pwc-archive/papers-with-abstracts huggingface dataset:
Learning-Based Two-Way Communications: Algorithmic Framework and Comparative Analysis
Authors: David R. Nickel, Anindya Bijoy Das, David J. Love, Christopher G. Brinton
Date: 2025-04-22 00:00:00

Machine learning (ML)-based feedback channel coding has garnered significant research interest in the past few years. However, there has been limited research exploring ML approaches in the so-called "two-way" setting where two users jointly encode messages and feedback for each other over a shared channel. In this work, we present a general architecture for ML-based two-way feedback coding, and show how several popular one-way schemes can be converted to the two-way setting through our framework. We compare such schemes against their one-way counterparts, revealing error-rate benefits of ML-based two-way coding in certain signal-to-noise ratio (SNR) regimes. We then analyze the tradeoffs between error performance and computational overhead for three state-of-the-art neural network coding models instantiated in the two-way paradigm.
Underwater Multi-Wavelength Optical Links With Blue Targets and Constraints: Opportunities and Challenges
Authors: Alain R. Ndjiongue, Octavia A. Dobre, Hyundong Shin, Harald Haas
Date: 2025-04-21 00:00:00

Underwater optical wireless technologies offer multiple advantages over the acoustic technology. Acoustic signals, for instance, are susceptible to noise from marine sources due to marine life and human activities. This is not the case with optical signals. However, absorption and scattering significantly attenuate optical signals. This limits the communication range and requires higher transmission power or more sensitive receivers to detect transmitted light. Therefore, it is necessary to design underwater optical systems with a higher transmission rate and reduced attenuation. To this end, we introduce a framework for designing optical signaling constellations employing multi-wavelength light sources to account for the transmission distance and achievable rate. In particular, we redefine the color-shift keying (CSK) constraint region to target blue light and adapt to marine environments. We discuss an example of 4-point underwater CSK. The corresponding analytical results demonstrate the trade-offs between the symbol error probability, achievable rate, and transmission range of the proposed scheme.
Joint Knowledge and Power Management for Secure Semantic Communication Networks
Authors: Xuesong Liu, Yansong Liu, Haoyu Tang, Fangzhou Zhao, Le Xia, Yao Sun
Date: 2025-04-21 00:00:00
Tasks: Management, Semantic Communication

Recently, semantic communication (SemCom) has shown its great superiorities in resource savings and information exchanges. However, while its unique background knowledge guarantees accurate semantic reasoning and recovery, semantic information security-related concerns are introduced at the same time. Since the potential eavesdroppers may have the same background knowledge to accurately decrypt the private semantic information transmitted between legal SemCom users, this makes the knowledge management in SemCom networks rather challenging in joint consideration with the power control. To this end, this paper focuses on jointly addressing three core issues of power allocation, knowledge base caching (KBC), and device-to-device (D2D) user pairing (DUP) in secure SemCom networks. We first develop a novel performance metric, namely semantic secrecy throughput (SST), to quantify the information security level that can be achieved at each pair of D2D SemCom users. Next, an SST maximization problem is formulated subject to secure SemCom-related delay and reliability constraints. Afterward, we propose a security-aware resource management solution using the Lagrange primal-dual method and a two-stage method. Simulation results demonstrate our proposed solution nearly doubles the SST performance and realizes less than half of the queuing delay performance compared to different benchmarks.
Time-Series Analysis on Edge-AI Hardware for Healthcare Monitoring
Authors: Jinhai Hu
Date: 2025-04-21 00:00:00
Tasks: EEG, Time Series, Time Series Analysis

