pwc-archive/papers-with-abstracts huggingface dataset:
Spatial Distribution of Data Capacity for the Reduction of Number of Repeaters in Ultra Long-Haul Links
Authors: Smaranika Swain, Christian Koefoed Schou, Metodi Plamenov Yankov, Michael Galili, Leif Katsuo Oxenløwe
Date: 2024-01-30 00:00:00

We present a novel method to reduce the number of repeaters and amplifiers in trans-oceanic links by distributing a given data capacity in spatial channels. We analytically, numerically and experimentally demonstrate the principle and show that about 40 % of the repeaters can be omitted compared to a recently deployed cable. The method predicts that a single-fiber transmission link with 50 km amplifier spacing would be better off, repeater-wise, if the targeted single-fiber capacity is distributed in two fibers, each with an amplifier spacing of 150 km. In this scenario, one would thus only require 2/3 of the original number of amplifiers, and only 1/3 of the number of repeaters, housing the amplifiers. To test the principle of the proposed method, we experimentally and numerically investigate a 6900-km long link with amplifier spacing of 50 and 150 km using a recirculating fiber transmission loop, and find that the result supports the analytical model and thus the proposed method. We then use this concept to analytically investigate a realistic $12$-fiber pair cable, and find that the same capacity could be distributed in 19 fiber pairs requiring only about 56 % of the original number of repeaters.
A fully differentiable model for unsupervised singing voice separation
Authors: Gael Richard, Pierre Chouteau, Bernardo Torres
Date: 2024-01-30 00:00:00
Tasks: Music Source Separation

A novel model was recently proposed by Schulze-Forster et al. in [1] for unsupervised music source separation. This model allows to tackle some of the major shortcomings of existing source separation frameworks. Specifically, it eliminates the need for isolated sources during training, performs efficiently with limited data, and can handle homogeneous sources (such as singing voice). But, this model relies on an external multipitch estimator and incorporates an Ad hoc voice assignment procedure. In this paper, we propose to extend this framework and to build a fully differentiable model by integrating a multipitch estimator and a novel differentiable assignment module within the core model. We show the merits of our approach through a set of experiments, and we highlight in particular its potential for processing diverse and unseen data.
Subspace-Based Detection in OFDM ISAC Systems under Different Constellations
Authors: Yangming Lai, Musa Furkan Keskin, Henk Wymeersch, Luca Venturino, Wei Yi, Lingjiang Kong
Date: 2024-01-30 00:00:00
Tasks: ISAC

This paper investigates subspace-based target detection in OFDM integrated sensing and communications (ISAC) systems, considering the impact of various constellations. To meet diverse communication demands, different constellation schemes with varying modulation orders (e.g., PSK, QAM) can be employed, which in turn leads to variations in peak sidelobe levels (PSLs) within the radar functionality. These PSL fluctuations pose a significant challenge in the context of multi-target detection, particularly in scenarios where strong sidelobe masking effects manifest. To tackle this challenge, we have devised a subspace-based approach for a step-by-step target detection process, systematically eliminating interference stemming from detected targets. Simulation results corroborate the effectiveness of the proposed method in achieving consistently high target detection performance under a wide range of constellation options in OFDM ISAC systems.
Resource allocation exploiting reflective surfaces to minimize the outage probability in VLC
Authors: Borja Genoves Guzman, Maximo Morales Cespedes, Victor P. Gil Jimenez, Ana Garcia Armada, Maite Brandt-Pearce
Date: 2024-01-29 00:00:00

