pwc-archive/papers-with-abstracts huggingface dataset:
Benchmarking and Validation of Sub-mW 30GHz VG-LNAs in 22nm FDSOI CMOS for 5G/6G Phased-Array Receivers
Authors: Domenico Zito, Michele Spasaro
Date: 2024-09-11 00:00:00
Tasks: Benchmarking

Next-generation (5G/6G) wireless systems demand low-power mm-wave phased-array ICs. Variable-gain LNAs (VGLNAs) are key building blocks enabling hardware complexity reduction, performance enhancement and functionality extension. This paper reports a performance benchmarking of two low-power 30GHz VG-LNAs for phased-array ICs, which provide a 7.5dB gain control for 18dB Taylor taper in a 30GHz 8x8 antenna array, for a comprehensive validation of the new class of VGLNAs and its design methodology. In particular, this paper reports a second and implementation (VG-LNA2) with a reduced number (four) of gain-control back-gate voltages and super-low-Vt MOSFETs, with respect to the previous first implementation (VG-LNA1) with six gain-control back-gate voltages and regular- Vt MOSFETs, both in the same 22nm FDSOI CMOS technology. The results show that VG-LNA2 exhibits performance comparable to those of VG-LNA1, with a slightly lower power consumption. Overall, the performance benchmarking shows that the design methodology adopted for the new class of VG-LNAs leads to record low-power consumption and small form factor solutions reaching the targeted performances, regardless of the arrangements of the back-gate voltages for gain control and transistor sets, resulting in a comprehensive validation of the innovative design features and effective design methodology.
Domain Adaptation for DoA Estimation in Multipath Channels with Interferences
Authors: Amitay Bar, Joseph S. Picard, Israel Cohen, Ronen Talmon
Date: 2024-09-12 00:00:00
Tasks: Domain Adaptation

We consider the problem of estimating the direction-of-arrival (DoA) of a desired source located in a known region of interest in the presence of interfering sources and multipath. We propose an approach that precedes the DoA estimation and relies on generating a set of reference steering vectors. The steering vectors' generative model is a free space model, which is beneficial for many DoA estimation algorithms. The set of reference steering vectors is then used to compute a function that maps the received signals from the adverse environment to a reference domain free from interfering sources and multipath. We show theoretically and empirically that the proposed map, which is analogous to domain adaption, improves DoA estimation by mitigating interference and multipath effects. Specifically, we demonstrate a substantial improvement in accuracy when the proposed approach is applied before three commonly used beamformers: the delay-and-sum (DS), the minimum variance distortionless response (MVDR), and the Multiple Signal Classification (MUSIC).
Polarforming for Wireless Communications: Modeling and Performance Analysis
Authors: Zijian Zhou, Jingze Ding, Chenbo Wang, Bingli Jiao, Rui Zhang
Date: 2024-09-12 00:00:00
Tasks: Diversity

This paper presents, for the first time, the concept of polarforming for wireless communications. Polarforming refers to a novel technique that enables the polarization of an antenna to shape into a desired polarization state for aligning with the polarization of an electromagnetic (EM) wave. It can fully leverage polarization diversity to enhance the performance of wireless communication systems through polarization matching. To implement polarforming, we propose a new paradigm of phase shifter (PS)-based polarization-reconfigurable antennas (PRAs) that can form linear, circular, and general elliptical polarizations by phase shift control. To further demonstrate the benefits of polarforming, we investigate a PRA-aided wireless communication system equipped with tunable polarization of antennas. We characterize the multiple-input multiple-output (MIMO) channel capacity of the considered system as a function of the phase shifts of PS-based PRAs. We also provide a detailed polarforming interpretation under the single-input single-output (SISO) scenario and theoretically show how polarforming differs from the conventional (analog) beamforming based on PSs. Moreover, we develop an alternating optimization approach to maximize the channel capacity for the systems with single-antenna transmitter/receiver. Based on the water-filling principle, we also derive an upper bound on the MIMO channel capacity with PS-based PRAs and then maximize this capacity bound by optimizing the phase shifts through alternating optimization. Finally, comprehensive simulation results are presented, which not only validate the effectiveness of polarforming in combating channel depolarization but also exhibit substantial performance improvements over conventional systems.
Variability in Grasp Type Distinction for Myoelectric Prosthesis Control Using a Non-Invasive Brain-Machine Interface
Authors: Corentin Piozin, Lisa Bouarroudj, Jean-Yves Audran, Brice Lavrard, Catherine Simon, Florian Waszak, Selim Eskiizmirliler
Date: 2024-09-11 00:00:00
Tasks: Classification, EEG, Motor Imagery

