pwc-archive/papers-with-abstracts huggingface dataset:
A Digital Beamforming Receiver Architecture Implemented on a FPGA for Space Applications
Authors: Eduardo Ortega, Agustín Martínez, Antonio Oliva, Fernando Sanz, Oscar Rodríguez, Manuel Prieto, Pablo Parra, Antonio da Silva, Sebastián Sánchez
Date: 2024-05-29 00:00:00

The burgeoning interest within the space community in digital beamforming is largely attributable to the superior flexibility that satellites with active antenna systems offer for a wide range of applications, notably in communication services. This paper delves into the analysis and practical implementation of a Digital Beamforming and Digital Down Conversion (DDC) chain, leveraging a high-speed Analog-to-Digital Converter (ADC) certified for space applications alongside a high-performance Field-Programmable Gate Array (FPGA). The proposed design strategy focuses on optimizing resource efficiency and minimizing power consumption by strategically sequencing the beamformer processor ahead of the complex down-conversion operation. This innovative approach entails the application of demodulation and low-pass filtering exclusively to the aggregated beam channel, culminating in a marked reduction in the requisite digital signal processing resources relative to traditional, more resource-intensive digital beamforming and DDC architectures. In the experimental validation, an evaluation board integrating a high-speed ADC and a FPGA was utilized. This setup facilitated the empirical validation of the design's efficacy by applying various RF input signals to the digital beamforming receiver system. The ADC employed is capable of high-resolution signal processing, while the FPGA provides the necessary computational flexibility and speed for real-time digital signal processing tasks. The findings underscore the potential of this design to significantly enhance the efficiency and performance of digital beamforming systems in space applications.
High-SNR Comparison of Linear Precoding and DPC in RIS-Aided MIMO Broadcast Channels
Authors: Dominik Semmler, Benedikt Fesl, Michael Joham, Wolfgang Utschick
Date: 2024-05-29 00:00:00

We compare dirty paper coding (DPC) and linear precoding methods in a reconfigurable intelligent surface (RIS)- aided high-signal-to-noise ratio (SNR) scenario, where the channel between the base station (BS) and the RIS is dominated by a line-of-sight (LOS) component. Furthermore, we consider two groups of users where one group can be efficiently served by the BS, whereas the other one has a negligible direct channel and has to be served via the RIS. Within this scenario, we analytically show fundamental differences between DPC and linear methods. In particular, our analysis addresses two essential aspects, i.e., the orthogonality of the BS-RIS channel with the direct channel and a channel mitigation term, depending on the number of RIS elements, that is present only for linear precoding techniques. The mitigation term generally leads to strong limitations for the linear method, especially for random or statistical phase shifts. Moreover, we discuss under which circumstances this mitigation term is negligible and in which scenarios DPC and linear precoding lead to the same performance.
Layered Chirp Spread Spectrum Modulations for LPWANs
Authors: Ali Waqar Azim, Ahmad Bazzi, Roberto Bomfin, Raed Shubair, Marwa Chafii
Date: 2024-05-29 00:00:00

This article examines two chirp spread spectrum techniques specifically devised for low-power wide-area networks (LPWANs) to optimize energy and spectral efficiency (SE). These methods referred to as layered CSS (LCSS) and layered dual-mode CSS (LDMCSS), involves utilizing multiple layers for multiplexing symbols with varying chirp rates. These waveform designs exemplify a high degree of SE compared to existing schemes. Additionally, LDMCSS necessitates a lesser number of layers than LCSS to attain comparable SE, thereby reducing computational complexity. These proposed techniques can employ coherent and non-coherent detection and can be adjusted to achieve various spectral efficiencies by altering the number of multiplexed layers. Unlike our proposed LCSS and LDMCSS, other CSS alternatives for LPWANs cannot provide the same level of flexibility and SE. The performance of these techniques is evaluated in terms of bit error rate under different channel conditions, as well as with phase and frequency offsets.
Near-Field Localization with RIS via Two-Dimensional Signal Path Classification
Authors: Jeongwan Kang, Seung-Woo Ko, Sunwoo Kim
Date: 2024-05-29 00:00:00

