pwc-archive/papers-with-abstracts huggingface dataset:
Heuristic Deep Reinforcement Learning for Phase Shift Optimization in RIS-assisted Secure Satellite Communication Systems with RSMA
Authors: Tingnan Bao, Melike Erol-Kantarci
Date: 2025-01-21 00:00:00
Tasks: Computational Efficiency, Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning

This paper presents a novel heuristic deep reinforcement learning (HDRL) framework designed to optimize reconfigurable intelligent surface (RIS) phase shifts in secure satellite communication systems utilizing rate splitting multiple access (RSMA). The proposed HDRL approach addresses the challenges of large action spaces inherent in deep reinforcement learning by integrating heuristic algorithms, thus improving exploration efficiency and leading to faster convergence toward optimal solutions. We validate the effectiveness of HDRL through comprehensive simulations, demonstrating its superiority over traditional algorithms, including random phase shift, greedy algorithm, exhaustive search, and Deep Q-Network (DQN), in terms of secure sum rate and computational efficiency. Additionally, we compare the performance of RSMA with non-orthogonal multiple access (NOMA), highlighting that RSMA, particularly when implemented with an increased number of RIS elements, significantly enhances secure communication performance. The results indicate that HDRL is a powerful tool for improving the security and reliability of RSMA satellite communication systems, offering a practical balance between performance and computational demands.
Machine Learning Based Probe Skew Correction for High-frequency BH Loop Measurements
Authors: Yakun Wang, Song Liu, Jun Wang, Binyu Cui, Jingrong Yang
Date: 2025-01-21 00:00:00

Experimental characterization of magnetic components has grown to be increasingly important to understand and model their behaviours in high-frequency PWM converters. The BH loop measurement is the only available approach to separate the core loss as an electrical method, which, however, is susceptive to the probe phase skew. As an alternative to the regular de-skew approaches based on hardware, this work proposes a novel machine-learning-based method to identify and correct the probe skew, which builds on the newly discovered correlation between the skew and the shape/trajectory of the measured BH loop. A special technique is proposed to artificially generate skewed images from measured waveforms as augmented training sets. A machine learning pipeline is developed with the Convolutional Neural Network (CNN) to treat the problem as an image-based prediction task. The trained model has demonstrated a high accuracy and generalizability in identifying the skew value from a BH loop unseen by the model, which enables the compensation of the skew to yield the corrected core loss value and BH loop.
A Comprehensive Metric for Resilience Evaluation of Power Distribution Systems under Cyber Attacks
Authors: Mitikiri Sagar Babu, Victor Sam Moses Babu, Vedantham Lakshmi Srinivas, Pratyush Chakraborty, Mayukha Pal
Date: 2025-01-21 00:00:00

Power distribution systems (PDS) serve as the backbone of our modern society, ensuring electricity reaches homes, businesses, and critical infrastructure. However, the increasing digitization and interconnectivity of these systems have exposed them to cyber threats. This study presents a comprehensive approach to evaluate and enhance the resilience of PDS under cyber attacks using the Common Vulnerability Scoring System (CVSS) and complex network parameters. By systematically assessing vulnerabilities and computing resilience once critical CVSS thresholds are reached, this work identifies key resilience metrics including the critical loads service requirements. The proposed methodology improves system resilience through strategic tie-line switching, which is validated on the modified IEEE 33-bus system. Four case studies are conducted, illustrating the performance of the proposed methodology under various cyber attack scenarios. The results demonstrate the effectiveness of the approach in quantifying and enhancing resilience, offering a valuable tool for PDS operators to mitigate risks and ensure continuous service delivery to critical loads during the exploitation of cyber threats.
Diffeomorphic ICP Registration for Single and Multiple Point Sets
Authors: Adrien Wohrer
Date: 2025-01-21 00:00:00

We propose a generalization of the iterative closest point (ICP) algorithm for point set registration, in which the registration functions are non-rigid and follow the large deformation diffeomorphic metric mapping (LDDMM) framework. The algorithm is formulated as a well-posed probabilistic inference, and requires to solve a novel variation of LDDMM landmark registration with an additional term involving the Jacobian of the mapping. The algorithm can easily be generalized to construct a diffeomorphic, statistical atlas of multiple point sets. The method is successfully validated on a first set of synthetic data.
Wideband Pulse Generation for Underwater Applications Using Parametric Array
Authors: Yasin Kumru, Akif Sinan Taşdelen, Hayrettin Köymen
Date: 2025-01-21 00:00:00