Visible light communication (VLC) is a technology that complements radio frequency (RF) to fulfill the ever-increasing demand for wireless data traffic. The ubiquity of light-emitting diodes (LEDs), exploited as transmitters, increases the VLC market penetration and positions it as one of the most promising technologies to alleviate the spectrum scarcity of RF. However, VLC deployment is hindered by blockage causing connectivity outages in the presence of obstacles. Recently, optical reconfigurable intelligent surfaces (ORISs) have been considered to mitigate this problem. While prior works exploit ORISs for data or secrecy rate maximization, this paper studies the optimal placement of mirrors and ORISs, and the LED power allocation, for jointly minimizing the outage probability while keeping the lighting standards. We describe an optimal outage minimization framework and present solvable heuristics. We provide extensive numerical results and show that the use of ORISs may reduce the outage probability by up to 67% with respect to a no-mirror scenario and provide a gain of hundreds of kbit/J in optical energy efficiency with respect to the presented benchmark.
3-D Position Optimization of Solar-Powered Hovering UAV Relay in Optical Wireless Backhaul
Authors: Heyou Liu, Muhammad Salman Bashir, Mohamed-Slim Alouini
Date: 2024-01-29 00:00:00
Tasks: Position

A major hurdle in widespread deployment of UAVs (unmanned aerial vehicle) in existing communications infrastructure is the limited UAV onboard energy. Therefore, this study considers solar energy harvesting UAVs for wireless communications. In this context, we consider three dimensional position optimization of a solar-powered UAV relay that connects a distant sensor field to an optical ground station (OGS) for data processing. The integrated sensor-UAV-OGS network utilizes radio frequency band for sensor-to-UAV links and the optical band for the UAV-to-OGS feeder link. Since atmospheric conditions affect both the harvested solar energy as well as the optical wireless signal, this study tackles UAV position optimization problems under various channel conditions such as clouds, atmospheric turbulence and dirt. From this study, we discover that the optimum position of the UAV -- that maximizes the end-to-end channel capacity -- is heavily dependent on the atmospheric channel conditions.
Data and Physics driven Deep Learning Models for Fast MRI Reconstruction: Fundamentals and Methodologies
Authors: Jiahao Huang, Yinzhe Wu, Fanwen Wang, Yingying Fang, Yang Nan, Cagan Alkan, Daniel Abraham, Congyu Liao, Lei Xu, Zhifan Gao, Weiwen Wu, Lei Zhu, Zhaolin Chen, Peter Lally, Neal Bangerter, Kawin Setsompop, Yike Guo, Daniel Rueckert, Ge Wang, Guang Yang
Date: 2024-01-29 00:00:00
Tasks: Diagnostic, Federated Learning, MRI Reconstruction

Magnetic Resonance Imaging (MRI) is a pivotal clinical diagnostic tool, yet its extended scanning times often compromise patient comfort and image quality, especially in volumetric, temporal and quantitative scans. This review elucidates recent advances in MRI acceleration via data and physics-driven models, leveraging techniques from algorithm unrolling models, enhancement-based methods, and plug-and-play models to the emerging full spectrum of generative model-based methods. We also explore the synergistic integration of data models with physics-based insights, encompassing the advancements in multi-coil hardware accelerations like parallel imaging and simultaneous multi-slice imaging, and the optimization of sampling patterns. We then focus on domain-specific challenges and opportunities, including image redundancy exploitation, image integrity, evaluation metrics, data heterogeneity, and model generalization. This work also discusses potential solutions and future research directions, with an emphasis on the role of data harmonization and federated learning for further improving the general applicability and performance of these methods in MRI reconstruction.
Fine Time Measurement for the Internet of Things: A Practical Approach Using ESP32
Authors: V. Barral Vales, O. C. Fernández, T. Domínguez-Bolaño, C. J. Escudero, José A. García-Naya
Date: 2024-01-29 00:00:00

In the world of Internet of Things (IoT), obtaining the physical location of devices has always been a task of great interest for developing increasingly complex location-based services (LBS). That is why in recent years wireless communication standards have been incorporating new additions focused on providing localization mechanisms to technologies widely used in the IoT world, such as Wi-Fi or Bluetooth. In particular, the IEEE 802.11-2016 Wi-Fi standard introduced ranging estimation between two devices through the so-called fine time measurement (FTM) protocol, defined by the IEEE 802.11mc. FTM is not yet widespread in the IoT field, but commercial modules capable of offering this functionality at a reasonable price are starting to appear. In early 2021, the most widespread system on a chip (SOC) family among IoT devices, the ESP32-XX series, added support for this Wi-Fi standard, enabling, for the first time, the use of a standard designed for location-based systems. This article analyzes the performance of this FTM implementation by carrying out and studying several measurement campaigns in different indoor and outdoor scenarios. Additionally, this work proposes an alternative real-time implementation for distance estimation inside the ESP32 using an approach based on machine learning. Such an implementation is successfully validated in a scenario totally different than those considered for the training and test sets. Finally, both the measurement sets and the developed software are available to the scientific community.
Learned Spike Encoding of the Channel Response for Low-Power Environment Sensing
Authors: Eleonora Cicciarella, Riccardo Mazzieri, Jacopo Pegoraro, Michele Rossi
Date: 2024-01-29 00:00:00
Tasks: Edge-computing