This project addresses the need for efficient, real-time analysis of biomedical signals such as electrocardiograms (ECG) and electroencephalograms (EEG) for continuous health monitoring. Traditional methods rely on long-duration data recording followed by offline analysis, which is power-intensive and delays responses to critical symptoms such as arrhythmia. To overcome these limitations, a time-domain ECG analysis model based on a novel dynamically-biased Long Short-Term Memory (DB-LSTM) neural network is proposed. This model supports simultaneous ECG forecasting and classification with high performance-achieving over 98% accuracy and a normalized mean square error below 1e-3 for forecasting, and over 97% accuracy with faster convergence and fewer training parameters for classification. To enable edge deployment, the model is hardware-optimized by quantizing weights to INT4 or INT3 formats, resulting in only a 2% and 6% drop in classification accuracy during training and inference, respectively, while maintaining full accuracy for forecasting. Extensive simulations using multiple ECG datasets confirm the model's robustness. Future work includes implementing the algorithm on FPGA and CMOS circuits for practical cardiac monitoring, as well as developing a digital hardware platform that supports flexible neural network configurations and on-chip online training for personalized healthcare applications.
Bayesian Sensing for Time-Varying Channels in ISAC Systems
Authors: Xueyang Wang, Kai Wu, J. Andrew Zhang, Shiqi Gong, Chengwen Xing
Date: 2025-04-21 00:00:00
Tasks: Bayesian Inference, ISAC

Future mobile networks are projected to support integrated sensing and communications in high-speed communication scenarios. Nevertheless, large Doppler shifts induced by time-varying channels may cause severe inter-carrier interference (ICI). Frequency domain shows the potential of reducing ISAC complexity as compared with other domains. However, parameter mismatching issue still exists for such sensing. In this paper, we develop a novel sensing scheme based on sparse Bayesian framework, where the delay and Doppler estimation problem in time-varying channels is formulated as a 3D multiple measurement-sparse signal recovery (MM-SSR) problem. We then propose a novel two-layer variational Bayesian inference (VBI) method to decompose the 3D MM-SSR problem into two layers and estimate the Doppler in the first layer and the delay in the second layer alternatively. Subsequently, as is benefited from newly unveiled signal construction, a simplified two-stage multiple signal classification (MUSIC)-based VBI method is proposed, where the delay and the Doppler are estimated by MUSIC and VBI, respectively. Additionally, the Cram\'er-Rao bound (CRB) of the considered sensing parameters is derived to characterize the lower bound for the proposed estimators. Corroborated by extensive simulation results, our proposed method can achieve improved mean square error (MSE) than its conventional counterparts and is robust against the target number and target speed, thereby validating its wide applicability and advantages over prior arts.
The PHD/CPHD filter for Multiple Extended Target Tracking with Trajectory Set Theory and Explicit Shape Estimation
Authors: Yuanhao Cheng, Yunhe Cao, Tat-Soon Yeo, Fu Jie, Wei zhang
Date: 2025-04-21 00:00:00

In this paper, we propose two methods for tracking multiple extended targets or unresolved group targets with elliptical extent shape. These two methods are deduced from the famous Probability Hypothesis Density (PHD) filter and the Cardinality-PHD (CPHD) filter, respectively. In these two methods, Trajectory Set Theory (TST) is combined to establish the target trajectory estimates. Moreover, by employing a decoupled shape estimation model, the proposed methods can explicitly provide the shape estimation of the target, such as the orientation of the ellipse extension and the length of its two axes. We derived the closed Bayesian recursive of these two methods with stable trajectory generation and accurate extent estimation, resulting in the TPHD-E filter and the TCPHD-E filter. In addition, Gaussian mixture implementations of our methods are provided, which are further referred to as the GM-TPHD-E filter and the GM-TCPHD-E filters. We illustrate the ability of these methods through simulations and experiments with real data. These experiments demonstrate that the two proposed algorithms have advantages over existing algorithms in target shape estimation, as well as in the completeness and accuracy of target trajectory generation.
Blinding the Wiretapper: RIS-Enabled User Occultation in the ISAC Era
Authors: Getuar Rexhepi, Hyeon Seok Rou, Giuseppe Thadeu Freitas de Abreu, George C. Alexandropoulos
Date: 2025-04-21 00:00:00
Tasks: ISAC

