pwc-archive/papers-with-abstracts huggingface dataset:
Efficient UAV Hovering, Resource Allocation, and Trajectory Design for ISAC with Limited Backhaul Capacity
Authors: Ata Khalili, Atefeh Rezaei, Dongfang Xu, Falko Dressler, Robert Schober
Date: 2024-04-30 00:00:00
Tasks: Integrated sensing and communication, ISAC

In this paper, we investigate the joint resource allocation and trajectory design for a multi-user, multi-target unmanned aerial vehicle (UAV)-enabled integrated sensing and communication (ISAC) system, where the link capacity between a ground base station (BS) and the UAV is limited. The UAV conducts target sensing and information transmission in orthogonal time slots to prevent interference. As is common in practical systems, sensing is performed while the UAV hovers, allowing the UAV to acquire high-quality sensing data. Subsequently, the acquired sensing data is offloaded to the ground BS for further processing. We jointly optimize the UAV trajectory, UAV velocity, beamforming for the communication users, power allocated to the sensing beam, and time of hovering for sensing to minimize the power consumption of the UAV while ensuring the communication quality of service (QoS) and successful sensing. Due to the prohibitively high complexity of the resulting non-convex mixed integer non-linear program (MINLP), we employ a series of transformations and optimization techniques, including semidefinite relaxation, big-M method, penalty approach, and successive convex approximation, to obtain a low-complexity suboptimal solution. Our simulation results reveal that 1) the proposed design achieves significant power savings compared to two baseline schemes; 2) stricter sensing requirements lead to longer sensing times, highlighting the challenge of efficiently managing both sensing accuracy and sensing time; 3) the optimized trajectory design ensures precise hovering directly above the targets during sensing, enhancing sensing quality and enabling the application of energy-focused beams; and 4) the proposed trajectory design balances the capacity of the backhaul link and the downlink rate of the communication users.
Coordinated Half-Duplex/Full-Duplex Cooperative Rate-Splitting Multiple Access in Multi-Cell Networks
Authors: Mohamed Elhattab, Shreya Khisa, Chadi Assi, Ali Ghrayeb, Marwa Qaraqe, Georges Kaddoum
Date: 2024-09-02 00:00:00

This paper explores downlink Cooperative Rate-Splitting Multiple Access (C-RSMA) in a multi-cell wireless network with the assistance of Joint-Transmission Coordinated Multipoint (JT-CoMP). In this network, each cell consists of a base station (BS) equipped with multiple antennas, one or more cell-center users (CCU), and multiple cell-edge users (CEU) located at the edge of the cells. Through JT-CoMP, all the BSs collaborate to simultaneously transmit the data to all the users including the CCUs and CEUs. To enhance the signal quality for the CEUs, CCUs relay the common stream to the CEUs by operating in either half-duplex (HD) or full-duplex (FD) decode-and-forward (DF) relaying mode. In this setup, we aim to jointly optimize the beamforming vectors at the BS, the allocation of common stream rates, the transmit power at relaying users, i.e., CCUs, and the time slot fraction, aiming to maximize the minimum achievable data rate. However, the formulated optimization problem is non-convex and is challenging to solve directly. To address this challenge, we employ change-of-variables, first-order Taylor approximations, and a low-complexity algorithm based on Successive Convex Approximation (SCA). We demonstrate through simulation results the efficacy of the proposed scheme, in terms of average achievable data rate, and we compare its performance to that of four baseline schemes, including HD/FD cooperative non-orthogonal multiple access (C-NOMA), NOMA, and RSMA without user cooperation. The results show that the proposed FD C-RSMA can achieve 25% over FD C-NOMA and the proposed HD C-RSMA can achieve 19% over HD C-NOMA respectively, when the BS transmit power is 20 dBm.
Energy Harvesting Characterization in Cell-Free Massive MIMO Using Markov Chains
Authors: Muhammad Zeeshan Mumtaz, Mohammadali Mohammadi, Hien Quoc Ngo, Michail Matthaiou
Date: 2024-09-02 00:00:00

