pwc-archive/papers-with-abstracts huggingface dataset:
Exploring the usage of Probabilistic Neural Networks for Ionospheric electron density estimation
Authors: Miquel Garcia-Fernandez
Date: 2025-03-08 00:00:00
Tasks: Density Estimation

A fundamental limitation of traditional Neural Networks (NN) in predictive modelling is their inability to quantify uncertainty in their outputs. In critical applications like positioning systems, understanding the reliability of predictions is critical for constructing confidence intervals, early warning systems, and effectively propagating results. For instance, Precise Point Positioning in satellite navigation heavily relies on accurate error models for ancillary data (orbits, clocks, ionosphere, and troposphere) to compute precise error estimates. In addition, these uncertainty estimates are needed to establish robust protection levels in safety critical applications. To address this challenge, the main objectives of this paper aims at exploring a potential framework capable of providing both point estimates and associated uncertainty measures of ionospheric Vertical Total Electron Content (VTEC). In this context, Probabilistic Neural Networks (PNNs) offer a promising approach to achieve this goal. However, constructing an effective PNN requires meticulous design of hidden and output layers, as well as careful definition of prior and posterior probability distributions for network weights and biases. A key finding of this study is that the uncertainty provided by the PNN model in VTEC estimates may be systematically underestimated. In low-latitude areas, the actual error was observed to be as much as twice the model's estimate. This underestimation is expected to be more pronounced during solar maximum, correlating with increased VTEC values.
Gradient-Driven Graph Neural Networks for Learning Digital and Hybrid Precoder
Authors: Lin Zhang, Shengqian Han, Chenyang Yang
Date: 2025-03-08 00:00:00
Tasks: Fairness

The optimization of multi-user multi-input multi-output (MU-MIMO) precoders is a widely recognized challenging problem. Existing work has demonstrated the potential of graph neural networks (GNNs) in learning precoding policies. However, existing GNNs often exhibit poor generalizability for the numbers of users or antennas. In this paper, we develop a gradient-driven GNN design method for the learning of fully digital and hybrid precoding policies. The proposed GNNs leverage two kinds of knowledge, namely the gradient of signal-to-interference-plus-noise ratio (SINR) to the precoders and the permutation equivariant property of the precoding policy. To demonstrate the flexibility of the proposed method for accommodating different optimization objectives and different precoding policies, we first apply the proposed method to learn the fully digital precoding policies. We study two precoder optimization problems for spectral efficiency (SE) maximization and log-SE maximization to achieve proportional fairness. We then apply the proposed method to learn the hybrid precoding policy, where the gradients to analog and digital precoders are exploited for the design of the GNN. Simulation results show the effectiveness of the proposed methods for learning different precoding policies and better generalization performance to the numbers of both users and antennas compared to baseline GNNs.
Physics-Informed Generative Approaches for Wireless Channel Modeling
Authors: Satyavrat Wagle, Akshay Malhotra, Shahab Hamidi-Rad, Aditya Sant, David J. Love, Christopher G. Brinton
Date: 2025-03-07 00:00:00

In recent years, machine learning (ML) methods have become increasingly popular in wireless communication systems for several applications. A critical bottleneck for designing ML systems for wireless communications is the availability of realistic wireless channel datasets, which are extremely resource intensive to produce. To this end, the generation of realistic wireless channels plays a key role in the subsequent design of effective ML algorithms for wireless communication systems. Generative models have been proposed to synthesize channel matrices, but outputs produced by such methods may not correspond to geometrically viable channels and do not provide any insight into the scenario of interest. In this work, we aim to address both these issues by integrating a parametric, physics-based geometric channel (PBGC) modeling framework with generative methods. To address limitations with gradient flow through the PBGC model, a linearized reformulation is presented, which ensures smooth gradient flow during generative model training, while also capturing insights about the underlying physical environment. We evaluate our model against prior baselines by comparing the generated samples in terms of the 2-Wasserstein distance and through the utility of generated data when used for downstream compression tasks.
Generative Multi-Agent Q-Learning for Policy Optimization: Decentralized Wireless Networks
Authors: Talha Bozkus, Urbashi Mitra
Date: 2025-03-07 00:00:00
Tasks: Q-Learning, Reinforcement Learning (RL)