An undesirable consequence of the foreseeable proliferation of sophisticated integrated sensing and communications (ISAC) technologies is the enabling of spoofing, by malicious agents, of situational information (such as proximity, direction or location) of legitimate users of wireless systems. In order to mitigate this threat, we present a novel ISAC scheme that, aided by a reconfigurable intelligent surface (RIS), enables the occultation of the positions of user equipment (UE) from wiretappers, while maintaining both sensing and desired communication performance between the UEs and a legitimate base station (BS). To that end, we first formulate an RIS phase-shift optimization problem that jointly maximizes the sum-rate performance of the UEs (communication objective), while minimizing the projection of the wiretapper's effective channel onto the legitimate channel (hiding objective), thereby disrupting the attempts by a wiretapper of localizing the UEs. Then, in order to efficiently solve the resulting non-convex joint optimization problem, a novel manifold optimization algorithm is derived, whose effectiveness is validated by numerical results, which demonstrate that the proposed approach preserves legitimate ISAC performance while significantly degrading the wiretapper's sensing capability.
A Self-supervised Learning Method for Raman Spectroscopy based on Masked Autoencoders
Authors: Pengju Ren, Ri-gui Zhou, Yaochong Li
Date: 2025-04-21 00:00:00
Tasks: Clustering, Deep Clustering, Denoising, Self-Supervised Learning

Raman spectroscopy serves as a powerful and reliable tool for analyzing the chemical information of substances. The integration of Raman spectroscopy with deep learning methods enables rapid qualitative and quantitative analysis of materials. Most existing approaches adopt supervised learning methods. Although supervised learning has achieved satisfactory accuracy in spectral analysis, it is still constrained by costly and limited well-annotated spectral datasets for training. When spectral annotation is challenging or the amount of annotated data is insufficient, the performance of supervised learning in spectral material identification declines. In order to address the challenge of feature extraction from unannotated spectra, we propose a self-supervised learning paradigm for Raman Spectroscopy based on a Masked AutoEncoder, termed SMAE. SMAE does not require any spectral annotations during pre-training. By randomly masking and then reconstructing the spectral information, the model learns essential spectral features. The reconstructed spectra exhibit certain denoising properties, improving the signal-to-noise ratio (SNR) by more than twofold. Utilizing the network weights obtained from masked pre-training, SMAE achieves clustering accuracy of over 80% for 30 classes of isolated bacteria in a pathogenic bacterial dataset, demonstrating significant improvements compared to classical unsupervised methods and other state-of-the-art deep clustering methods. After fine-tuning the network with a limited amount of annotated data, SMAE achieves an identification accuracy of 83.90% on the test set, presenting competitive performance against the supervised ResNet (83.40%).
A Purely Data-Driven Adaptive Impedance Matching Method Robust to Parasitic Effects
Authors: Wendong Cheng, Li Chen, Weidong Wang
Date: 2025-04-21 00:00:00

Adaptive impedance matching between antennas and radio frequency front-end (RFFE) power modules is essential for mobile communication systems. To address the matching performance degradation caused by parasitic effects in practical tunable matching networks (TMN), this paper proposes a purely data-driven adaptive impedance matching method that avoids trial-and-error physical adjustment. First, we propose the residual enhanced circuit behavior modeling network (RECBM-Net), a deep learning model that maps TMN operating states to their scattering parameters (S-parameters). Then, we formulate the matching process based on the trained surrogate model as a mathematical optimization problem. We employ two classic numerical methods with different online computational overhead, namely simulated annealing particle swarm optimization (SAPSO) and adaptive moment estimation with automatic differentiation (AD-Adam), to search for the matching solution. To further reduce the online inference overhead caused by repeated forward propagation through RECBM-Net, we train an inverse mapping solver network (IMS-Net) to directly predict the optimal solution. Simulation results show that RECBM-Net achieves exceptionally high modeling accuracy. While AD-Adam significantly reduces computational overhead compared to SAPSO, it sacrifices slight accuracy. IMS-Net offers the lowest online overhead while maintaining excellent matching accuracy.
Delay-Angle Information Spoofing for Channel State Information-Free Location-Privacy Enhancement
Authors: Jianxiu Li, Urbashi Mitra
Date: 2025-04-21 00:00:00