In this paper, we propose two-dimensional signal path classification (2D-SPC) for reconfigurable intelligent surface (RIS)-assisted near-field (NF) localization. In the NF regime, multiple RIS-driven signal paths (SPs) can contribute to precise localization if these are decomposable and the reflected locations on the RIS are known, referred to as SP decomposition (SPD) and SP labeling (SPL), respectively. To this end, each RIS element modulates the incoming SP's phase by shifting it by one of the values in the phase shift profile (PSP) lists satisfying resolution requirements. By interworking with a conventional orthogonal frequency division multiplexing (OFDM) waveform, the user equipment can construct a 2D spectrum map that couples each SPs time of arrival (ToA) and PSP. Then, we design SPL by mapping SPs with the corresponding reflected RIS elements when they share the same PSP. Given two unlabeled SPs, we derive a geometric discriminant from checking whether the current label is correct. It can be extended to more than three SPs by sorting them using pairwise geometric discriminants between adjacent ones. From simulation results, it has been demonstrated that the proposed 2D SPC achieves consistent localization accuracy even if insufficient PSPs are given.
Decentralized Picosecond Synchronization for Distributed Wireless Systems
Authors: Naim Shandi, Jason M. Merlo, Jeffrey A. Nanzer
Date: 2024-05-28 00:00:00

We demonstrate a wireless, decentralized time-alignment method for distributed antenna arrays and distributed wireless networks that achieves picosecond-level synchronization. Distributed antenna arrays consist of spatially separated antennas that coordinate their functionality at the wavelength level to achieve coherent operations such as distributed beamforming. Accurate time alignment (synchronization) of the local clocks on each node in the array is necessary to support accurate time-delay beamforming of modulated signals. In this work we combine a consensus averaging algorithm and a high-accuracy wireless two-way time transfer method to achieve decentralized time alignment, correcting for the time-varying bias of the clocks in a method that has no central node. Internode time transfer is based on a spectrally-sparse, two-tone signal achieving near-optimal time delay accuracy. We experimentally demonstrate the approach in a wireless four-node software-defined radio system, with various network connectivity graphs. We show that within 20 iterations all the nodes achieve convergence within a bias of less than 12 ps and a standard deviation of less than 3 ps. The performance is evaluated versus the bandwidth of the two-tone waveform, which impacts the synchronization error, and versus the signal-to-noise ratio.
Artificial Intelligence Satellite Telecommunication Testbed using Commercial Off-The-Shelf Chipsets
Authors: Luis M. Garcés-Socarrás, Amirhossein Nik, Flor Ortiz, Juan A. Vásquez-Peralvo, Jorge L. González-Rios, Mouhamad Chehailty, Marcele Kuhfuss, Eva Lagunas, Jan Thoemel, Sumit Kumar, Vishal Singh, Juan C. Merlano Duncan, Sahar Malmir, Swetha Varadajulu, Jorge Querol, Symeon Chatzinotas
Date: 2024-05-28 00:00:00
Tasks: Management

The Artificial Intelligence Satellite Telecommunications Testbed (AISTT), part of the ESA project SPAICE, is focused on the transformation of the satellite payload by using artificial intelligence (AI) and machine learning (ML) methodologies over available commercial off-the-shelf (COTS) AI-capable chips for onboard processing. The objectives include validating artificial intelligence-driven SATCOM scenarios such as interference detection, spectrum sharing, radio resource management, decoding, and beamforming. The study highlights hardware selection and payload architecture. Preliminary results show that ML models significantly improve signal quality, spectral efficiency, and throughput compared to conventional payload. Moreover, the testbed aims to evaluate the performance and the use of AI-capable COTS chips in onboard SATCOM contexts.
Multi-modal Mood Reader: Pre-trained Model Empowers Cross-Subject Emotion Recognition
Authors: Yihang Dong, Xuhang Chen, Yanyan Shen, Michael Kwok-Po Ng, Tao Qian, Shuqiang Wang
Date: 2024-05-28 00:00:00
Tasks: Anatomy, EEG, Emotion Recognition, Unity

