pwc-archive/papers-with-abstracts huggingface dataset:
Neuromorphic Vision Data Coding: Classifying and Reviewing
Authors: Catarina Brites, João Ascenso
Date: 2024-05-11 00:00:00

In recent years, visual sensors have been quickly improving towards mimicking the visual information acquisition process of human brain, by responding to illumination changes as they occur in time rather than at fixed time intervals. In this context, the so-called neuromorphic vision sensors depart from the conventional frame-based image sensors by adopting a paradigm shift in the way visual information is acquired. This new way of visual information acquisition enables faster and asynchronous per-pixel responses/recordings driven by the scene dynamics with a very high dynamic range and low power consumption. However, the huge amount of data outputted by the emerging neuromorphic vision sensors critically demands highly efficient coding solutions in order applications may take full advantage of these new, attractive sensors' capabilities. For this reason, considerable research efforts have been invested in recent years towards developing increasingly efficient neuromorphic vision data coding (NVDC) solutions. In this context, the main objective of this paper is to provide a comprehensive overview of NVDC solutions in the literature, guided by a novel classification taxonomy, which allows better organizing this emerging field. In this way, more solid conclusions can be drawn about the current NVDC status quo, thus allowing to better drive future research and standardization developments in this emerging technical area.
Merging Point Data for InSAR Deformation Processing
Authors: Matthew T. Calef, Kelly M. Olsen, Piyush S. Agram
Date: 2024-05-10 00:00:00

Given a collection of points $S \subset \mathbb{R}^N$, which is partitioned into $M$ overlapping subsets $\{S_i\}_{i=1}^M$, and approximate data $\{D_i\}_{i=1}^M$ associated with the subsets, one may seek a consistent merged dataset $D$ that is derived from $\{S_i\}_{i=1}^M$ and $\{D_i\}_{i=1}^M$. This note presents a method for constructing $D$ under the assumption that $D$ represents discrete samples of a suitably smooth function $f:\mathbb{R}^N \rightarrow \mathbb{R}$ evaluated at the points in $S$. The method has two steps. The first step uses a least-squares solve to approximate the constant offsets for each $D_i$. The second step uses a sequence of discrete Dirichlet problems to resolve any remaining differences. We include a two dimensional example of this method applied to deformation measurements derived from Interferometric Synthetic Aperture Radar (InSAR).
Non-contact Lung Disease Classification via OFDM-based Passive 6G ISAC Sensing
Authors: Hasan Mujtaba Buttar, Muhammad Mahboob Ur Rahman, Muhammad Wasim Nawaz, Adnan Noor Mian, Adnan Zahid, Qammer H. Abbasi
Date: 2024-05-15 00:00:00
Tasks: Integrated sensing and communication, ISAC, Lung Disease Classification

This paper is the first to present a novel, non-contact method that utilizes orthogonal frequency division multiplexing (OFDM) signals (of frequency 5.23 GHz, emitted by a software defined radio) to radio-expose the pulmonary patients in order to differentiate between five prevalent respiratory diseases, i.e., Asthma, Chronic obstructive pulmonary disease (COPD), Interstitial lung disease (ILD), Pneumonia (PN), and Tuberculosis (TB). The fact that each pulmonary disease leads to a distinct breathing pattern, and thus modulates the OFDM signal in a different way, motivates us to acquire OFDM-Breathe dataset, first of its kind. It consists of 13,920 seconds of raw RF data (at 64 distinct OFDM frequencies) that we have acquired from a total of 116 subjects in a hospital setting (25 healthy control subjects, and 91 pulmonary patients). Among the 91 patients, 25 have Asthma, 25 have COPD, 25 have TB, 5 have ILD, and 11 have PN. We implement a number of machine and deep learning models in order to do lung disease classification using OFDM-Breathe dataset. The vanilla convolutional neural network outperforms all the models with an accuracy of 97%, and stands out in terms of precision, recall, and F1-score. The ablation study reveals that it is sufficient to radio-observe the human chest on seven different microwave frequencies only, in order to make a reliable diagnosis (with 96% accuracy) of the underlying lung disease. This corresponds to a sensing overhead that is merely 10.93% of the allocated bandwidth. This points to the feasibility of 6G integrated sensing and communication (ISAC) systems of future where 89.07% of bandwidth still remains available for information exchange amidst on-demand health sensing. Through 6G ISAC, this work provides a tool for mass screening for respiratory diseases (e.g., COVID-19) at public places.
Outlier-resilient model fitting via percentile losses: Methods for general and convex residuals
Authors: João Domingos, João Xavier
Date: 2024-05-15 00:00:00