In this paper, a delay-angle information spoofing (DAIS) strategy is proposed to enhance the location privacy at the physical layer. More precisely, the location-relevant delays and angles are artificially shifted without the aid of channel state information (CSI) at the transmitter, such that the location perceived by the eavesdropper is incorrect and distinct from the true one. By leveraging the intrinsic structure of the wireless channel, a precoder is designed to achieve DAIS while the legitimate localizer can remove the obfuscation via securely receiving a modest amount of information, i.e., the delay-angle shifts. A lower bound on eavesdropper's localization error is derived, revealing that location privacy is enhanced not only due to estimation error, but also by the geometric mismatch introduced by DAIS. Furthermore, the lower bound is explicitly expressed as a function of the delay-angle shifts, characterizing performance trends and providing the appropriate design of these shift parameters. The statistical hardness of maliciously inferring the delay-angle shifts by a single-antenna eavesdropper as well as the challenges for a multi-antenna eavesdropper are investigated to assess the robustness of the proposed DAIS strategy. Numerical results show that the proposed DAIS strategy results in more than 15 dB performance degradation for the eavesdropper as compared with that for the legitimate localizer at high signal-to-noise ratios, and provides more effective location-privacy enhancement than the prior art.
Max-Min Fairness for Stacked Intelligent Metasurface-Assisted Multi-User MISO Systems
Authors: Nipuni Ginige, Prathapasinghe Dharmawansa, Arthur Sousa de Sena, Nurul Huda Mahmood, Nandana Rajatheva, Matti Latva-aho
Date: 2025-04-20 00:00:00
Tasks: Fairness, Stochastic Optimization

Stacked intelligent metasurface (SIM) is an emerging technology that uses multiple reconfigurable surface layers to enable flexible wave-based beamforming. In this paper, we focus on an \ac{SIM}-assisted multi-user multiple-input single-output system, where it is essential to ensure that all users receive a fair and reliable service level. To this end, we develop two max-min fairness algorithms based on instantaneous channel state information (CSI) and statistical CSI. For the instantaneous CSI case, we propose an alternating optimization algorithm that jointly optimizes power allocation using geometric programming and wave-based beamforming coefficients using the gradient descent-ascent method. For the statistical CSI case, since deriving an exact expression for the average minimum achievable rate is analytically intractable, we derive a tight upper bound and thereby formulate a stochastic optimization problem. This problem is then solved, capitalizing on an alternating approach combining geometric programming and gradient descent algorithms, to obtain the optimal policies. Our numerical results show significant improvements in the minimum achievable rate compared to the benchmark schemes. In particular, for the instantaneous CSI scenario, the individual impact of the optimal wave-based beamforming is significantly higher than that of the power allocation strategy. Moreover, the proposed upper bound is shown to be tight in the low signal-to-noise ratio regime under the statistical CSI.
Beamforming Design and Association Scheme for Multi-RIS Multi-User mmWave Systems Through Graph Neural Networks
Authors: Mengbing Liu, Chongwen Huang, Ahmed Alhammadi, Marco Di Renzo, Merouane Debbah, Chau Yuen
Date: 2025-04-20 00:00:00
Tasks: Blocking, Graph Neural Network

Reconfigurable intelligent surface (RIS) is emerging as a promising technology for next-generation wireless communication networks, offering a variety of merits such as the ability to tailor the communication environment. Moreover, deploying multiple RISs helps mitigate severe signal blocking between the base station (BS) and users, providing a practical and efficient solution to enhance the service coverage. However, fully reaping the potential of a multi-RIS aided communication system requires solving a non-convex optimization problem. This challenge motivates the adoption of learning-based methods for determining the optimal policy. In this paper, we introduce a novel heterogeneous graph neural network (GNN) to effectively leverage the graph topology of a wireless communication environment. Specifically, we design an association scheme that selects a suitable RIS for each user. Then, we maximize the weighted sum rate (WSR) of all the users by iteratively optimizing the RIS association scheme, and beamforming designs until the considered heterogeneous GNN converges. Based on the proposed approach, each user is associated with the best RIS, which is shown to significantly improve the system capacity in multi-RIS multi-user millimeter wave (mmWave) communications. Specifically, simulation results demonstrate that the proposed heterogeneous GNN closely approaches the performance of the high-complexity alternating optimization (AO) algorithm in the considered multi-RIS aided communication system, and it outperforms other benchmark schemes. Moreover, the performance improvement achieved through the RIS association scheme is shown to be of the order of 30%.
Iterative Polynomial Approximation Algorithms for Inverse Graph Filters
Authors: Cheng Cheng, Qiyu Sun, Cong Zheng
Date: 2025-04-19 00:00:00