Radio Frequency (RF) sensing holds the potential for enabling pervasive monitoring applications. However, modern sensing algorithms imply complex operations, which clash with the energy-constrained nature of edge sensing devices. This calls for the development of new processing and learning techniques that can strike a suitable balance between performance and energy efficiency. Spiking Neural Networks (SNNs) have recently emerged as an energy-efficient alternative to conventional neural networks for edge computing applications. They process information in the form of sparse binary spike trains, thus potentially reducing energy consumption by several orders of magnitude. Their fruitful use for RF signal processing critically depends on the representation of RF signals in the form of spike signals. We underline that existing spike encoding algorithms to do so generally produce inaccurate signal representations and dense (i.e., inefficient) spike trains. In this work, we propose a lightweight neural architecture that learns a tailored spike encoding representations of RF channel responses by jointly reconstructing the input and its spectral content. By leveraging a tunable regularization term, our approach enables fine-grained control over the performance-energy trade-off of the system. Our numerical results show that the proposed method outperforms existing encoding algorithms in terms of reconstruction error and sparsity of the obtained spike encodings.
Reconfigurable AI Modules Aided Channel Estimation and MIMO Detection
Authors: Xiangzhao Qin, Sha Hu, Jiankun Zhang, Jing Qian, Hao Wang
Date: 2024-01-29 00:00:00
Tasks: Super-Resolution

Deep learning (DL) based channel estimation (CE) and multiple input and multiple output detection (MIMODet), as two separate research topics, have provided convinced evidence to demonstrate the effectiveness and robustness of artificial intelligence (AI) for receiver design. However, problem remains on how to unify the CE and MIMODet by optimizing AI's structure to achieve near optimal detection performance such as widely considered QR with M-algorithm (QRM) that can perform close to the maximum likelihood (ML) detector. In this paper, we propose an AI receiver that connects CE and MIMODet as an unified architecture. As a merit, CE and MIMODet only adopt structural input features and conventional neural networks (NN) to perform end-to-end (E2E) training offline. Numerical results show that, by adopting a simple super-resolution based convolutional neural network (SRCNN) as channel estimator and domain knowledge enhanced graphical neural network (GNN) as detector, the proposed QRM enhanced GNN receiver (QRMNet) achieves comparable block error rate (BLER) performance to near-optimal baseline detectors.
A Hybrid MLP-Quantum approach in Graph Convolutional Neural Networks for Oceanic Nino Index (ONI) prediction
Authors: Francesco Mauro, Alessandro Sebastianelli, Bertrand Le Saux, Paolo Gamba, Silvia Liberata Ullo
Date: 2024-01-29 00:00:00

This paper explores an innovative fusion of Quantum Computing (QC) and Artificial Intelligence (AI) through the development of a Hybrid Quantum Graph Convolutional Neural Network (HQGCNN), combining a Graph Convolutional Neural Network (GCNN) with a Quantum Multilayer Perceptron (MLP). The study highlights the potentialities of GCNNs in handling global-scale dependencies and proposes the HQGCNN for predicting complex phenomena such as the Oceanic Nino Index (ONI). Preliminary results suggest the model potential to surpass state-of-the-art (SOTA). The code will be made available with the paper publication.
Numerical Stability of DFT Computation for Signals with Structured Support
Authors: Charantej Reddy Pochimireddy, Aditya Siripuram, Brad Osgood
Date: 2024-01-29 00:00:00