Decoding multiple movements from the same limb using electroencephalographic (EEG) activity is a key challenge with applications for controlling prostheses in upper-limb amputees. This study investigates the classification of four hand movements to control a modified Myobock prosthesis via EEG signals. We report results from three EEG recording sessions involving four amputees and twenty able-bodied subjects performing four grasp movements under three conditions: Motor Execution (ME), Motor Imagery (MI), and Motor Observation (MO). EEG preprocessing was followed by feature extraction using Common Spatial Patterns (CSP), Wavelet Decomposition (WD), and Riemannian Geometry. Various classification algorithms were applied to decode EEG signals, and a metric assessed pattern separability. We evaluated system performance across different electrode combinations and compared it to the original setup. Our results show that distinguishing movement from no movement achieved 100% accuracy, while classification between movements reached 70-90%. No significant differences were found between recording conditions in classification performance. Able-bodied participants outperformed amputees, but there were no significant differences in Motor Imagery. Performance did not improve across the sessions, and there was considerable variability in EEG pattern distinction. Reducing the number of electrodes by half led to only a 2% average accuracy drop. These results provide insights into developing wearable brain-machine interfaces, particularly for electrode optimization and training in grasp movement classification.
Addressing Pilot Contamination in Channel Estimation with Variational Autoencoders
Authors: Amar Kasibovic, Benedikt Fesl, Michael Baur, Wolfgang Utschick
Date: 2024-09-11 00:00:00

Pilot contamination (PC) is a well-known problem that affects massive multiple-input multiple-output (MIMO) systems. When frequency and pilots are reused between different cells, PC constitutes one of the main bottlenecks of the system's performance. In this paper, we propose a method based on the variational autoencoder (VAE), capable of reducing the impact of PC-related interference during channel estimation (CE). We obtain the first and second-order statistics of the conditionally Gaussian (CG) channels for both the user equipments (UEs) in a cell of interest and those in interfering cells, and we then use these moments to compute conditional linear minimum mean square error estimates. We show that the proposed estimator is capable of exploiting the interferers' additional statistical knowledge, outperforming other classical approaches. Moreover, we highlight how the achievable performance is tied to the chosen setup, making the setup selection crucial in the study of multi-cell CE.
Symbol Level Precoding for Systems with Improper Gaussian Interference
Authors: Lu Liu, Rang Liu, Ly V. Nguyen, A. Lee Swindlehurst
Date: 2024-09-11 00:00:00

This paper focuses on precoding design in multi-antenna systems with improper Gaussian interference (IGI), characterized by correlated real and imaginary parts. We first study block level precoding (BLP) and symbol level precoding (SLP) assuming the receivers apply a pre-whitening filter to decorrelate and normalize the IGI. We then shift to the scenario where the base station (BS) incorporates the IGI statistics in the SLP design, which allows the receivers to employ a standard detection algorithm without pre-whitenting. Finally we address the case where the channel and statistics of the IGI are unknown, and we formulate robust BLP and SLP designs that minimize the worst case performance in such settings. Interestingly, we show that for BLP, the worst-case IGI is in fact proper, while for SLP the worst case occurs when the interference signal is maximally improper, with fully correlated real and imaginary parts. Numerical results reveal the superior performance of SLP in terms of symbol error rate (SER) and energy efficiency (EE), especially for the case where there is uncertainty in the non-circularity of the jammer.
Robust Square Root Unscented Kalman filter of graph signals
Authors: Jinhui Hu, Haiquan Zhao, Yi Peng
Date: 2024-09-11 00:00:00
Tasks: State Estimation

Considering the problem of nonlinear and non-gaussian filtering of the graph signal, in this paper, a robust square root unscented Kalman filter based on graph signal processing is proposed. The algorithm uses a graph topology to generate measurements and an unscented transformation is used to obtain the priori state estimates. In addition, in order to enhance the numerical stability of the unscented Kalman filter, the algorithm combines the double square root decomposition method to update the covariance matrix in the graph frequency domain. Furthermore, to handle the non-Gaussian noise problem in the state estimation process, an error augmentation model is constructed in the graph frequency domain by unifying the measurement error and state error, which utilizes the Laplace matrix of the graph to effectively reduce the cumulative error at each vertex. Then the general robust cost function is adopted as the optimal criterion to deal with the error, which has more parameter options so that effectively suppresses the problems of random outliers and abnormal measurement values in the state estimation process. Finally, the convergence of the error of the proposed algorithm is firstly verified theoretically, and then the robustness of the proposed algorithm is verified by experimental simulation.
Downlink Beamforming for Cell-Free ISAC: A Fast Complex Oblique Manifold Approach
Authors: Shayan Zargari, Diluka Galappaththige, Chintha Tellambura, Geoffrey Ye Li
Date: 2024-09-10 00:00:00
Tasks: Integrated sensing and communication, ISAC

