pwc-archive/papers-with-abstracts huggingface dataset:
Delay-Doppler Signal Processing with Zadoff-Chu Sequences
Authors: Sandesh Rao Mattu, Imran Ali Khan, Venkatesh Khammammetti, Beyza Dabak, Saif Khan Mohammed, Krishna Narayanan, Robert Calderbank
Date: 2024-12-05 00:00:00
Tasks: compressed sensing

Much of the engineering behind current wireless systems has focused on designing an efficient and high-throughput downlink to support human-centric communication such as video streaming and internet browsing. This paper looks ahead to design of the uplink, anticipating the emergence of machine-type communication (MTC) and the confluence of sensing, communication, and distributed learning. We demonstrate that grant-free multiple access is possible even in the presence of highly time-varying channels. Our approach provides a pathway to standards adoption, since it is built on enhancing the 2-step random access procedure which is already part of the 5GNR standard. This 2-step procedure uses Zadoff-Chu (ZC) sequences as preambles that point to radio resources which are then used to upload data. We also use ZC sequences as preambles / pilots, but we process signals in the Delay-Doppler (DD) domain rather than the time-domain. We demonstrate that it is possible to detect multiple preambles in the presence of mobility and delay spread using a receiver with no knowledge of the channel other than the worst case delay and Doppler spreads. Our approach depends on the mathematical properties of ZC sequences in the DD domain. We derive a closed form expression for ZC pilots in the DD domain, we characterize the possible self-ambiguity functions, and we determine the magnitude of the possible cross-ambiguity functions. These mathematical properties enable detection of multiple pilots through solution of a compressed sensing problem. The columns of the compressed sensing matrix are the translates of individual ZC pilots in delay and Doppler. We show that columns in the design matrix satisfy a coherence property that makes it possible to detect multiple preambles in a single Zak-OTFS subframe using One-Step Thresholding (OST), which is an algorithm with low complexity.
Deep-Unrolling Multidimensional Harmonic Retrieval Algorithms on Neuromorphic Hardware
Authors: Vlad C. Andrei, Alexandru P. Drăguţoiu, Gabriel Béna, Mahmoud Akl, Yin Li, Matthias Lohrmann, Ullrich J. Mönich, Holger Boche
Date: 2024-12-05 00:00:00
Tasks: multidimensional harmonic retrieval, Retrieval

This paper explores the potential of conversion-based neuromorphic algorithms for highly accurate and energy-efficient single-snapshot multidimensional harmonic retrieval (MHR). By casting the MHR problem as a sparse recovery problem, we devise the currently proposed, deep-unrolling-based Structured Learned Iterative Shrinkage and Thresholding (S-LISTA) algorithm to solve it efficiently using complex-valued convolutional neural networks with complex-valued activations, which are trained using a supervised regression objective. Afterward, a novel method for converting the complex-valued convolutional layers and activations into spiking neural networks (SNNs) is developed. At the heart of this method lies the recently proposed Few Spikes (FS) conversion, which is extended by modifying the neuron model's parameters and internal dynamics to account for the inherent coupling between real and imaginary parts in complex-valued computations. Finally, the converted SNNs are mapped onto the SpiNNaker2 neuromorphic board, and a comparison in terms of estimation accuracy and power efficiency between the original CNNs deployed on an NVIDIA Jetson Xavier and the SNNs is being conducted. The measurement results show that the converted SNNs achieve almost five-fold power efficiency at moderate performance loss compared to the original CNNs.
Hierarchical Learning for IRS-Assisted MEC Systems with Rate-Splitting Multiple Access
Authors: Yinyu Wu, Xuhui Zhang, Jinke Ren, Yanyan Shen, Bo Yang, Shuqiang Wang, Xinping Guan, Dusit Niyato
Date: 2024-12-05 00:00:00
Tasks: Deep Reinforcement Learning, Edge-computing