Q-learning is a widely used reinforcement learning (RL) algorithm for optimizing wireless networks, but faces challenges with large state-spaces. Recently proposed multi-environment mixed Q-learning (MEMQ) algorithm addresses these challenges by employing multiple Q-learning algorithms across multiple synthetically generated, distinct but structurally related environments, so-called digital cousins. In this paper, we propose a novel multi-agent MEMQ (M-MEMQ) for cooperative decentralized wireless networks with multiple networked transmitters (TXs) and base stations (BSs). TXs do not have access to global information (joint state and actions). The new concept of coordinated and uncoordinated states is introduced. In uncoordinated states, TXs act independently to minimize their individual costs and update local Q-functions. In coordinated states, TXs use a Bayesian approach to estimate the joint state and update the joint Q-functions. The cost of information-sharing scales linearly with the number of TXs and is independent of the joint state-action space size. Several theoretical guarantees, including deterministic and probabilistic convergence, bounds on estimation error variance, and the probability of misdetecting the joint states, are given. Numerical simulations show that M-MEMQ outperforms several decentralized and centralized training with decentralized execution (CTDE) multi-agent RL algorithms by achieving 55% lower average policy error (APE), 35% faster convergence, 50% reduced runtime complexity, and 45% less sample complexity. Furthermore, M-MEMQ achieves comparable APE with significantly lower complexity than centralized methods. Simulations validate the theoretical analyses.
Revisiting Beamforming Design for Stable Millimeter-Wave Communications Under Blockages
Authors: Kanta Terui, Kabuto Arai, Koji Ishibashi
Date: 2025-03-07 00:00:00

This study examines analog beamforming designs utilizing multi-panel arrays for millimeter-wave (mmWave) communication systems under stochastic path blockages where each panel is an integrated circuit that includes power amplifiers, a limited number of antenna elements, and the corresponding phase shifters. In existing commercial mmWave systems, analog beams are typically designed by leveraging all panels cooperatively to align with the line-of-sight (LoS) path, thereby maximizing array gain. Although this beam design is effective in static channels, it is highly susceptible to frequent link disconnections caused by sudden path blockages. To address this challenge, the present study revisits the design of analog beamforming and proposes a multi-beam approach using multi-panel arrays to enhance robustness to path blockages. To evaluate the performance of the multi-beam with multimodal directivity, a theoretical analysis of the outage probability of the spectral efficiency (SE) is conducted. To design the optimal multi-beam based on the derived outage probability, we formulate a panel allocation problem to determine the assignment of panels to specific paths, including both LoS and non-line-of-sight (NLoS) paths. Numerical simulations confirm that the optimal beam, at high target SE, comprises a single sharp beam aligned to the LoS path to maximize array gain, whereas the optimal beam at low target SE is a multi-beam aligned to both the LoS and NLoS paths to acquire spatial diversity. These results demonstrate that the proposed multi-beam design, which utilizes multiple paths, effectively enhances the stability of mmWave communications while ensuring a minimum required performance level.
Noise-Robust Radio Frequency Fingerprint Identification Using Denoise Diffusion Model
Authors: Guolin Yin, Junqing Zhang, Yuan Ding, Simon Cotton
Date: 2025-03-07 00:00:00

Securing Internet of Things (IoT) devices presents increasing challenges due to their limited computational and energy resources. Radio Frequency Fingerprint Identification (RFFI) emerges as a promising authentication technique to identify wireless devices through hardware impairments. RFFI performance under low signal-to-noise ratio (SNR) scenarios is significantly degraded because the minute hardware features can be easily swamped in noise. In this paper, we leveraged the diffusion model to effectively restore the RFF under low SNR scenarios. Specifically, we trained a powerful noise predictor and tailored a noise removal algorithm to effectively reduce the noise level in the received signal and restore the device fingerprints. We used Wi-Fi as a case study and created a testbed involving 6 commercial off-the-shelf Wi-Fi dongles and a USRP N210 software-defined radio (SDR) platform. We conducted experimental evaluations on various SNR scenarios. The experimental results show that the proposed algorithm can improve the classification accuracy by up to 34.9%.
Deep Frequency Attention Networks for Single Snapshot Sparse Array Interpolation
Authors: Ruxin Zheng, Shunqiao Sun, Hongshan Liu
Date: 2025-03-07 00:00:00
Tasks: Missing Elements