We investigated wideband pulse generation for underwater acoustic applications using a parametric array. We fabricated a transducer consisting of a 3 mm thick 75 mm-by-75 mm square-shaped PZT ceramic plate, which is matched to water media at the radiating face and terminated by a very low impedance at the back. All measurements were made in a large test tank. We transmitted square-root amplitude modulated pulses centered around 855 kHz primary frequency. We showed that phase-sensitive generation of in-phase and out-of-phase bursts suitable for coded transmission using a parametric array is possible. We generated very short duration bursts, as short as half-cycle, at a 10-80 kHz difference frequency range. The definition of the bursts is excellent, e.g., with a normalized cross-correlation of 0.92 with an ideal 2-cycle square burst, for both in-phase and out-of-phase pulses.
Block Phase Tracking Reference Signal (PTRS) Allocation for DFT-s-OFDM
Authors: Mostafa Ibrahim, Sabit Ekin, Yingming, Tsai, Ravikumar Pragada
Date: 2025-01-20 00:00:00
Tasks: Noise Estimation

This study introduces a Block Phase Tracking Reference Signal (PTRS) allocation approach for Discrete Fourier Transform-spread-Orthogonal Frequency Division Multiplexing (DFT-s-OFDM) systems to enhance phase noise tracking and compensation. Our proposed block allocation methodology leverages the concepts of multiresolution time-frequency tiling for more effective sampling, thereby mitigating aliasing effects and improving phase noise resilience. A key contribution of our approach is a novel modulation and demodulation scheme, incorporating a dedicated DFT-s-OFDM symbol, a modulator branch for block PTRS generation, and a dedicated demodulator for accurate phase noise estimation and correction.
Generative AI-enabled Blockage Prediction for Robust Dual-Band mmWave Communication
Authors: Mohammad Ghassemi, Han Zhang, Ali Afana, Akram Bin Sediq, Melike Erol-Kantarci
Date: 2025-01-20 00:00:00
Tasks: Cloud Computing, Prediction

In mmWave wireless networks, signal blockages present a significant challenge due to the susceptibility to environmental moving obstructions. Recently, the availability of visual data has been leveraged to enhance blockage prediction accuracy in mmWave networks. In this work, we propose a Vision Transformer (ViT)-based approach for visual-aided blockage prediction that intelligently switches between mmWave and Sub-6 GHz frequencies to maximize network throughput and maintain reliable connectivity. Given the computational demands of processing visual data, we implement our solution within a hierarchical fog-cloud computing architecture, where fog nodes collaborate with cloud servers to efficiently manage computational tasks. This structure incorporates a generative AI-based compression technique that significantly reduces the volume of visual data transmitted between fog nodes and cloud centers. Our proposed method is tested with the real-world DeepSense 6G dataset, and according to the simulation results, it achieves a blockage prediction accuracy of 92.78% while reducing bandwidth usage by 70.31%.
Efficient Bearing Sensor Data Compression via an Asymmetrical Autoencoder with a Lifting Wavelet Transform Layer
Authors: Xin Zhu, Ahmet Enis Cetin
Date: 2025-01-20 00:00:00
Tasks: Data Compression, Decoder, Denoising

Bearing data compression is vital to manage the large volumes of data generated during condition monitoring. In this paper, a novel asymmetrical autoencoder with a lifting wavelet transform (LWT) layer is developed to compress bearing sensor data. The encoder part of the network consists of a convolutional layer followed by a wavelet filterbank layer. Specifically, a dual-channel convolutional block with diverse convolutional kernel sizes and varying processing depths is integrated into the wavelet filterbank layer to enable comprehensive feature extraction from the wavelet domain. Additionally, the adaptive hard-thresholding nonlinearity is applied to remove redundant components while denoising the primary wavelet coefficients. On the decoder side, inverse LWT, along with multiple linear layers and activation functions, is employed to reconstruct the original signals. Furthermore, to enhance compression efficiency, a sparsity constraint is introduced during training to impose sparsity on the latent representations. The experimental results demonstrate that the proposed approach achieves superior data compression performance compared to state-of-the-art methods.
Estimating Rural Path Loss with ITU-R P.1812-7 : Impact of Geospatial Inputs
Authors: Mathieu Chateauvert, Jonathan Ethier, Adrian Florea
Date: 2025-01-20 00:00:00