Intelligent reflecting surface (IRS)-assisted mobile edge computing (MEC) systems have shown notable improvements in efficiency, such as reduced latency, higher data rates, and better energy efficiency. However, the resource competition among users will lead to uneven allocation, increased latency, and lower throughput. Fortunately, the rate-splitting multiple access (RSMA) technique has emerged as a promising solution for managing interference and optimizing resource allocation in MEC systems. This paper studies an IRS-assisted MEC system with RSMA, aiming to jointly optimize the passive beamforming of the IRS, the active beamforming of the base station, the task offloading allocation, the transmit power of users, the ratios of public and private information allocation, and the decoding order of the RSMA to minimize the average delay from a novel uplink transmission perspective. Since the formulated problem is non-convex and the optimization variables are highly coupled, we propose a hierarchical deep reinforcement learning-based algorithm to optimize both continuous and discrete variables of the problem. Additionally, to better extract channel features, we design a novel network architecture within the policy and evaluation networks of the proposed algorithm, combining convolutional neural networks and densely connected convolutional network for feature extraction. Simulation results indicate that the proposed algorithm not only exhibits excellent convergence performance but also outperforms various benchmarks.
Environment Reconstruction with Multi-targets Reflectors-merged Sensing Method Based on THz Single-sided Channel Characteristics
Authors: Zhaowei Chang, Pan Tang, Jianhua Zhang, Hao Jiang, Guangyi Liu
Date: 2024-12-05 00:00:00
Tasks: Integrated sensing and communication, ISAC

Terahertz (THz) integrated sensing and communication (ISAC) holds the potential to achieve high data rates and high-resolution sensing. Reconstructing the propagation environment is a vital step for THz ISAC, as it enhances the predictability of the communication channel to reduce communication overhead. In this letter, we propose an environment reconstruction methodology (ERM) merging reflectors of multi-targets based on THz single-sided channel small-scale characteristics. In this method, the inclination and position of tiny reflection faces of one single multi-path (MPC) are initially detected by double-triangle equations based on Snells law and geometry properties. Then, those reflection faces of multi-target MPCs, which are filtrated as available and one-order reflection MPCs, are globally merged to accurately reconstruct the entire propagation environment. The ERM is capable of operating with only small-scale parameters of receiving MPC. Subsequently, we validate our ERM through two experiments: bi-static ray-tracing simulations in an L-shaped room and channel measurements in an urban macrocellular (UMa) scenario in THz bands. The validation results demonstrate a small deviation of 0.03 m between the sensing outcomes and the predefined reflectors in the ray-tracing simulation and a small sensing root-mean-square error of 1.28 m and 0.45 m in line-of-sight and non-line-of-sight cases respectively based on channel measurements. Overall, this work is valuable for designing THz communication systems and facilitating the application of THz ISAC communication techniques.
Performance Analysis of XL-MIMO with Rotary and Movable Antennas for High-speed Railway
Authors: Wenhui Yi, Jiayi Zhang, Zhe Wang, Huahua Xiao, Bo Ai
Date: 2024-12-05 00:00:00

The rotary and movable antennas (ROMA) technology is efficient in enhancing wireless network capacity by adjusting both the antenna spacing and three-dimensional (3D) rotation of antenna surfaces, based on the spatial distribution of users and channel statistics. Applying ROMA to high-speed rail (HSR) wireless communications can significantly improve system performance in terms of array gain and spatial multiplexing. However, the rapidly changing channel conditions in HSR scenarios present challenges for ROMA configuration. In this correspondence, we propose a analytical framework for configuring ROMA-based extremely large-scale multiple-input-multiple-output (XL-MIMO) system in HSR scenarios based on spatial correlation. First, we develop a localization model based on a mobility-aware near-field beam training algorithm to determine the real-time position of the train relay antennas. Next, we derive the expression for channel orthogonality and antenna spacing based on the spatial correlation matrix, and obtain the optimal antenna spacing when the transceiver panels are aligned in parallel. Moreover, we propose an optimization algorithm for the rotation angle of the transceiver panels, leveraging the differential evolution method, to determine the optimal angle. Finally, numerical results are provided to validate the computational results and optimization algorithm.
Fluid Antenna Systems Enabling 6G:Principles, Applications, and Research Directions
Authors: Tuo Wu, Kangda Zhi, Junteng Yao, Xiazhi Lai, Jianchao Zheng, Hong Niu, Maged Elkashlan, Kai-Kit Wong, Chan-Byoung Chae, Zhiguo Ding, George K. Karagiannidis, Merouane Debbah, Chau Yuen
Date: 2024-12-05 00:00:00
Tasks: Position