We consider the problem of robustly fitting a model to data that includes outliers by formulating a percentile optimization problem. This problem is non-smooth and non-convex, hence hard to solve. We derive properties that the minimizers of such problems must satisfy. These properties lead to methods that solve the percentile formulation both for general residuals and for convex residuals. The methods fit the model to subsets of the data, and then extract the solution of the percentile formulation from these partial fits. As illustrative simulations show, such methods endure higher outlier percentages, when compared with standard robust estimates. Additionally, the derived properties provide a broader and alternative theoretical validation for existing robust methods, whose validity was previously limited to specific forms of the residuals.
Continuous Multi-Link Operation: A Contention-Free Mechanism for the Unlicensed Spectrum
Authors: Gianluca Fontanesi, Francesc Wilhelmi, Lorenzo Galati-Giordano
Date: 2024-05-15 00:00:00
Tasks: Fairness

This paper proposes a novel mechanism to enforce contention-free channel access in the unlicensed spectrum, as opposed to the traditional contention-based approach. To achieve this objective, we build on the Wi-Fi~7 multi-link operation (MLO) and define the means whereby independent channel access attempts are performed in all the addressable links to ensure one available channel/link is ready for transmission at all times, such that a sequence of continuous acquired channels can be maintained. We call this method continuous multi-link operation (ConMLO). In this work, we aim to verify the applicability of ConMLO, its ability to retain spectrum resources for a given duration of time, and its fairness with respect existing approaches, namely legacy single-link operation (SLO) and MLO. To this end, we use realistic data traffic measurements acquired in a crowded football stadium as an exemplary case of challenging spectrum occupation. Our results show that the proposed ConMLO can effectively guarantee continuous channel acquisition under different occupancy scenarios without compromising fairness of channel access compared to existing legacy modes.
PRANK: a singular value based noise filtering of multiple response datasets for experimental dynamics
Authors: Francesco Trainotti, Steven W. B. Klaassen, Tomaz Bregar, Daniel J. Rixen
Date: 2024-05-13 00:00:00

High quality measurements are paramount to a successful application of experimental techniques in structural dynamics. The presence of noise and disturbances can significantly distort the information stored in the data and, if not adequately treated, may result in erroneous findings and misleading predictions. A common technique to filter out noise relies on decomposing the dataset into singular components sorted by their degree of significance. Discarding low-value contributions helps to clean the data and remove spuriousness. This paper presents PRANK, a novel singular value-based reconstruction approach for multiple response vibration datasets. PRANK integrates the effect of Principal Response Functions and Hankel filtering actions, resulting in an improved data reconstruction for both system poles and zeros. The proposed formulation is tested on both analytical and numerical examples, showcasing its robustness, efficiency and versatility. PRANK operates with both time- and frequency-based data. Applied to noisy full-field camera measurements, the filter delivered excellent performance, indicating its potential for various identification tasks and applications in vibration analysis.
A Robust UAV-Based Approach for Power-Modulated Jammer Localization Using DoA
Authors: Zexin Fang, Bin Han, Hans D. Schotten
Date: 2024-05-15 00:00:00

Unmanned aerial vehicles (UAVs) are well-suited to localize jammers, particularly when jammers are at non-terrestrial locations, where conventional detection methods face challenges. In this work we propose a novel localization method, sample pruning gradient descend (SPGD), which offers robust performance against multiple power-modulated jammers with low computational complexity.
Anchor Layout Optimization for Ultrasonic Indoor Positioning Using Swarm Intelligence
Authors: Daan Delabie, Thomas Wilding, Liesbet Van der Perre, Lieven De Strycker
Date: 2024-05-15 00:00:00