Emotion recognition based on Electroencephalography (EEG) has gained significant attention and diversified development in fields such as neural signal processing and affective computing. However, the unique brain anatomy of individuals leads to non-negligible natural differences in EEG signals across subjects, posing challenges for cross-subject emotion recognition. While recent studies have attempted to address these issues, they still face limitations in practical effectiveness and model framework unity. Current methods often struggle to capture the complex spatial-temporal dynamics of EEG signals and fail to effectively integrate multimodal information, resulting in suboptimal performance and limited generalizability across subjects. To overcome these limitations, we develop a Pre-trained model based Multimodal Mood Reader for cross-subject emotion recognition that utilizes masked brain signal modeling and interlinked spatial-temporal attention mechanism. The model learns universal latent representations of EEG signals through pre-training on large scale dataset, and employs Interlinked spatial-temporal attention mechanism to process Differential Entropy(DE) features extracted from EEG data. Subsequently, a multi-level fusion layer is proposed to integrate the discriminative features, maximizing the advantages of features across different dimensions and modalities. Extensive experiments on public datasets demonstrate Mood Reader's superior performance in cross-subject emotion recognition tasks, outperforming state-of-the-art methods. Additionally, the model is dissected from attention perspective, providing qualitative analysis of emotion-related brain areas, offering valuable insights for affective research in neural signal processing.
Deep Learning-based Epicenter Localization using Single-Station Strong Motion Records
Authors: Melek Türkmen, Sanem Meral, Baris Yilmaz, Melis Cikis, Erdem Akagündüz, Salih Tileylioglu
Date: 2024-05-28 00:00:00
Tasks: Transfer Learning

This paper explores the application of deep learning (DL) techniques to strong motion records for single-station epicenter localization. Often underutilized in seismology-related studies, strong motion records offer a potential wealth of information about seismic events. We investigate whether DL-based methods can effectively leverage this data for accurate epicenter localization. Our study introduces AFAD-1218, a collection comprising more than 36,000 strong motion records sourced from Turkey. To utilize the strong motion records represented in either the time or the frequency domain, we propose two neural network architectures: deep residual network and temporal convolutional networks. Through extensive experimentation, we demonstrate the efficacy of DL approaches in extracting meaningful insights from these records, showcasing their potential for enhancing seismic event analysis and localization accuracy. Notably, our findings highlight significant reductions in prediction error achieved through the exclusion of low signal-to-noise ratio records, both in nationwide experiments and regional transfer-learning scenarios. Overall, this research underscores the promise of DL techniques in harnessing strong motion records for improved seismic event characterization and localization.
Mutual Information Analysis of Neuromorphic Coding for Distributed Wireless Spiking Neural Networks
Authors: Pietro Savazzi, Anna Vizziello, Fabio Dell'Acqua
Date: 2024-05-28 00:00:00

Wireless spiking neural networks (WSNNs) allow energy-efficient communications, especially when considering edge intelligence and learning for both terrestrial beyond 5G/6G and space networking systems. Recent research work has revealed that distributed wireless SNNs (DWSNNs) show good performance in terms of inference accuracy and low energy consumption of edge devices, under the constraints of limited bandwidth and spike loss probability. Following this reasoning, this technology can be promising for wireless sensor networks (WSNs) in space applications, where the energy constraint is predominant. In this work, we focus on neuromorphic impulse radio (IR) transmission techniques for DWSNNs, quantitatively evaluating the features of different coding algorithms that can be viewed as IR modulations. Specifically, the main contribution of this work is the evaluation of information-theoretic measures that may help in quantifying performance trade-offs among existing neuromorphic coding techniques.
Bistatic Sensing at THz Frequencies via a Two-Stage Ultra-Wideband MIMO-OFDM System
Authors: Tommaso Bacchielli, Lorenzo Pucci, Davide Dardari, Andrea Giorgetti
Date: 2024-05-28 00:00:00
Tasks: Integrated sensing and communication, ISAC, Position

Only the chairs can edit The availability of abundant bandwidth at terahertz (THz) frequencies holds promise for significantly enhancing the sensing performance of integrated sensing and communication (ISAC) systems in the next-generation wireless systems, enabling high accuracy and resolution for precise target localization. In orthogonal frequency-division multiplexing (OFDM) systems, wide bandwidth can be achieved by increasing the subcarrier spacing rather than the number of subcarriers, thereby keeping the complexity of the sensing system low. However, this approach may lead to an ambiguity problem in target range estimation. To address this issue, this work proposes a two-stage maximum likelihood method for estimating target position in an ultra-wideband bistatic multiple-antenna OFDM-based ISAC system operating at THz frequencies. Numerical results show that the proposed estimation approach effectively resolves the ambiguity problem while achieving high resolution and accuracy target position estimation at a very low signal-to-noise ratio regime.
Batteryless BLE and Light-based IoT Sensor Nodes for Reliable Environmental Sensing
Authors: Jimmy Fernandez Landivar, Khojiakbar Botirov, Hazem Sallouha, Marcos Katz, Sofie Pollin
Date: 2024-05-27 00:00:00