Fluid antenna system (FAS) as a new version of reconfigurable antenna technologies promoting shape and position flexibility, has emerged as an exciting and possibly transformative technology for wireless communications systems. FAS represents any software-controlled fluidic, conductive or dielectric structure that can dynamically alter antenna's shape and position to change the gain, the radiation pattern, the operating frequency, and other critical radiation characteristics. With its capability, it is highly anticipated that FAS can contribute greatly to the upcoming sixth generation (6G) wireless networks. This article substantiates this thought by addressing four major questions: 1) Is FAS crucial to 6G? 2) How to characterize FAS? 3) What are the applications of FAS? 4) What are the relevant challenges and future research directions? In particular, five promising research directions that underscore the potential of FAS are discussed. We conclude this article by showcasing the impressive performance of FAS.
Learning Networks from Wide-Sense Stationary Stochastic Processes
Authors: Anirudh Rayas, Jiajun Cheng, Rajasekhar Anguluri, Deepjyoti Deka, Gautam Dasarathy
Date: 2024-12-04 00:00:00

Complex networked systems driven by latent inputs are common in fields like neuroscience, finance, and engineering. A key inference problem here is to learn edge connectivity from node outputs (potentials). We focus on systems governed by steady-state linear conservation laws: $X_t = {L^{\ast}}Y_{t}$, where $X_t, Y_t \in \mathbb{R}^p$ denote inputs and potentials, respectively, and the sparsity pattern of the $p \times p$ Laplacian $L^{\ast}$ encodes the edge structure. Assuming $X_t$ to be a wide-sense stationary stochastic process with a known spectral density matrix, we learn the support of $L^{\ast}$ from temporally correlated samples of $Y_t$ via an $\ell_1$-regularized Whittle's maximum likelihood estimator (MLE). The regularization is particularly useful for learning large-scale networks in the high-dimensional setting where the network size $p$ significantly exceeds the number of samples $n$. We show that the MLE problem is strictly convex, admitting a unique solution. Under a novel mutual incoherence condition and certain sufficient conditions on $(n, p, d)$, we show that the ML estimate recovers the sparsity pattern of $L^\ast$ with high probability, where $d$ is the maximum degree of the graph underlying $L^{\ast}$. We provide recovery guarantees for $L^\ast$ in element-wise maximum, Frobenius, and operator norms. Finally, we complement our theoretical results with several simulation studies on synthetic and benchmark datasets, including engineered systems (power and water networks), and real-world datasets from neural systems (such as the human brain).
Active Beam Learning for Full-Duplex Wireless Systems
Authors: Jeong Min Kong, Ian P. Roberts
Date: 2024-12-04 00:00:00

In this paper, we present a novel active beam learning method for in-band full-duplex wireless systems, that aims to design transmit and receive beams which suppress self-interference and maximize the sum spectral efficiency. Rather than rely on explicit estimation of the downlink, uplink, and/or self-interference channels like in most existing work, our method instead actively probes all three channels through measurements of SNR and INR over a fixed number of time slots. Then, once this probing concludes, all collected probing measurements are used to design transmit and receive beams which serve downlink and uplink in a full-duplex fashion. We realize this active beam learning scheme through a network of LSTMs and DNNs, which learns to design each probing beam pair and subsequently extract and record valuable information from each probing measurement such that near-optimal serving beams can be designed following the probing stage. Simulation indicates that our method reliably suppresses self-interference while delivering near-maximal SNR on the downlink and uplink with merely 3-10 probing time slots, while exhibiting robustness to measurement noise and the structure of the self-interference channel.
Electrocardiogram-based diagnosis of liver diseases: an externally validated and explainable machine learning approach
Authors: Juan Miguel Lopez Alcaraz, Wilhelm Haverkamp, Nils Strodthoff
Date: 2024-12-04 00:00:00
Tasks: Binary Classification, Diagnostic, Electrocardiography (ECG)