Indoor positioning applications are craving for ever higher precision and accuracy across the entire coverage zone. Optimal anchor placement and the deployment of multiple distributed anchor nodes could have a major impact in this regard. This paper examines the influences of these two difficult to approach hypotheses by means of a straightforward ultrasonic 3D indoor positioning system deployed in a real-life scenario via a geometric based simulation framework. To obtain an optimal anchor placement, a particle swarm optimization (PSO) algorithm is introduced and consequently performed for setups ranging from 4 to 10 anchors. In this way, besides the optimal anchor placement layout, the influence of deploying several distributed anchor nodes is investigated. In order to theoretically compare the optimization progress, a system model and Cram\'er-Rao lower bound (CRLB) are established and the results are quantified based on the simulation data. With limited anchors, the placement is crucial to obtain a high precision high reliability (HPHR) indoor positioning system (IPS), while the addition of anchors, to a lesser extent, gives a supplementary improvement.
Integrated Sensing and Communication Enabled Cooperative Passive Sensing Using Mobile Communication System
Authors: Zhiqing Wei, Haotian Liu, Hujun Li, Wangjun Jiang, Zhiyong Feng, Huici Wu, Ping Zhang
Date: 2024-05-15 00:00:00
Tasks: Integrated sensing and communication, ISAC

Integrated sensing and communication (ISAC) is a potential technology of the sixth-generation (6G) mobile communication system, which enables communication base station (BS) with sensing capability. However, the performance of single-BS sensing is limited, which can be overcome by multi-BS cooperative sensing. There are three types of multi-BS cooperative sensing, including cooperative active sensing, cooperative passive sensing, and cooperative active and passive sensing, where the multi-BS cooperative passive sensing has the advantages of low hardware modification cost and large sensing coverage. However, multi-BS cooperative passive sensing faces the challenges of synchronization offsets mitigation and sensing information fusion. To address these challenges, a non-line of sight (NLoS) and line of sight (LoS) signal cross-correlation (NLCC) method is proposed to mitigate carrier frequency offset (CFO) and time offset (TO). Besides, a symbol-level multi-BS sensing information fusion method is proposed. The discrete samplings of echo signals from multiple BSs are matched independently and coherent accumulated to improve sensing accuracy. Moreover, a lowcomplexity joint angle-of-arrival (AoA) and angle-of-departure (AoD) estimation method is proposed to reduce the computational complexity. Simulation results show that symbol-level multi-BS cooperative passive sensing scheme has an order of magnitude higher sensing accuracy than single-BS passive sensing. This work provides a reference for the research on multi-BS cooperative passive sensing.
Interpretable attributed scattering center extracted via deep unfolding
Authors: Haodong Yang, Zhe Zhang, Zhongling Huang
Date: 2024-05-15 00:00:00

Most existing sparse representation-based approaches for attributed scattering center (ASC) extraction adopt traditional iterative optimization algorithms, which suffer from lengthy computation times and limited precision. This paper presents a solution by introducing an interpretable network that can effectively and rapidly extract ASC via deep unfolding. Initially, we create a dictionary containing reliable prior knowledge and apply it to the iterative shrinkage-thresholding algorithm (ISTA). Then, we unfold ISTA into a neural network, employing it to autonomously and precisely optimize the hyperparameters. The interpretability of physics is retained by applying a dictionary with physical meaning. The experiments are conducted on multiple test sets with diverse data distributions and demonstrate the superior performance and generalizability of our method.
Deep Learning-Based CSI Feedback for XL-MIMO Systems in the Near-Field Domain
Authors: Zhangjie Peng, Ruijing Liu, Zhaotian Li, Cunhua Pan, Jiangzhou Wang
Date: 2024-05-15 00:00:00

In this paper, we consider an extremely large-scale massive multiple-input-multiple-output (XL-MIMO) system. As the scale of antenna arrays increases, the range of near-field communications also expands. In this case, the signals no longer exhibit planar wave characteristics but spherical wave characteristics in the near-field channel, which makes the channel state information (CSI) highly complex. Additionally, the increase of the antenna arrays scale also makes the size of the CSI matrix significantly increase. Therefore, CSI feedback in the near-field channel becomes highly challenging. To solve this issue, we propose a deep-learning (DL)-based ExtendNLNet that can compress the CSI, and further reduce the overhead of CSI feedback. In addition, we have introduced the Non-Local block to obtain a larger area of CSI features. Simulation results show that the proposed ExtendNLNet can significantly improve the CSI recovery quality compared to other DL-based methods.
Multi-Objective Optimization-based Transmit Beamforming for Multi-Target and Multi-User MIMO-ISAC Systems
Authors: Chunwei Meng, Zhiqing Wei, Dingyou Ma, Wanli Ni, Liyan Su, Zhiyong Feng
Date: 2024-05-15 00:00:00
Tasks: Fairness, Integrated sensing and communication, ISAC