Sparse arrays have been widely exploited in radar systems because of their advantages in achieving large array aperture at low hardware cost, while significantly reducing mutual coupling. However, sparse arrays suffer from high sidelobes which may lead to false detections. Missing elements in sparse arrays can be interpolated using the sparse array measurements. In snapshot-limited scenarios, such as automotive radar, it is challenging to utilize difference coarrays which require a large number of snapshots to construct a covariance matrix for interpolation. For single snapshot sparse array interpolation, traditional model-based methods, while effective, require expert knowledge for hyperparameter tuning, lack task-specific adaptability, and incur high computational costs. In this paper, we propose a novel deep learning-based single snapshot sparse array interpolation network that addresses these challenges by leveraging a frequency-domain attention mechanism. The proposed approach transforms the sparse signal into the frequency domain, where the attention mechanism focuses on key spectral regions, enabling improved interpolation of missing elements even in low signal-to-noise ratio (SNR) conditions. By minimizing computational costs and enhancing interpolation accuracy, the proposed method demonstrates superior performance compared to traditional approaches, making it well-suited for automotive radar applications.
Hiding in Plain Sight: RIS-Aided Target Obfuscation in ISAC
Authors: Ahmed Magbool, Vaibhav Kumar, Marco Di Renzo, Mark F. Flanagan
Date: 2025-03-07 00:00:00
Tasks: Integrated sensing and communication, ISAC

Integrated sensing and communication (ISAC) has been identified as a promising technology for the sixth generation (6G) of communication networks. Target privacy in ISAC is essential to ensure that only legitimate sensors can detect the target while keeping it hidden from malicious ones. In this paper, we consider a downlink reconfigurable intelligent surface (RIS)-assisted ISAC system capable of protecting a sensing region against an adversarial detector. The RIS consists of both reflecting and sensing elements, adaptively changing the element assignment based on system needs. To achieve this, we minimize the maximum sensing signal-to-interference-plus-noise-ratio (SINR) at the adversarial detector within sample points in the sensing region, by optimizing the transmit beamformer at the base station, the RIS phase shift matrix, the received beamformer at the RIS, and the division between reflecting and absorptive elements at the RIS, where the latter function as sensing elements. At the same time, the system is designed to maintain a minimum sensing SINR at each monitored location, as well as minimum communication SINR for each user. To solve this challenging optimization problem, we develop an alternating optimization approach combined with a successive convex approximation based method tailored for each subproblem. Our results show that the proposed approach achieves a 25 dB reduction in the maximum sensing SINR at the adversarial detector compared to scenarios without sensing area protection. Also, the optimal RIS element assignment can further improve sensing protection by 3 dB over RISs with fixed element configuration.
A Hybrid Approach for Extending Automotive Radar Operation to NLOS Urban Scenarios
Authors: Aviran Gal, Igal Bilik
Date: 2025-03-07 00:00:00
Tasks: Autonomous Driving

Automotive radar is a key component of sensing suites in autonomous driving (AD) and advanced driver-assist systems (ADAS). However, limited line-of-sight (LOS) significantly reduces radar efficiency in dense urban environments. Therefore, automotive radars need to extend their capabilities beyond LOS by localizing occluding and reflective surfaces and non-line-of-sight (NLOS) targets. This work addresses the NLOS target localization challenge by revisiting the NLOS radar signal propagation model and introducing a hybrid localization approach. The proposed approach first detects and localizes reflective surfaces, then identifies the LOS/NLOS propagation conditions, and finally localizes the target without prior scene knowledge, without using Doppler information, and without any auxiliary sensors. The proposed hybrid approach addresses the computational complexity challenge by integrating a physical radar electromagnetic wave propagation model with a deep neural network (DNN) to estimate occluding surface parameters. The efficiency of the proposed approach to localize the NLOS targets and to identify the NLOS/LOS propagation conditions is evaluated via simulations in a broad range of realistic automotive scenarios. Extending automotive radar sensing beyond LOS is expected to enhance the safety and reliability of autonomous and ADAS-equipped vehicles.
Energy-Free Sensing and Context Recognition Using Photovoltaic Cells
Authors: Kaede Shintani, Hamada Rizk, Hirozumi Yamaguchi
Date: 2025-03-07 00:00:00
Tasks: User Identification