Background: Liver diseases present a significant global health challenge and often require costly, invasive diagnostics. Electrocardiography (ECG), a widely available and non-invasive tool, can enable the detection of liver disease by capturing cardiovascular-hepatic interactions. Methods: We trained tree-based machine learning models on ECG features to detect liver diseases using two large datasets: MIMIC-IV-ECG (467,729 patients, 2008-2019) and ECG-View II (775,535 patients, 1994-2013). The task was framed as binary classification, with performance evaluated via the area under the receiver operating characteristic curve (AUROC). To improve interpretability, we applied explainability methods to identify key predictive features. Findings: The models showed strong predictive performance with good generalizability. For example, AUROCs for alcoholic liver disease (K70) were 0.8025 (95% confidence interval (CI), 0.8020-0.8035) internally and 0.7644 (95% CI, 0.7641-0.7649) externally; for hepatic failure (K72), scores were 0.7404 (95% CI, 0.7389-0.7415) and 0.7498 (95% CI, 0.7494-0.7509), respectively. The explainability analysis consistently identified age and prolonged QTc intervals (corrected QT, reflecting ventricular repolarization) as key predictors. Features linked to autonomic regulation and electrical conduction abnormalities were also prominent, supporting known cardiovascular-liver connections and suggesting QTc as a potential biomarker. Interpretation: ECG-based machine learning offers a promising, interpretable approach for liver disease detection, particularly in resource-limited settings. By revealing clinically relevant biomarkers, this method supports non-invasive diagnostics, early detection, and risk stratification prior to targeted clinical assessments.
LDPC-Coded Molecular Communications with Increased Diversity
Authors: Alpar Türkoğlu, Berk Karabacakoğlu, Ali Emre Pusane
Date: 2024-12-04 00:00:00
Tasks: Diversity

This paper suggests achieving diversity gains while utilizing low-denisty parity check (LDPC) codes in molecular communications. Intersymbol interference (ISI) causes a significant disadvantage in error performance for molecular communications. Even though decoding LDPC codes with soft decoding yields a considerable enhancement in the bit error rate (BER) curves, this can be further improved by utilizing diversity gain. In order to achieve this, two different messenger molecule types are sent to transmit the message codeword and its interleaved version. The molecular communication channel is then modeled, and the error performance of the proposed method is estimated by Monte-Carlo simulations. This approach provides considerable improvement in the error performance in the scenario where few messenger molecules are transmitted per bit.
Galvanic Body-Coupled Powering for Wireless Implanted Neurostimulators
Authors: Asif Iftekhar Omi, Emma Farina, Anyu Jiang, Adam Khalifa, Shriya Srinivasan, Baibhab Chatterjee
Date: 2024-12-04 00:00:00

Body-coupled powering (BCP) is an innovative wireless power transfer (WPT) technique, recently explored for its potential to deliver power to cutting-edge biomedical implants such as nerve and muscle stimulators. This paper demonstrates the efficient technique of designing WPT systems embedding BCP via galvanic coupling (G-BCP). The G-BCP configuration utilizes two metal circular rings surrounding the body area of interest as the transmitter (TX) electrodes required for galvanic (differential) excitation and a wireless implant as the receiver (RX) equipped with two electrodes for differential power reception accordingly. By focusing on the unique advantages of this approach - such as enhanced targeting accuracy, improved power transfer efficiency (PTE), and favorable tissue penetration characteristics, G-BCP emerges as a superior alternative to traditional WPT methods. A comprehensive analysis is conducted to obtain the optimized device parameters while simultaneously allowing flexible placement of implants at different depths and alignments. To substantiate the proposed design concept, a prototype was simulated in Ansys HFSS, employing a multi-layered tissue medium of 10mm radius and targeting the sciatic nerve of a rat. Impressively, this prototype achieves > 20% PTE at 1.25 GHz, with the implant (radius of RX electrodes = 1 mm) located 2 mm deep inside the tissue model having complex load impedance of Rload = 1000 Ohm and Cload = 5pF. Therefore, the G-BCP-based wirelessly powered microdevices are envisaged to be a key enabler in neural recording and stimulation, specifically for the peripheral nervous system, enhancing therapeutic outcomes and patient experiences.
User-Movement-Robust Virtual Reality Through Dual-Beam Reception in mmWave Networks
Authors: Rizqi Hersyandika, Qing Wang, Yang Miao, Sofie Pollin
Date: 2024-12-04 00:00:00
Tasks: Diversity