This paper explores a discrete energy state transition model for energy harvesting (EH) in cell-free massive multiple-input multiple-output (CF-mMIMO) networks. Multiple-antenna access points (APs) provide wireless power and information to single-antenna UE equipment (UEs). The harvested energy at the UEs is used for both uplink (UL) training and data transmission. We investigate the energy transition probabilities based on the energy differential achieved in each coherence interval. A Markov chain-based stochastic process is introduced to characterize the evolving UE energy status. A detailed statistical model is developed for a non-linear EH circuit at the UEs, using the derived closed-form expressions for the mean and variance of the harvested energy. More specifically, simulation results confirm that the proposed Gamma distribution approximation can accurately capture the statistical behavior of the harvested energy. Furthermore, the energy state transitions are evaluated using the proposed Markov chain-based framework, while mathematical expressions for the self, positive and negative transition probabilities of the discrete energy states are also presented. Our numerical results depict that increasing the number of APs with a constant number of service antennas provides significant improvement in the positive energy state transition and reduces the negative transition probabilities of the overall network.
EEG-Language Modeling for Pathology Detection
Authors: Sam Gijsen, Kerstin Ritter
Date: 2024-09-02 00:00:00
Tasks: Contrastive Learning, EEG, Language Modeling, Language Modelling, Multiple Instance Learning, Representation Learning, Retrieval, Text Segmentation, zero-shot-classification, Zero-Shot Learning

Multimodal language modeling has enabled breakthroughs for representation learning, yet remains unexplored in the realm of functional brain data for pathology detection. This paper pioneers EEG-language models (ELMs) trained on clinical reports and 15000 EEGs. We propose to combine multimodal alignment in this novel domain with timeseries cropping and text segmentation, enabling an extension based on multiple instance learning to alleviate misalignment between irrelevant EEG or text segments. Our multimodal models significantly improve pathology detection compared to EEG-only models across four evaluations and for the first time enable zero-shot classification as well as retrieval of both neural signals and reports. In sum, these results highlight the potential of ELMs, representing significant progress for clinical applications.
Orthogonal Time Frequency Multiplexing (OTFDM): A Novel Waveform Targeted for IMT-2030
Authors: Koteswara Rao Gudimitla, Sibgath Ali Khan M, Kiran Kuchi
Date: 2024-09-02 00:00:00

The rapid evolution of the International Mobile Telecommunications (IMT) landscape has prompted the International Telecommunications Union Working Party 5D (ITU WP5D) to outline the framework for IMT-2030 and beyond. This next-generation initiative seeks to meet the diverse demands of future networks, with key objectives including hyper-low latency, enhanced energy efficiency, and robust support for high mobility. Current 5th generation (5G) technologies employ waveforms like Orthogonal Frequency Division Multiplexing (OFDM) and Discrete Fourier Transform Spread Orthogonal Frequency Division Multiplexing (DFT-s-OFDM). However, these waveforms are insufficient to fully meet the stringent requirements of next-generation communication systems. This paper introduces a novel waveform, Orthogonal Time Frequency Division Multiplexing (OTFDM), designed to address the limitations of existing waveforms. OTFDM achieves ultra-low latency by enabling single-shot transmission of data and Reference Signals (RS) within a single symbol. Furthermore, OTFDM supports high mobility with improved resilience to Doppler shifts and enhances power amplifier efficiency through its low Peak-to-Average Power Ratio (PAPR) characteristics. The proposed waveform incorporates advanced signal processing techniques, including time-frequency multiplexing and frequency domain spectrum shaping, to mitigate inter-symbol interference (ISI). These techniques enable accurate per-symbol channel estimation, thus supporting higher-order modulations even at higher user speeds. Extensive simulations validate the efficacy of OTFDM, demonstrating its capability to support user speeds up to 500 Km/h with minimal RS overhead. This paper explores the technical aspects of OTFDM and discusses its potential implications for the next-generation wireless communication systems.
Monitoring of water sorption and swelling of potato starch-glycerol extruded blend by magnetic resonance imaging and multivariate curve resolution
Authors: Sílvia Mas Garcia, Jean-Michel Roger, Ruzica Ferbus, Denis Lourdin, Corinne C. Rondeau-Mouro
Date: 2024-09-02 00:00:00