Integrated sensing and communication (ISAC) is an enabling technology for the sixth-generation mobile communications, which equips the wireless communication networks with sensing capabilities. In this paper, we investigate transmit beamforming design for multiple-input and multiple-output (MIMO)-ISAC systems in scenarios with multiple radar targets and communication users. A general form of multi-target sensing mutual information (MI) is derived, along with its upper bound, which can be interpreted as the sum of individual single-target sensing MI. Additionally, this upper bound can be achieved by suppressing the cross-correlation among reflected signals from different targets, which aligns with the principles of adaptive MIMO radar. Then, we propose a multi-objective optimization framework based on the signal-to-interference-plus-noise ratio of each user and the tight upper bound of sensing MI, introducing the Pareto boundary to characterize the achievable communication-sensing performance boundary of the proposed ISAC system. To achieve the Pareto boundary, the max-min system utility function method is employed, while considering the fairness between communication users and radar targets. Subsequently, the bisection search method is employed to find a specific Pareto optimal solution by solving a series of convex feasible problems. Finally, simulation results validate that the proposed method achieves a better tradeoff between multi-user communication and multi-target sensing performance. Additionally, utilizing the tight upper bound of sensing MI as a performance metric can enhance the multi-target resolution capability and angle estimation accuracy.
Task-Oriented Mulsemedia Communication using Unified Perceiver and Conformal Prediction in 6G Wireless Systems
Authors: Hongzhi Guo, Ian F. Akyildiz
Date: 2024-05-14 00:00:00
Tasks: Conformal Prediction

The growing prominence of eXtended Reality (XR), holographic-type communications, and metaverse demands truly immersive user experiences by using many sensory modalities, including sight, hearing, touch, smell, taste, etc. Additionally, the widespread deployment of sensors in areas such as agriculture, manufacturing, and smart homes is generating diverse sensory data. A new media format known as multisensory media (mulsemedia) has emerged, which incorporates many sensory modalities beyond the traditional visual and auditory media. 6G wireless systems are envisioned to support the Internet of Senses, making it crucial to explore effective data fusion and communication strategies for mulsemedia. In this paper, we introduce a task-oriented multi-task mulsemedia communication system named MuSeCo, which is developed using unified Perceiver models and Conformal Prediction. This unified model can accept any sensory input and efficiently extract latent semantic features, making it adaptable for deployment across various Artificial Intelligence of Things (AIoT) devices. Conformal Prediction is employed for modality selection and combination, enhancing task accuracy while minimizing data communication overhead. The model is trained using six sensory modalities across four classification tasks. Simulations and experiments demonstrate that it can effectively fuse sensory modalities, significantly reduce end-to-end communication latency and energy consumption, and maintain high accuracy in communication-constrained systems.
Instantaneous Bandwidth Estimation from Level-Crossing Samples via LSTM-based Encoder-Decoder Architecture
Authors: Johannes Königs, Carsten Bockelmann, Armin Dekorsy
Date: 2024-05-14 00:00:00
Tasks: Decoder

This paper presents an approach for instantaneous bandwidth estimation from level-crossing (LC) samples using a long short-term memory (LSTM) encoder-decoder architecture. LC sampling is a nonuniform sampling technique that is particularly useful for energy-efficient acquisition of signals with sparse spectra. Especially in combination with fully analog wireless sensor nodes, LC sampling offers a viable alternative to traditional sampling methods. However, due to the nonuniform distribution of samples, reconstructing the original signal is a challenging task. One promising reconstruction approach is time-warping, where the local signal spectrum is taken into account. However, this requires an accurate estimate of the instantaneous bandwidth of the signal. In this paper, we show that applying a neural network to the problem of estimating instantaneous bandwidth from LC samples can improve the overall reconstruction accuracy. We conduct a comprehensive numerical analysis of the proposed approach and compare it to an intensity-based bandwidth estimation method from literature.
FDD Massive MIMO: How to Optimally Combine UL Pilot and Limited DL CSI Feedback?
Authors: Jungyeon Kim, Jinseok Choi, Jeonghun Park, Ahmed Alkhateeb, Namyoon Lee
Date: 2024-05-14 00:00:00
Tasks: Quantization

