluation of ISAC systems. The widely used Geometry-Based Stochastic Model (GBSM), typically applied in standardized channel modeling, mainly focuses on the statistical fading characteristics of the channel. However, it fails to capture the characteristics of targets in ISAC systems, such as their positions and velocities, as well as the impact of the targets on the background. To address this issue, this paper proposes an extended GBSM (E-GBSM) sensing channel model that incorporates newly discovered channel characteristics into a unified modeling framework. In this framework, the sensing channel is divided into target and background channels. For the target channel, the model introduces a concatenated modeling approach, while for the background channel, a parameter called the power control factor is introduced to assess impact of the target on the background channel, making the modeling framework applicable to both mono-static and bi-static sensing modes. To validate the proposed model's effectiveness, measurements of target and background channels are conducted in both indoor and outdoor scenarios, covering various sensing targets such as metal plates, reconfigurable intelligent surfaces, human bodies, UAVs, and vehicles. The experimental results provide important theoretical support and empirical data for the standardization of ISAC channel modeling.
Enhanced Filterless Multi-Color VLC via QCT
Authors: Idris Cinemre, Serkan Vela, Gokce Hacioglu
Date: 2025-04-13 00:00:00

Color shift keying (CSK) in visible light communication (VLC) often suffers from filter-induced crosstalk and reduced brightness. This paper proposes using quartered composite transform (QCT) with multi-color light-emitting diodes (LEDs) to improve both illumination and communication. The proposd DC-biased QCT scheme eliminates receiver optical filters, thereby removing crosstalk and significantly increasing signal-to-noise ratio (SNR). Simulations demonstrate QCT maintains high illumination quality (CRI 79.72, CCT 3462 K) while achieving over double the average illuminance compared to CSK under the same power budget. QCT also shows better bit error rate (BER) performance in low-to-moderate SNR regimes and has ability to convert multi-tap frequency-selective channel into an equivalent single-tap flat-fading channel to mitigate inter-symbol interference (ISI), proving a promising technique for brighter, high-performance, filter-less VLC.
Quantum Manifold Optimization: A Design Framework for Future Communications Systems
Authors: Getuar Rexhepi, Hyeon Seok Rou, Giuseppe Thadeu Freitas de Abreu
Date: 2025-04-13 00:00:00

Inspired by recent developments in various areas of science relevant to quantum computing, we introduce quantum manifold optimization (QMO) as a promising framework for solving constrained optimization problems in next-generation wireless communication systems. We begin by showing how classical wireless design problems - such as pilot design in cell-free (CF)-massive MIMO (mMIMO), beamformer optimization in gigantic multiple input multiple output (MIMO), and reconfigurable intelligent surface (RIS) phase tuning - naturally reside on structured manifolds like the Stiefel, Grassmannian, and oblique manifolds, with the latter novelly formulated in this work. Then, we demonstrate how these problems can be reformulated as trace-based quantum expectation values over variationally-encoded quantum states. While theoretical in scope, the work lays a foundation for a new class of quantum optimization algorithms with broad application to the design of future beyond-sixth-generation (B6G) systems.