Accurate radio wave propagation modeling is essential for effective spectrum management by regulators and network deployment by operators. This paper investigates the ITU-R P.1812-7 (P.1812) propagation model's reliance on geospatial inputs, particularly clutter information, to improve path loss estimation, with an emphasis on rural geographic regions. The research evaluates the impact of geospatial elevation and land cover datasets, including Global Forest Canopy Height (GFCH), European Space Agency WorldCover, and Natural Resources Canada LandCover, on P.1812 propagation model prediction accuracy. Results highlight the trade-offs between dataset resolution, geospatial data availability, and representative clutter height assignments. Simulations reveal that high-resolution data do not always yield better results and that global datasets such as the GFCH provide a robust alternative when high-resolution data are unavailable or out-of-date. This study provides a set of guidelines for geospatial dataset integration to enhance P.1812's rural path loss predictions.
CDMA/OTFS Sensing Outperforms Pure OTFS at the Same Communication Throughput
Authors: Hugo Hawkins, Chao Xu, Lie-Liang Yang, Lajos Hanzo
Date: 2025-01-20 00:00:00
Tasks: Integrated sensing and communication, ISAC, parameter estimation

There is a dearth of publications on the subject of spreading-aided Orthogonal Time Frequency Space (OTFS) solutions, especially for Integrated Sensing and Communication (ISAC), even though Code Division Multiple Access (CDMA) assisted multi-user OTFS (CDMA/OTFS) exhibits tangible benefits. Hence, this work characterises both the communication Bit Error Rate (BER) and sensing Root Mean Square Error (RMSE) performance of CDMA/OTFS, and contrasts them to pure OTFS. Three CDMA/OTFS configurations are considered: Delay Code Division Multiple Access OTFS (Dl-CDMA/OTFS), Doppler Code Division Multiple Access OTFS (Dp-CDMA/OTFS), and Delay Doppler Code Division Multiple Access OTFS (DD-CDMA/OTFS), which harness direct sequence spreading along the delay axis, Doppler axis, and DD domains respectively. For each configuration, the performance of Gold, Hadamard, and Zadoff-Chu sequences is investigated. The results demonstrate that Zadoff-Chu Dl-CDMA/OTFS and DD-CDMA/OTFS consistently outperform pure OTFS sensing, whilst maintaining a similar communication performance at the same throughput. The extra modulation complexity of CDMA/OTFS is similar to that of other OTFS multi-user methodologies, but the demodulation complexity of CDMA/OTFS is lower than that of some other OTFS multi-user methodologies. CDMA/OTFS sensing can also consistently outperform OTFS sensing whilst not requiring any additional complexity for target parameter estimation. Therefore, CDMA/OTFS is an appealing candidate for implementing multi-user OTFS ISAC.
Faster-Than-Nyquist Equalization with Convolutional Neural Networks
Authors: Bruno De Filippo, Carla Amatetti, Alessandro Vanelli-Coralli
Date: 2025-01-20 00:00:00

Faster-than-Nyquist (FTN) signaling aims at improving the spectral efficiency of wireless communication systems by exceeding the boundaries set by the Nyquist-Shannon sampling theorem. 50 years after its first introduction in the scientific literature, wireless communications have significantly changed, but spectral efficiency remains one of the key challenges. To adopt FTN signaling, inter-symbol interference (ISI) patterns need to be equalized at the receiver. Motivated by the pattern recognition capabilities of convolutional neural networks with skip connections, we propose such deep learning architecture for ISI equalization and symbol demodulation in FTN receivers. We investigate the performance of the proposed model considering quadrature phase shift keying modulation and low density parity check coding, and compare it to a set of benchmarks, including frequency-domain equalization, a quadratic-programming-based receiver, and an equalization scheme based on a deep neural network. We show that our receiver outperforms any benchmark, achieving error rates comparable to those in additive white Gaussian noise channel, and higher effective throughput, thanks to the increased spectral efficiency of FTN signaling. With a compression factor of 60% and code rate 3/4, the proposed model achieves a peak effective throughput of 2.5 Mbps at just 10dB of energy per bit over noise power spectral density ratio, with other receivers being limited by error floors due to the strong inter-symbol interference. To promote reproducibility in deep learning for wireless communications, our code is open source at the repository provided in the references.
Integrated Long-range Sensing and Communications in Multi Target Scenarios using CP-OFDM
Authors: Benedikt Geiger, Silvio Mandelli, Marcus Henninger, Daniel Gil Gaviria, Charlotte Muth, Laurent Schmalen
Date: 2025-01-20 00:00:00