Chebyshev interpolation polynomials exhibit the exponential approximation property to analytic functions on a cube. Based on the Chebyshev interpolation polynomial approximation, we propose iterative polynomial approximation algorithms to implement the inverse filter with a polynomial graph filter of commutative graph shifts in a distributed manner. The proposed algorithms exhibit exponential convergence properties, and they can be implemented on distributed networks in which agents are equipped with a data processing subsystem for limited data storage and computation power, and with a one-hop communication subsystem for direct data exchange only with their adjacent agents. Our simulations show that the proposed polynomial approximation algorithms may converge faster than the Chebyshev polynomial approximation algorithm and the conventional gradient descent algorithm do.
Exploiting Symmetric Non-Convexity for Multi-Objective Symbol-Level DFRC Signal Design
Authors: Ly V. Nguyen, Rang Liu, Nhan Thanh Nguyen, Markku Juntti, Björn Ottersten, A. Lee Swindlehurst
Date: 2025-04-19 00:00:00

Symbol-level precoding (SLP) is a promising solution for addressing the inherent interference problem in dual-functional radar-communication (DFRC) signal designs. This paper considers an SLP-DFRC signal design problem which optimizes the radar performance under communication performance constraints. We show that a common phase modulation applied to the transmit signals from an antenna array does not affect the performance of different radar sensing metrics, including beampattern similarity, signal-to-interference-plus-noise ratio (SINR), and Cram\'er-Rao lower bound (CRLB). We refer to this as symmetric-rotation invariance, upon which we develop low-complexity yet efficient DFRC signal design algorithms. More specifically, we propose a symmetric non-convexity (SNC)-based DFRC algorithm that relies on the non-convexity of the radar sensing metrics to identify a set of radar-only solutions. Based on these solutions, we further exploit the symmetry property of the radar sensing metrics to efficiently design the DFRC signal. We show that the proposed SNC-based algorithm is versatile in the sense that it can be applied to the DFRC signal optimization of all three sensing metrics mentioned above (beampattern, SINR, and CRLB). In addition, since the radar sensing metrics are independent of the communication channel and data symbols, the set of radar-only solutions can be constructed offline, thereby reducing the computational complexity. We also develop an accelerated SNC-based algorithm that further reduces the complexity. Finally, we numerically demonstrate the superiority of the proposed algorithms compared to existing methods in terms of sensing and communication performance as well as computational requirements.
A Real-time and Hardware Efficient Artfecat-free Spike Sorting Using Deep Spike Detection
Authors: Xiaoyu Jiang, Tao Fang, Majid Zamani
Date: 2025-04-19 00:00:00
Tasks: channel selection, Spike Sorting

Spike sorting is a valuable tool in understanding brain regions. It assigns detected spike waveforms to their origins, helping to research the mechanism of the human brain and the development of implantable brain-machine interfaces (iBMIs). The presence of noise and artefacts will adversely affect the efficacy of spike sorting. This paper proposes a framework for low-cost and real-time implementation of deep spike detection, which consists of two one-dimensional (1-D) convolutional neural network (CNN) model for channel selection and artefact removal. The framework utilizes simulation and hardware layers, and it applies several low-power techniques to optimise the implementation cost of a 1-D CNN model. A compact CNN model with 210 bytes memory size is achieved using structured pruning, network projection and quantization in the simulation layer. The hardware layer also accommodates various techniques including a customized multiply-accumulate (MAC) engine, novel fused layers in the convolution pipeline and proposing flexible resource allocation for a power-efficient and low-delay design. The optimized 1-D CNN significantly decreases both computational complexity and model size, with only a minimal reduction in accuracy. Classification of 1-D CNN on the Cyclone V 5CSEMA5F31C6 FPGA evaluation platform is accomplished in just 16.8 microseconds at a frequency of 2.5 MHz. The FPGA prototype achieves an accuracy rate of 97.14% on a standard dataset and operates with a power consumption of 2.67mW from a supply voltage of 1.1 volts. An accuracy of 95.05% is achieved with a power of 5.6mW when deep spike detection is implemented using two optimized 1-D CNNs on an FPGA board.