We consider the problem of building numerically stable algorithms for computing Discrete Fourier Transform (DFT) of $N$- length signals with known frequency support of size $k$. A typical algorithm, in this case, would involve solving (possibly poorly conditioned) system of equations, causing numerical instability. When $N$ is a power of 2, and the frequency support is a random subset of $\mathbb{Z}_N$, we provide an algorithm that has (a possibly optimal) $O(k \log k)$ complexity to compute the DFT while solving system of equations that are $O(1)$ in size.
DMCE: Diffusion Model Channel Enhancer for Multi-User Semantic Communication Systems
Authors: Youcheng Zeng, Xinxin He, Xu Chen, Haonan Tong, Zhaohui Yang, Yijun Guo, Jianjun Hao
Date: 2024-01-29 00:00:00
Tasks: Semantic Communication, Semantic Segmentation

To achieve continuous massive data transmission with significantly reduced data payload, the users can adopt semantic communication techniques to compress the redundant information by transmitting semantic features instead. However, current works on semantic communication mainly focus on high compression ratio, neglecting the wireless channel effects including dynamic distortion and multi-user interference, which significantly limit the fidelity of semantic communication. To address this, this paper proposes a diffusion model (DM)-based channel enhancer (DMCE) for improving the performance of multi-user semantic communication, with the DM learning the particular data distribution of channel effects on the transmitted semantic features. In the considered system model, multiple users (such as road cameras) transmit semantic features of multi-source data to a receiver by applying the joint source-channel coding (JSCC) techniques, and the receiver fuses the semantic features from multiple users to complete specific tasks. Then, we propose DMCE to enhance the channel state information (CSI) estimation for improving the restoration of the received semantic features. Finally, the fusion results at the receiver are significantly enhanced, demonstrating a robust performance even under low signal-to-noise ratio (SNR) regimes, enabling the generation of effective object segmentation images. Extensive simulation results with a traffic scenario dataset show that the proposed scheme can improve the mean Intersection over Union (mIoU) by more than 25\% at low SNR regimes, compared with the benchmark schemes.
A semidefinite programming approach for robust elliptic localization
Authors: Wenxin Xiong, Yuming Chen, Jiajun He, Zhang-Lei Shi, Keyuan Hu, Hing Cheung So, Chi-Sing Leung
Date: 2024-01-28 00:00:00

This short communication addresses the problem of elliptic localization with outlier measurements. Outliers are prevalent in various location-enabled applications, and can significantly compromise the positioning performance if not adequately handled. Instead of following the common trend of using $M$-estimation or adjusting the conventional least squares formulation by integrating extra error variables, we take a different path. Specifically, we explore the worst-case robust approximation criterion to bolster resistance of the elliptic location estimator against outliers. From a geometric standpoint, our method boils down to pinpointing the Chebyshev center of a feasible set, which is defined by the available bistatic ranges with bounded measurement errors. For a practical approach to the associated min-max problem, we convert it into the convex optimization framework of semidefinite programming (SDP). Numerical simulations confirm that our SDP-based technique can outperform a number of existing elliptic localization schemes in terms of positioning accuracy in Gaussian mixture noise.
Large Deviation Analysis of Score-based Hypothesis Testing
Authors: Enmao Diao, Taposh Banerjee, Vahid Tarokh
Date: 2024-01-27 00:00:00

Score-based statistical models play an important role in modern machine learning, statistics, and signal processing. For hypothesis testing, a score-based hypothesis test is proposed in \cite{wu2022score}. We analyze the performance of this score-based hypothesis testing procedure and derive upper bounds on the probabilities of its Type I and II errors. We prove that the exponents of our error bounds are asymptotically (in the number of samples) tight for the case of simple null and alternative hypotheses. We calculate these error exponents explicitly in specific cases and provide numerical studies for various other scenarios of interest.
Time-modulated array beamforming with periodic stair-step pulses
Authors: R. Maneiro-Catoira, J. Brégains, José A. García-Naya, L. Castedo
Date: 2024-01-27 00:00:00