The sustainable design of Internet of Things (IoT) networks encompasses considerations related to energy efficiency and autonomy as well as considerations related to reliable communications, ensuring no energy is wasted on undelivered data. Under these considerations, this work proposes the design and implementation of energy-efficient Bluetooth Low Energy (BLE) and Light-based IoT (LIoT) batteryless IoT sensor nodes powered by an indoor light Energy Harvesting Unit (EHU). Our design intends to integrate these nodes into a sensing network to improve its reliability by combining both technologies and taking advantage of their features. The nodes incorporate state-of-the-art components, such as low-power sensors and efficient System-on-Chips (SoCs). Moreover, we design a strategy for adaptive switching between active and sleep cycles as a function of the available energy, allowing the IoT nodes to continuously operate without batteries. Our results show that by adapting the duty cycle of the BLE and LIoT nodes depending on the environment's light intensity, we can ensure a continuous and reliable node operation. In particular, measurements show that our proposed BLE and LIoT node designs are able to communicate with an IoT gateway in a bidirectional way, every 19.3 and 624.6 seconds, respectively, in an energy-autonomous and reliable manner.
Enhanced Automotive Radar Collaborative Sensing By Exploiting Constructive Interference
Authors: Lifan Xu, Shunqiao Sun, A. Lee Swindlehurst
Date: 2024-05-27 00:00:00
Tasks: object-detection, Object Detection

Automotive radar emerges as a crucial sensor for autonomous vehicle perception. As more cars are equipped radars, radar interference is an unavoidable challenge. Unlike conventional approaches such as interference mitigation and interference-avoiding technologies, this paper introduces an innovative collaborative sensing scheme with multiple automotive radars that exploits constructive interference. Through collaborative sensing, our method optimally aligns cross-path interference signals from other radars with another radar's self-echo signals, thereby significantly augmenting its target detection capabilities. This approach alleviates the need for extensive raw data sharing between collaborating radars. Instead, only an optimized weighting matrix needs to be exchanged between the radars. This approach considerably decreases the data bandwidth requirements for the wireless channel, making it a more feasible and practical solution for automotive radar collaboration. Numerical results demonstrate the effectiveness of the constructive interference approach for enhanced object detection capability.
Towards Accurate Ego-lane Identification with Early Time Series Classification
Authors: Yuchuan Jin, Theodor Stenhammar, David Bejmer, Axel Beauvisage, Yuxuan Xia, Junsheng Fu
Date: 2024-05-27 00:00:00
Tasks: Autonomous Driving, Time Series, Time Series Classification

Accurate and timely determination of a vehicle's current lane within a map is a critical task in autonomous driving systems. This paper utilizes an Early Time Series Classification (ETSC) method to achieve precise and rapid ego-lane identification in real-world driving data. The method begins by assessing the similarities between map and lane markings perceived by the vehicle's camera using measurement model quality metrics. These metrics are then fed into a selected ETSC method, comprising a probabilistic classifier and a tailored trigger function, optimized via multi-objective optimization to strike a balance between early prediction and accuracy. Our solution has been evaluated on a comprehensive dataset consisting of 114 hours of real-world traffic data, collected across 5 different countries by our test vehicles. Results show that by leveraging road lane-marking geometry and lane-marking type derived solely from a camera, our solution achieves an impressive accuracy of 99.6%, with an average prediction time of only 0.84 seconds.
Reliable Sub-Nyquist Spectrum Sensing via Conformal Risk Control
Authors: Hyojin Lee, Sangwoo Park, Osvaldo Simeone, Yonina C. Eldar, Joonhyuk Kang
Date: 2024-05-27 00:00:00