The field of energy-free sensing and context recognition has recently gained significant attention as it allows operating systems without external power sources. Photovoltaic cells can convert light energy into electrical energy to power sensing devices, but their power may not be sufficient to ensure energy-free sensing due to the varying power needs of sensors and high computational demands. In this paper, we propose the use of photovoltaic cells as a standalone sensor for the recognition of different contexts, including user identification, step counting, and location tracking. The system utilizes the photocurrent readings generated by the photovoltaic cells to capture the unique mobility patterns of different users. By analyzing these patterns, the system can accurately identify the user, count the number of steps taken, and track the user's location. We propose a computationally efficient DTW to match the variable length sequences of photocurrent readings to a database of known patterns and identify the closest subject and location matches. The system was rigorously evaluated in a realistic environment, and the results indicate that it can accurately estimate step count, identify subjects, and localize them with an accuracy of 88%, 90%, and 43cm, respectively. This is achieved while the proposed system is non-intrusive and can operate without external power sources, making it a promising technology for energy-free sensing and context recognition.
Fluid Antenna System Empowering 5G NR
Authors: Hanjiang Hong, Kai-Kit Wong, Haoyang Li, Hao Xu, Han Xiao, Hyundong Shin, Kin-Fai Tong, Yangyang Zhang
Date: 2025-03-07 00:00:00
Tasks: Position

Fluid antenna system (FAS) is an emerging technology that uses the new form of shape- and position-reconfigurable antennas to empower the physical layer for wireless communications. Prior studies on FAS were however limited to narrowband channels. Motivated by this, this paper addresses the integration of FAS in the fifth generation (5G) orthogonal frequency division multiplexing (OFDM) framework to address the challenges posed by wideband communications. We propose the framework of the wideband FAS OFDM system that includes a novel port selection matrix. Then we derive the achievable rate expression and design the adaptive modulation and coding (AMC) scheme based on the rate. Extensive link-level simulation results demonstrate striking improvements of FAS in the wideband channels, underscoring the potential of FAS in future wireless communications.
Performance Analysis of Spatial and Temporal Learning Networks in the Presence of DVL Noise
Authors: Rajini Makam, Nadav Cohen, Sumukh Shadakshari, Srinivasa Puranika Bhatta, Itzik Klein, Suresh Sundaram
Date: 2025-03-07 00:00:00

Navigation is a critical aspect of autonomous underwater vehicles (AUVs) operating in complex underwater environments. Since global navigation satellite system (GNSS) signals are unavailable underwater, navigation relies on inertial sensing, which tends to accumulate errors over time. To mitigate this, the Doppler velocity log (DVL) plays a crucial role in determining navigation accuracy. In this paper, we compare two neural network models: an adapted version of BeamsNet, based on a one-dimensional convolutional neural network, and a Spectrally Normalized Memory Neural Network (SNMNN). The former focuses on extracting spatial features, while the latter leverages memory and temporal features to provide more accurate velocity estimates while handling biased and noisy DVL data. The proposed approaches were trained and tested on real AUV data collected in the Mediterranean Sea. Both models are evaluated in terms of accuracy and estimation certainty and are benchmarked against the least squares (LS) method, the current model-based approach. The results show that the neural network models achieve over a 50% improvement in RMSE for the estimation of the AUV velocity, with a smaller standard deviation.
Integrated Sensing, Communication, and Computation Over-the-Air in OFDM Systems
Authors: Biao Dong, Bin Cao, Qinyu Zhang
Date: 2025-03-07 00:00:00