Time-modulated arrays (TMAs) are able to improve the side-lobe level of the radiation pattern at the fundamental mode but cannot steer the beam at such a mode towards a given direction. Beam-steering is possible in a TMA, but only at the harmonic patterns and at the expense of a severe TMA efficiency reduction. In this work we propose a TMA approach that simultaneously performs both features over the same beam by using two sets of switches: (1) single-pole four-throw switches to generate periodic stair-step pulses suitable for efficiently synthesizing a uniform steerable beam over the first positive harmonic, and (2) single-pole single-throw switches to reconfigure the side-lobe level of the previous beam. Performance, small size, cost-effectiveness, and performance invariability with the carrier frequency are features that make this TMA approach a competitive solution for analog beamforming. Accordingly, the structure is an attractive proposal for the design of multibeam transceivers.
Time-modulated phased array controlled with nonideal bipolar squared periodic sequences
Authors: R. Maneiro-Catoira, J. Brégains, José A. García-Naya, L. Castedo
Date: 2024-01-27 00:00:00

Bipolar (+/-1) sequences with no zero state suit particularly well for safeguarding the switched feeding network efficiency when applied to time-modulated arrays (TMAs). During the zero state of a conventional time-modulating sequence, if a given array element is switched off, a certain amount of energy of the transmitted/received signal is wasted. We propose a novel single sideband time-modulated phased array (TMPA) architecture governed by realistic bipolar squared sequences in which the rise/fall time of the switches is considered. By using single-pole dual-throw switches and non-reconfigurable passive devices, the TMPA exploits, exclusively, the first positive harmonic pattern while exhibiting an excellent performance in terms of efficiency and control level of the undesired harmonics without using synthesis optimization algorithms (software simplicity).
Bit error probability and capacity bound of OFDM systems in deterministic doubly-selective channels
Authors: Tomás Domínguez-Bolaño, José Rodríguez-Piñeiro, José A. García-Naya, Luis Castedo
Date: 2024-01-27 00:00:00

Doubly-selective channels, such as those that occur when the transmitter and the receiver move relative to each other at high speeds, are a key scenario for fifth generation (5G) cellular systems, which are mostly based in the use of the orthogonal frequency-division multiplexing (OFDM) modulation. In this paper, we consider an OFDM system using quadrature amplitude modulation (QAM) symbols and we show that, when transmitting over deterministic doubly-selective channels, the inter-carrier interference (ICI) affecting a symbol can be well approximated by a complex-valued normal distribution. Based on this, we derive a lower bound for the link capacity using the Shannon-Hartley theorem. Finally, we provide an approximation of the bit error probability (BEP) using the well-known BEP expressions for Gray-coded QAM constellations over additive white Gaussian noise (AWGN) channels, and show numerical results that confirm that the proposed BEP expression approximates accurately the bit error ratio (BER) of the OFDM system for standardized channel models. The proposed closed-form analytical expressions for the capacity and the BEP do not only allow for discarding the need of computationally-costly Monte-Carlo system simulations, but also provide a theoretical framework to optimize the system parameters directly impacting on the achievable performance.
A Self-Healing Mesh Network without Global-Time Synchronization
Authors: Alphan Şahin, Hüseyin Arslan
Date: 2024-01-26 00:00:00

In this paper, we propose a slot-based protocol that does not rely on global-time synchronization to achieve a self-healing mesh network. With the proposed protocol, each node synchronizes with its neighbors locally by adjusting its time to transmit based on the reception instant of a decoded beacon signal. Also, it determines its slots without any coordinator to avoid collisions. Finally, to communicate the messages over the mesh network, it identifies the forwarding nodes on the shortest path without knowing the entire communication graph. We show that the proposed protocol can effectively resolve collisions over time while enabling nodes to synchronize with each other in a distributed manner. We numerically analyze the performance of the proposed protocol for different configurations under a realistic channel model considering asymmetrical links. We also implement the proposed method in practice with \ac{LoRa} devices. We demonstrate that the nodes adapt themselves to changes in the network and deliver a message from a sensing node to a reference node via multi-hop routing.