Detecting occupied subbands is a key task for wireless applications such as unlicensed spectrum access. Recently, detection methods were proposed that extract per-subband features from sub-Nyquist baseband samples and then apply thresholding mechanisms based on held-out data. Such existing solutions can only provide guarantees in terms of false negative rate (FNR) in the asymptotic regime of large held-out data sets. In contrast, this work proposes a threshold mechanism-based conformal risk control (CRC), a method recently introduced in statistics. The proposed CRC-based thresholding technique formally meets user-specified FNR constraints, irrespective of the size of the held-out data set. By applying the proposed CRC-based framework to both reconstruction-based and classification-based sub-Nyquist spectrum sensing techniques, it is verified via experimental results that CRC not only provides theoretical guarantees on the FNR but also offers competitive true negative rate (TNR) performance.
Beware of Overestimated Decoding Performance Arising from Temporal Autocorrelations in Electroencephalogram Signals
Authors: Xiran Xu, Bo wang, Boda Xiao, Yadong Niu, Yiwen Wang, Xihong Wu, Jing Chen
Date: 2024-05-27 00:00:00
Tasks: Brain Computer Interface, EEG, Eeg Decoding, Electroencephalogram (EEG), Emotion Recognition, Experimental Design

Researchers have reported high decoding accuracy (>95%) using non-invasive Electroencephalogram (EEG) signals for brain-computer interface (BCI) decoding tasks like image decoding, emotion recognition, auditory spatial attention detection, etc. Since these EEG data were usually collected with well-designed paradigms in labs, the reliability and robustness of the corresponding decoding methods were doubted by some researchers, and they argued that such decoding accuracy was overestimated due to the inherent temporal autocorrelation of EEG signals. However, the coupling between the stimulus-driven neural responses and the EEG temporal autocorrelations makes it difficult to confirm whether this overestimation exists in truth. Furthermore, the underlying pitfalls behind overestimated decoding accuracy have not been fully explained due to a lack of appropriate formulation. In this work, we formulate the pitfall in various EEG decoding tasks in a unified framework. EEG data were recorded from watermelons to remove stimulus-driven neural responses. Labels were assigned to continuous EEG according to the experimental design for EEG recording of several typical datasets, and then the decoding methods were conducted. The results showed the label can be successfully decoded as long as continuous EEG data with the same label were split into training and test sets. Further analysis indicated that high accuracy of various BCI decoding tasks could be achieved by associating labels with EEG intrinsic temporal autocorrelation features. These results underscore the importance of choosing the right experimental designs and data splits in BCI decoding tasks to prevent inflated accuracies due to EEG temporal autocorrelation.
A Deep-NN Beamforming Approach for Dual Function Radar-Communication THz UAV
Authors: Gianluca Fontanesi, Anna Guerra, Francesco Guidi, Juan A. Vásquez-Peralvo, Nir Shlezinger, Alberto Zanella, Eva Lagunas, Symeon Chatzinotas, Davide Dardari, Petar M. Djurić
Date: 2024-05-27 00:00:00

In this paper, we consider a scenario with one UAV equipped with a ULA, which sends combined information and sensing signals to communicate with multiple GBS and, at the same time, senses potential targets placed within an interested area on the ground. We aim to jointly design the transmit beamforming with the GBS association to optimize communication performance while ensuring high sensing accuracy. We propose a predictive beamforming framework based on a dual DNN solution to solve the formulated nonconvex optimization problem. A first DNN is trained to produce the required beamforming matrix for any point of the UAV flying area in a reduced time compared to state-of-the-art beamforming optimizers. A second DNN is trained to learn the optimal mapping from the input features, power, and EIRP constraints to the GBS association decision. Finally, we provide an extensive simulation analysis to corroborate the proposed approach and show the benefits of EIRP, SINR performance and computational speed.
Waveforms for Computing Over the Air
Authors: Ana Pérez-Neira, Marc Martinez-Gost, Alphan Şahin, Saeed Razavikia, Carlo Fischione, Kaibin Huang
Date: 2024-05-27 00:00:00

Over-the-air computation (AirComp) leverages the signal-superposition characteristic of wireless multiple access channels to perform mathematical computations. Initially introduced to enhance communication reliability in interference channels and wireless sensor networks, AirComp has more recently found applications in task-oriented communications, namely, for wireless distributed learning and in wireless control systems. Its adoption aims to address latency challenges arising from an increased number of edge devices or IoT devices accessing the constrained wireless spectrum. This paper focuses on the physical layer of these systems, specifically on the waveform and the signal processing aspects at the transmitter and receiver to meet the challenges that AirComp presents within the different contexts and use cases.