Magnetic resonance microimaging (MR mu I) is an outstanding technique for studying water transfers in millimetric bio-based materials in a non-destructive and non-invasive manner. However, depending on the composition of the material, monitoring and quantification of these transfers can be very complex, and hence reliable image processing and analysis tools are necessary. In this study, a combination of MR mu I and multivariate curve resolution-alternating least squares (MCR-ALS) is proposed to monitor the water ingress into a potato starch extruded blend containing 20% glycerol that was shown to have interesting properties for biomedical, textile, and food applications. In this work, the main purpose of MCR is to provide spectral signatures and distribution maps of the components involved in the water uptake process that occurs over time with various kinetics. This approach allowed the description of the system evolution at a global (image) and a local (pixel) level, hence, permitted the resolution of two waterfronts, at two different times into the blend that could not be resolved by any other mathematical processing method usually used in magnetic resonance imaging (MRI). The results were supplemented by scanning electron microscopy (SEM) observations in order to interpret these two waterfronts in a biological and physico-chemical point of view.
Throughput Optimization in Cache-aided Networks: An Opportunistic Probing and Scheduling Approach
Authors: Zhou Zhang, Saman Atapattu, Yizhu Wang, Marco Di Renzo
Date: 2024-09-02 00:00:00
Tasks: Diversity, Scheduling

This paper addresses the challenges of throughput optimization in wireless cache-aided cooperative networks. We propose an opportunistic cooperative probing and scheduling strategy for efficient content delivery. The strategy involves the base station probing the relaying channels and cache states of multiple cooperative nodes, thereby enabling opportunistic user scheduling for content delivery. Leveraging the theory of Sequentially Planned Decision (SPD) optimization, we dynamically formulate decisions on cooperative probing and stopping time. Our proposed Reward Expected Thresholds (RET)-based strategy optimizes opportunistic probing and scheduling. This approach significantly enhances system throughput by exploiting gains from local caching, cooperative transmission and time diversity. Simulations confirm the effectiveness and practicality of the proposed Media Access Control (MAC) strategy.
DMRA: An Adaptive Line Spectrum Estimation Method through Dynamical Multi-Resolution of Atoms
Authors: Mingguang Han, Yi Zeng, Xiaoguang Li, Tiejun Li
Date: 2024-09-01 00:00:00
Tasks: compressed sensing, Computational Efficiency, Super-Resolution

We proposed a novel dense line spectrum super-resolution algorithm, the DMRA, that leverages dynamical multi-resolution of atoms technique to address the limitation of traditional compressed sensing methods when handling dense point-source signals. The algorithm utilizes a smooth $\tanh$ relaxation function to replace the $\ell_0$ norm, promoting sparsity and jointly estimating the frequency atoms and complex gains. To reduce computational complexity and improve frequency estimation accuracy, a two-stage strategy was further introduced to dynamically adjust the number of the optimized degrees of freedom. The strategy first increases candidate frequencies through local refinement, then applies a sparse selector to eliminate insignificant frequencies, thereby adaptively adjusting the degrees of freedom to improve estimation accuracy. Theoretical analysis were provided to validate the proposed method for multi-parameter estimations. Computational results demonstrated that this algorithm achieves good super-resolution performance in various practical scenarios and outperforms the state-of-the-art methods in terms of frequency estimation accuracy and computational efficiency.
Misaligned Over-The-Air Computation of Multi-Sensor Data with Wiener-Denoiser Network
Authors: Mingjun Du, Sihui Zheng, Xiao-Ping Zhang, Yuhan Dong
Date: 2024-09-01 00:00:00
Tasks: Blind Image Deblurring, Deblurring, Denoising, Image Deblurring

In data driven deep learning, distributed sensing and joint computing bring heavy load for computing and communication. To face the challenge, over-the-air computation (OAC) has been proposed for multi-sensor data aggregation, which enables the server to receive a desired function of massive sensing data during communication. However, the strict synchronization and accurate channel estimation constraints in OAC are hard to be satisfied in practice, leading to time and channel-gain misalignment. The paper formulates the misalignment problem as a non-blind image deblurring problem. At the receiver side, we first use the Wiener filter to deblur, followed by a U-Net network designed for further denoising. Our method is capable to exploit the inherent correlations in the signal data via learning, thus outperforms traditional methods in term of accuracy. Our code is available at https://github.com/auto-Dog/MOAC_deep
Estimating Channels With Hundreds of Sub-Paths for MU-MIMO Uplink: A Structured High-Rank Tensor Approach
Authors: Panqi Chen, Lei Cheng
Date: 2024-09-01 00:00:00