Utilizing the mmWave band can potentially achieve the high data rate needed for realistic and seamless interaction within a virtual reality (VR) application. To this end, beamforming in both the access point (AP) and head-mounted display (HMD) sides is necessary. The main challenge in this use case is the specific and highly dynamic user movement, which causes beam misalignment, degrading the received signal level and potentially leading to outages. This study examines mmWave-based coordinated multi-point networks for VR applications, where two or multiple APs cooperatively transmit the signals to an HMD for connectivity diversity. Instead of using omnireception, we propose dual-beam reception based on the analog beamforming at the HMD, enhancing the receive beamforming gain towards serving APs while achieving diversity. Evaluation using actual HMD movement data demonstrates the effectiveness of our approach, showcasing a reduction in outage rates of up to 13% compared to quasi-omnidirectional reception with two serving APs, and a 17% decrease compared to steerable single-beam reception with a serving AP. Widening the separation angle between two APs can further reduce outage rates due to head rotation as rotations can still be tracked using the steerable multi-beam, albeit at the expense of received signal levels reduction during the non-outage period.
Channel Estimation for Hybrid MIMO Systems With Array Model Errors and Beam Squint Effects
Authors: Kabuto Arai, Koji Ishibashi
Date: 2024-12-04 00:00:00

This paper proposes a channel estimation method for hybrid wideband multiple-input-multiple-output (MIMO) systems in high-frequency bands, including millimeter-wave (mmWave) and sub-terahertz (sub-THz), in the presence of beam squint effects and array errors arising from hardware impairments and environmental time fluctuations such as thermal effects and dynamic motion of the array. Although conventional channel estimation methods calibrate array errors through offline operation with large training pilots, the calibration errors remain due to time-varying array errors. Therefore, the proposed channel estimation method calibrates array errors online with small pilot overhead. In the proposed method, array response matrices are explicitly decomposed into a small number of physical parameters including path gains, angles and array errors, which are iteratively estimated by alternating optimization based on a maximum likelihood (ML) criterion. To enhance the convergence performance, we introduce a switching mechanism from an ongrid algorithm to an off-gird algorithm depending on the estimation accuracy of the array error during algorithmic iterations. Furthermore, we introduce an approximate mutual coupling model to reduce the number of parameters. The reduction of parameters not only lowers computational complexity but also mitigates overfitting to noisy observations. Numerical simulations demonstrate that the proposed method effectively works online even with small pilot overhead in the presence of array errors.
How Should One Fit Channel Measurements to Fading Distributions for Performance Analysis?
Authors: Santiago Fernández, José David Vega-Sánchez, Juan E. Galeote-Cazorla, F. Javier López-Martínez
Date: 2024-12-04 00:00:00

Accurate channel modeling plays a pivotal role in optimizing communication systems, and fitting field measurements to stochastic models is crucial for capturing the key propagation features and to map these to achievable system performances. In this work, we shed light onto what's the most appropriate alternative for channel fitting, when the ultimate goal is performance analysis. Results show that likelihood-based and average-error metrics should be used with caution, since they can largely fail to predict outage probability measures. We show that supremum-error fitting metrics with tail awareness are more robust to estimate both ergodic and outage performance measures, even when they yield a larger average-error fitting.
Achieving Beamfocusing via Two Separated Uniform Linear Arrays
Authors: Alva Kosasih, Özlem Tugfe Demir, Emil Björnson
Date: 2024-12-04 00:00:00

This paper investigates coordinated beamforming using a modular linear array (MLA), composed of a pair of physically separated uniform linear arrays (ULAs), treated as sub-arrays. We focus on how such setups can give rise to near-field effects in 6G networks without requiring many antennas. Unlike conventional far-field beamforming, near-field beamforming enables simultaneous data service to multiple users at different distances in the same angular direction, offering significant multiplexing gains. We present a detailed analysis, including analytical expressions of the beamwidth and beamdepth for the MLA. Our findings reveal that using the MLA approach, we can remove approximately 36% of the antennas in the ULA while achieving the same level of beamfocusing.