This work is concerned with integrated sensing, communication, and computation (ISCC) in uplink orthogonal frequency division multiplexing (OFDM) systems, wherein multiple devices perform target sensing and over-the-air computation (AirComp) simultaneously. We aim to minimize the computational mean squared error (MSE) by jointly optimizing the transmitting vector and the aggregation vector. To tackle the non-convexity of this problem, we develop a two-phase iterative algorithm. Simulations demonstrate the effectiveness of the proposed algorithm.
Intelligent Reflecting Surface-Aided Electromagnetic Stealth over Extended Regions
Authors: Qingjie Wu, Beixiong Zheng, Guangchi Zhang, Derrick Wing Kwan Ng, A. Lee Swindlehurst
Date: 2025-03-07 00:00:00

Compared to traditional electromagnetic stealth (ES) materials, which are effective only within specific frequencies and orientations, intelligent reflecting surface (IRS) technology introduces a novel paradigm for achieving dynamic and adaptive ES by adapting its reflection pattern in real time to neutralize radar probing signals echoed back from the target. In this letter, we study an IRS-aided ES system mounted on an aerial target to evade radar detection admist uncertain/moving radar positions over an extended area. Specifically, we aim to optimize the IRS's passive reflection to minimize the maximum received signal-to-noise ratio (SNR) of the target echo signal in the area. A semi-closed-form solution is derived by first discretizing the continuous spatial frequency deviation to approximate the semi-infinite reflection gain constraint and then leveraging the Lagrange dual method. Simulation results are provided to validate that the proposed IRS-aided ES strategy can consistently reduce the reflection gains for radars located across a large region.
A Hybrid Model/Data-Driven Solution to Channel, Position and Orientation Tracking in mmWave Vehicular Systems
Authors: Yun Chen, Nuria González-Prelcic, Takayuki Shimizu, Chinmay Mahabal
Date: 2025-03-07 00:00:00
Tasks: Position

Channel tracking in millimeter wave (mmWave) vehicular systems is crucial for maintaining robust vehicle-to-infrastructure (V2I) communication links, which can be leveraged to achieve high accuracy vehicle position and orientation tracking as a byproduct of communication. While prior work tends to simplify the system model by omitting critical system factors such as clock offsets, filtering effects, antenna array orientation offsets, and channel estimation errors, we address the challenges of a practical mmWave multiple-input multiple-output (MIMO) communication system between a single base station (BS) and a vehicle while tracking the vehicle's position and orientation (PO) considering realistic driving behaviors. We first develop a channel tracking algorithm based on multidimensional orthogonal matching pursuit (MOMP) with factoring (F-MOMP) to reduce computational complexity and enable high-resolution channel estimates during the tracking stage, suitable for PO estimation. Then, we develop a network called VO-ChAT (Vehicle Orientation-Channel Attention for orientation Tracking), which processes the channel estimate sequence for orientation prediction. Afterward, a weighted least squares (WLS) problem that exploits the channel geometry is formulated to create an initial estimate of the vehicle's 2D position. A second network named VP-ChAT (Vehicle Position-Channel Attention for position Tracking) refines the geometric position estimate. VP-ChAT is a Transformer inspired network processing the historical channel and position estimates to provide the correction for the initial geometric position estimate. The proposed solution is evaluated using raytracing generated channels in an urban canyon environment. For 80% of the cases it achieves a 2D position tracking accuracy of 26 cm while orientation errors are kept below 0.5 degree.
Joint Delay-Doppler Estimation using OFDMA Payloads for Integrated Sensing and Communications
Authors: Marc Miranda, Sebastian Semper, Christian Schneider, Reiner Thomä, Giovanni Del Galdo
Date: 2025-03-06 00:00:00

The use of future communication systems for sensing offers the potential for a number of new applications. In this paper, we show that leveraging user data payloads in multi-node Orthogonal Frequency Division Multiple Access (OFDMA) networks for estimating target delay and Doppler-shift parameters can yield a significant advantage in SNR and addressable bandwidth. However, gaps in the frequency-time resources, reference signal boosting and amplitude modulation schemes introduce challenges for estimation at the sensing receiver. In this work, we propose a joint delay and Doppler-shift model-based estimator designed to address these challenges. Furthermore, we demonstrate that incorporating knowledge of the device model into the estimation procedure helps mitigate the effects of the non-ideal radar ambiguity function caused by amplitude-modulated user payloads and sparse reference signals. Simulation results demonstrate that the estimator achieves the theoretical lower bound on estimation variance.