This letter introduces a structured high-rank tensor approach for estimating sub-6G uplink channels in multi-user multiple-input and multiple-output (MU-MIMO) systems. To tackle the difficulty of channel estimation in sub-6G bands with hundreds of sub-paths, our approach fully exploits the physical structure of channel and establishes the link between sub-6G channel model and a high-rank four-dimensional (4D) tensor Canonical Polyadic Decomposition (CPD) with three factor matrices being Vandermonde-constrained. Accordingly, a stronger uniqueness property is derived in this work. This model supports an efficient one-pass algorithm for estimating sub-path parameters, which ensures plug-in compatibility with the widely-used baseline. Our method performs much better than the state-of-the-art tensor-based techniques on the simulations adhering to the 3GPP 5G protocols.
Enhancing Multi-Stream Beamforming Through CQIs For 5G NR FDD Massive MIMO Communications: A Tuning-Free Scheme
Authors: Kai Li, Ying Li, Lei Cheng, Zhi-Quan Luo
Date: 2024-09-01 00:00:00
Tasks: Quantization

In the fifth-generation new radio (5G NR) frequency division duplex (FDD) massive multiple-input and multiple-output (MIMO) systems, downlink beamforming relies on the acquisition of downlink channel state information (CSI). Codebook based limited feedback schemes have been proposed and widely used in practice to recover the downlink CSI with low communication overhead. In such schemes, the performance of downlink beamforming is determined by the codebook design and the codebook indicator feedback. However, limited by the quantization quality of the codebook, directly utilizing the codeword indicated by the feedback as the beamforming vector cannot achieve high performance. Therefore, other feedback values, such as channel qualification indicator (CQI), should be considered to enhance beamforming. In this paper, we present the relation between CQI and the optimal beamforming vectors, based on which an empirical Bayes based intelligent tuning-free algorithm is devised to learn the optimal beamforming vector and the associated regularization parameter. The proposed algorithm can handle different communication scenarios of MIMO systems, including single stream and multiple streams data transmission scenarios. Numerical results have shown the excellent performance of the proposed algorithm in terms of both beamforming vector acquisition and regularization parameter learning.
Over-the-Air Computation in Cell-Free Massive MIMO Systems
Authors: Chen Chen, Emil Björnson, Carlo Fischione
Date: 2024-08-31 00:00:00

Over-the-air computation (AirComp) is considered as a communication-efficient solution for data aggregation and distributed learning by exploiting the superposition properties of wireless multi-access channels. However, AirComp is significantly affected by the uneven signal attenuation experienced by different wireless devices. Recently, Cell-free Massive MIMO (mMIMO) has emerged as a promising technology to provide uniform coverage and high rates by joint coherent transmission. In this paper, we investigate AirComp in Cell-free mMIMO systems, taking into account spatially correlated fading and channel estimation errors. In particular, we propose optimal designs of transmit coefficients and receive combing at different levels of cooperation among access points. Numerical results demonstrate that Cell-free mMIMO using fully centralized processing significantly outperforms conventional Cellular mMIMO with regard to the mean squared error (MSE). Moreover, we show that Cell-free mMIMO using local processing and large-scale fading decoding can achieve a lower MSE than Cellular mMIMO when the wireless devices have limited power budgets.
Fast Capacity Estimation in Ultra-dense Wireless Networks with Random Interference
Authors: Dandan Jiang, Rui Wang, Jiang Xue
Date: 2024-08-31 00:00:00
Tasks: Capacity Estimation