6G communication systems promise to deliver sensing capabilities by utilizing the orthogonal frequency division multiplexing (OFDM) communication signal for sensing. However, the cyclic prefix inherent in OFDM systems limits the sensing range, necessitating compensation techniques to detect small, distant targets like drones. In this paper, we show that state-of-the-art coherent compensation methods fail in scenarios involving multiple targets, resulting in an increased noise floor in the radar image. Our contributions include a novel multi target coherent compensation algorithm and a generalized signal-to-interference-and-noise ratio for multiple targets to evaluate the performance. Our algorithm achieves the same detection performance at long distances requiring only 3.6% of the radio resources compared to classical OFDM radar processing. This enables resource efficient sensing at long distances in multi target scenarios with legacy communications-only networks.
Joint Optimization of Geometric and Probabilistic Constellation Shaping for OFDM-ISAC Systems
Authors: Benedikt Geiger, Fan Liu, Shihang Lu, Andrej Rode, Laurent Schmalen
Date: 2025-01-20 00:00:00
Tasks: ISAC

6G communications systems are expected to integrate radar-like sensing capabilities enabling novel use cases. However, integrated sensing and communications (ISAC) introduces a trade-off between communications and sensing performance because the optimal constellations for each task differ. In this paper, we compare geometric, probabilistic and joint constellation shaping for orthogonal frequency division multiplexing (OFDM)-ISAC systems using an autoencoder (AE) framework. We first derive the constellation-dependent detection probability and propose a novel loss function to include the sensing performance in the AE framework. Our simulation results demonstrate that constellation shaping enables a dynamic trade-off between communications and sensing. Depending on whether sensing or communications performance is prioritized, geometric or probabilistic constellation shaping is preferred. Joint constellation shaping combines the advantages of geometric and probabilistic shaping, significantly outperforming legacy modulation formats.
Efficient Multi-Source Localization in Near-Field Using only Angular Domain MUSIC
Authors: Mehdi Haghshenas, Aamir Mahmood, Mikael Gidlund
Date: 2025-01-20 00:00:00

The localization of multiple signal sources using sensor arrays has been a long-standing research challenge. While numerous solutions have been developed, signal space methods like MUSIC and ESPRIT have gained widespread popularity. As sensor arrays grow in size, sources are frequently located in the near-field region. The standard MUSIC algorithm can be adapted to locate these sources by performing a 3D search over both the distance and the angles of arrival (AOA), including azimuth and elevation, though this comes with significant computational complexity. To address this, a modified version of MUSIC has been developed to decouple the AoA and distance, enabling sequential estimation of these parameters and reducing computational demands. However, this approach suffers from reduced accuracy. To maintain the accuracy of MUSIC while minimizing complexity, this paper proposes a novel method that exploits angular variation across the array aperture, eliminating the need for a grid search over distance. The proposed method divides the large aperture into smaller sections, with each focusing on estimating the angles of arrival. These angles are then triangulated to localize the sources in the near-field of the large aperture. Numerical simulations show that this approach not only surpasses the Modified MUSIC algorithm in terms of mean absolute error but also achieves accuracy comparable to standard MUSIC, all while greatly reducing computational complexity-370 times in our simulation scenario.
Optimized Beamforming for Joint Bistatic Positioning and Monostatic Sensing
Authors: Yuchen Zhang, Hui Chen, Pinjun Zheng, Boyu Ning, Henk Wymeersch, Tareq Y. Al-Naffouri
Date: 2025-01-20 00:00:00