In frequency-division duplexing (FDD) multiple-input multiple-output (MIMO) systems, obtaining accurate downlink channel state information (CSI) for precoding is vastly challenging due to the tremendous feedback overhead with the growing number of antennas. Utilizing uplink pilots for downlink CSI estimation is a promising approach that can eliminate CSI feedback. However, the downlink CSI estimation accuracy diminishes significantly as the number of channel paths increases, resulting in reduced spectral efficiency. In this paper, we demonstrate that achieving downlink spectral efficiency comparable to perfect CSI is feasible by combining uplink CSI with limited downlink CSI feedback information. Our proposed downlink CSI feedback strategy transmits quantized phase information of downlink channel paths, deviating from conventional limited methods. We put forth a mean square error (MSE)-optimal downlink channel reconstruction method by jointly exploiting the uplink CSI and the limited downlink CSI. Armed with the MSE-optimal estimator, we derive the MSE as a function of the number of feedback bits for phase quantization. Subsequently, we present an optimal feedback bit allocation method for minimizing the MSE in the reconstructed channel through phase quantization. Utilizing a robust downlink precoding technique, we establish that the proposed downlink channel reconstruction method is sufficient for attaining a sum-spectral efficiency comparable to perfect CSI.
Design of a Multi-User RIS-Aided System with Statistical Channel Knowledge
Authors: Sadaf Syed, Dominik Semmler, Donia Ben Amor, Michael Joham, Wolfgang Utschick
Date: 2024-05-14 00:00:00

Reconfigurable intelligent surface (RIS) is a promising technology to enhance the spectral and energy efficiency in a wireless communication system. The design of the phase shifts of an RIS in every channel coherence interval demands a huge training overhead, making its deployment practically infeasible. The design complexity can be significantly reduced by exploiting the second-order statistics of the channels. This paper is the extension of our previous work to the design of an RIS for the multi-user setup, where we employ maximisation of the lower bound of the achievable sum-rate of the users. Unlike for the single-user case, obtaining a closed-form expression for the update of the filters and phase shifts is more challenging in the multi-user case. We resort to the fractional programming (FP) approach and the non-convex block coordinate descent (BCD) method to solve the optimisation problem. As the phase shifts of the RIS obtained by the proposed algorithms are based on the statistical channel knowledge, they do not need to be updated in every channel coherence interval.
Cooperative Sensing of Side Lobes Interference for mmWave Blockages Localization and Mapping
Authors: Hiba Dakdouk, Mohamed Sana, Benoit Denis
Date: 2024-05-14 00:00:00
Tasks: Management, Position

Radio localization and sensing are anticipated to play a crucial role in enhancing radio resource management in future networks. In this work, we focus on millimeter-wave communications, which are highly vulnerable to blockages, leading to severe attenuation and performance degradation. In a previous work, we proposed a novel mechanism that senses the radio environment to estimate the angular position of a moving blocker with respect to the sensing node. Building upon this foundation, this paper investigates the benefits of cooperation between different entities in the network by sharing sensed data to jointly locate the moving blocker while mapping the interference profile to probe the radio environment. Numerical evaluations demonstrate that cooperative sensing can achieve a more precise location estimation of the blocker as it further allows accurate estimation of its distance rather than its relative angular position only, leading to effective assessment of the blocker direction, trajectory and possibly, its speed, and size.
CFM6, a closed-form NLI EGN model supporting multiband transmission with arbitrary Raman amplification
Authors: Yanchao Jiang, Pierluigi Poggiolini
Date: 2024-05-14 00:00:00
Tasks: Form

We formulated a closed-form EGN model for nonlinear interference in ultra-wideband optical systems with arbitrary Raman amplification. This model enhanced the CISCO-POLITO-CFM5 performance by introducing a novel contribution attributed to the backward Raman amplification. It can handle the frequency-dependent fiber parameters and inter-channel stimulated Raman scattering.