In wireless communication systems, the accurate and reliable evaluation of channel capacity is believed to be a fundamental and critical issue for terminals. However, with the rapid development of wireless technology, large-scale communication networks with significant random interference have emerged, resulting in extremely high computational costs for capacity calculation. In ultra-dense wireless networks with extremely large numbers of base stations (BSs) and users, we provide fast estimation methods for determining the capacity. We consider two scenarios according to the ratio of the number of users to the number of BSs, $\beta_m$. First, when $\beta_m\leq1$, the FIsher-Spiked Estimation (FISE) algorithm is proposed to determine the capacity by modeling the channel matrix with random interference as a Fisher matrix. Second, when $\beta_m>1$, based on a closed-form expression for capacity estimation requiring solely simple computations, we prove that this estimation stabilizes and remains invariant with increasing $\beta_m$. Our methods can guarantee high accuracy on capacity estimation with low complexity, which is faster than the existing methods. Furthermore, our approaches exhibit excellent generality, free of network area shapes, BS and user distributions, and sub-network locations. Extensive simulation experiments across various scenarios demonstrate the high accuracy and robustness of our methods.
Towards Sustainable Personalized On-Device Human Activity Recognition with TinyML and Cloud-Enabled Auto Deployment
Authors: Bidyut Saha, Riya Samanta, Soumya K Ghosh, Ram Babu Roy
Date: 2024-08-26 00:00:00
Tasks: Activity Recognition, Human Activity Recognition, Transfer Learning

Human activity recognition (HAR) holds immense potential for transforming health and fitness monitoring, yet challenges persist in achieving personalized outcomes and sustainability for on-device continuous inferences. This work introduces a wrist-worn smart band designed to address these challenges through a novel combination of on-device TinyML-driven computing and cloud-enabled auto-deployment. Leveraging inertial measurement unit (IMU) sensors and a customized 1D Convolutional Neural Network (CNN) for personalized HAR, users can tailor activity classes to their unique movement styles with minimal calibration. By utilising TinyML for local computations, the smart band reduces the necessity for constant data transmission and radio communication, which in turn lowers power consumption and reduces carbon footprint. This method also enhances the privacy and security of user data by limiting its transmission. Through transfer learning and fine-tuning on user-specific data, the system achieves a 37\% increase in accuracy over generalized models in personalized settings. Evaluation using three benchmark datasets, WISDM, PAMAP2, and the BandX demonstrates its effectiveness across various activity domains. Additionally, this work presents a cloud-supported framework for the automatic deployment of TinyML models to remote wearables, enabling seamless customization and on-device inference, even with limited target data. By combining personalized HAR with sustainable strategies for on-device continuous inferences, this system represents a promising step towards fostering healthier and more sustainable societies worldwide.
A Lightweight Human Pose Estimation Approach for Edge Computing-Enabled Metaverse with Compressive Sensing
Authors: Nguyen Quang Hieu, Dinh Thai Hoang, Diep N. Nguyen
Date: 2024-08-26 00:00:00
Tasks: Compressive Sensing, Edge-computing, Pose Estimation

The ability to estimate 3D movements of users over edge computing-enabled networks, such as 5G/6G networks, is a key enabler for the new era of extended reality (XR) and Metaverse applications. Recent advancements in deep learning have shown advantages over optimization techniques for estimating 3D human poses given spare measurements from sensor signals, i.e., inertial measurement unit (IMU) sensors attached to the XR devices. However, the existing works lack applicability to wireless systems, where transmitting the IMU signals over noisy wireless networks poses significant challenges. Furthermore, the potential redundancy of the IMU signals has not been considered, resulting in highly redundant transmissions. In this work, we propose a novel approach for redundancy removal and lightweight transmission of IMU signals over noisy wireless environments. Our approach utilizes a random Gaussian matrix to transform the original signal into a lower-dimensional space. By leveraging the compressive sensing theory, we have proved that the designed Gaussian matrix can project the signal into a lower-dimensional space and preserve the Set-Restricted Eigenvalue condition, subject to a power transmission constraint. Furthermore, we develop a deep generative model at the receiver to recover the original IMU signals from noisy compressed data, thus enabling the creation of 3D human body movements at the receiver for XR and Metaverse applications. Simulation results on a real-world IMU dataset show that our framework can achieve highly accurate 3D human poses of the user using only $82\%$ of the measurements from the original signals. This is comparable to an optimization-based approach, i.e., Lasso, but is an order of magnitude faster.