We investigate the performance tradeoff between \textit{bistatic positioning (BP)} and \textit{monostatic sensing (MS)} in a multi-input multi-output orthogonal frequency division multiplexing scenario. We derive the Cram\'er-Rao bounds (CRBs) for BP at the user equipment and MS at the base station. To balance these objectives, we propose a multi-objective optimization framework that optimizes beamformers using a weighted-sum CRB approach, ensuring the weak Pareto boundary. We also introduce two mismatch-minimizing approaches, targeting beamformer mismatch and variance matrix mismatch, and solve them distinctly. Numerical results demonstrate the performance tradeoff between BP and MS, revealing significant gains with the proposed methods and highlighting the advantages of minimizing the weighted-sum mismatch of variance matrices.
Resilience of LTE-A/5G-NR links Against Transient Electromagnetic Interference
Authors: Sharzeel Saleem, Mir Lodro
Date: 2025-01-20 00:00:00

This paper presents a comparative analysis of long-term evolution advanced (LTE-A) and fifth-generation new radio (5G-NR), focusing on the effects of Transient Electromagnetic Interference (EMI) caused by catenary-pantograph contact in a railway environment. We developed a software-defined radio (SDR)-based prototype for the performance evaluation of LTE-A and 5G-NR links in the presence of transient interference. The results show that both links experience considerable degradation due to interference at different center frequencies. Performance degradation is proportional to the gain of interference. The measurement results show that both links experience considerable performance degradation in the presence of transient EM interference.
Practical Modulo Sampling: Mitigating High-Frequency Components
Authors: Yhonatan Kvich, Shlomi Savariego, Moshe Namer, Yonina C. Eldar
Date: 2025-01-20 00:00:00
Tasks: Quantization

Recovering signals within limited dynamic range (DR) constraints remains a central challenge for analog-to-digital converters (ADCs). To prevent data loss, an ADCs DR typically must exceed that of the input signal. Modulo sampling has recently gained attention as a promising approach for addressing DR limitations across various signal classes. However, existing methods often rely on ideal ADCs capable of capturing the high frequencies introduced by the modulo operator, which is impractical in real-world hardware applications. This paper introduces an innovative hardware-based sampling approach that addresses these high-frequency components using an analog mixer followed by a Low-Pass Filter (LPF). This allows the use of realistic ADCs, which do not need to handle frequencies beyond the intended sampling rate. Our method eliminates the requirement for high-specification ADCs and demonstrates that the resulting samples are equivalent to those from an ideal high-spec ADC. Consequently, any existing modulo recovery algorithm can be applied effectively. We present a practical hardware prototype of this approach, validated through both simulations and hardware recovery experiments. Using a recovery method designed to handle quantization noise, we show that our approach effectively manages high-frequency artifacts, enabling reliable modulo recovery with realistic ADCs. These findings confirm that our hardware solution not only outperforms conventional methods in high-precision settings but also demonstrates significant real-world applicability.
SIG-SDP: Sparse Interference Graph-Aided Semidefinite Programming for Large-Scale Wireless Time-Sensitive Networking
Authors: Zhouyou Gu, Jihong Park, Branka Vucetic, Jinho Choi
Date: 2025-01-20 00:00:00

Wireless time-sensitive networking (WTSN) is essential for Industrial Internet of Things. We address the problem of minimizing time slots needed for WTSN transmissions while ensuring reliability subject to interference constraints -- an NP-hard task. Existing semidefinite programming (SDP) methods can relax and solve the problem but suffer from high polynomial complexity. We propose a sparse interference graph-aided SDP (SIG-SDP) framework that exploits the interference's sparsity arising from attenuated signals between distant user pairs. First, the framework utilizes the sparsity to establish the upper and lower bounds of the minimum number of slots and uses binary search to locate the minimum within the bounds. Here, for each searched slot number, the framework optimizes a positive semidefinite (PSD) matrix indicating how likely user pairs share the same slot, and the constraint feasibility with the optimized PSD matrix further refines the slot search range. Second, the framework designs a matrix multiplicative weights (MMW) algorithm that accelerates the optimization, achieved by only sparsely adjusting interfering user pairs' elements in the PSD matrix while skipping the non-interfering pairs. We also design an online architecture to deploy the framework to adjust slot assignments based on real-time interference measurements. Simulations show that the SIG-SDP framework converges in near-linear complexity and is highly scalable to large networks. The framework minimizes the number of slots with up to 10 times faster computation and up to 100 times lower packet loss rates than compared methods. The online architecture demonstrates how the algorithm complexity impacts dynamic networks' performance.