Cell-free integrated sensing and communication (CF-ISAC) systems are just emerging as an interesting technique for future communications. Such a system comprises several multiple-antenna access points (APs), serving multiple single-antenna communication users and sensing targets. However, efficient beamforming designs that achieve high precision and robust performance in densely populated networks are lacking. This paper proposes a new beamforming algorithm by exploiting the inherent Riemannian manifold structure. The aim is to maximize the communication sum rate while satisfying sensing beampattern gains and per AP transmit power constraints. To address this constrained optimization problem, a highly efficient augmented Lagrangian model-based iterative manifold optimization for CF-ISAC (ALMCI) algorithm is developed. This algorithm exploits the geometry of the proposed problem and uses a complex oblique manifold. Conventional convex-concave procedure (CCPA) and multidimensional complex quadratic transform (MCQT)-CSA algorithms are also developed as comparative benchmarks. The ALMCI algorithm significantly outperforms both of these. For example, with 16 APs having 12 antennas and 30 dBm transmit power each, our proposed ALMCI algorithm yields 22.7% and 6.7% sum rate gains over the CCPA and MCQT-CSA algorithms, respectively. In addition to improvement in communication capacity, the ALMCI algorithm achieves superior beamforming gains and reduced complexity.
Analog Beamforming Aided by Full-Dimension One-Bit Chains
Authors: Lina Liu, Weimin Xiao, Jialing Liu, Zhigang Rong
Date: 2024-09-10 00:00:00

This paper investigates the design of analog beamforming at the receiver in millimeter-wave (mmWave) multiple-input multiple-output (MIMO) systems, aided by full digital chains featuring 1-bit ADCs. We advocate utilizing these full digital chains to facilitate rapid channel estimation and beam acquisition for subsequent communication, even without prior knowledge of the training pilots. To balance energy consumption and implementation costs, we opt for 1-bit ADCs. We propose a two-stage maximum likelihood (ML)-based algorithm to estimate angles of arrival (AoAs) and facilitate the design of analog beamforming to maximize the received signal-to-noise ratio (SNR). We validate our proposed beamforming schemes in narrowband coherent channels through synthetic testing and in wideband coherent channels, particularly under the 3GPP clustered-delay-line (CDL)-C channel model.
Predictive Demodulation for Chaotic Communications
Authors: Marc Martinez-Gost, Ana Pérez-Neira, Miguel Ángel Lagunas
Date: 2024-09-10 00:00:00

Chaotic signals offer promising characteristics for wireless communications due to their wideband nature, low cross-correlation, and sensitivity to initial conditions. Although classical chaotic modulation schemes like Chaos Shift Keying (CSK) can theoretically match the performance of traditional modulation techniques (i.e., bit error rate), practical challenges, such as the difficulty in generating accurate signal replicas at the receiver, limit their effectiveness. Besides, chaotic signals are often considered unpredictable despite their deterministic nature. In this paper, we challenge this view by introducing a novel modulation scheme for chaotic communications that leverages the deterministic behavior of chaotic signals. The proposed approach eliminates the need for synchronized replicas of transmitted waveforms at the receiver. Moreover, to enhance noise robustness, we employ M-ary Frequency Shift Keying (FSK) modulation on the chaotic samples. Experimental results show that the proposed scheme significantly outperforms CSK when perfect replicas are unavailable, with the best performance achieved for low-order modulations, and resulting in minimal delay increase.
Compressed Sensing based Detection Schemes for Differential Spatial Modulation in Visible Light Communication Systems
Authors: Zichun Shi, Pu Miao, Peng Chen, Lei Xue, Li-Yang Zheng, Laiyuan Wang, Gaojie Chen
Date: 2024-09-10 00:00:00
Tasks: compressed sensing

Differential spatial modulation (DSM) exploits the time dimension to facilitate the differential modulation, which can perfectly avoid the challenge in acquiring of heavily entangled channel state information of visible light communication (VLC) system. However, it has huge search space and high complexity for large number of transmitters. In this paper, a novel vector correction (VC)-based orthogonal matching pursuit (OMP) detection algorithm is proposed to reduce the complexity, which exploits the sparsity and relativity of all transmitters, and then employs a novel correction criterion by correcting the index vectors of the error estimation for improving the demodulation performance. To overcome the local optimum dilemma in the atoms searching, an OMP-assisted genetic algorithm is also proposed to further improve the bit error rate (BER) performance of the VLC-DSM system. Simulation results demonstrate that the proposed schemes can significantly reduce the computational complexity at least by 62.5% while achieving an excellent BER performance as compared with traditional maximum likelihood based receiver.
Source Enumeration using the Distribution of Angles: A Robust and Parameter-Free Approach
Authors: Gokularam Muthukrishnan, Siva Shanmugam, Sheetal Kalyani
Date: 2024-09-10 00:00:00

Source enumeration, the task of estimating the number of sources from the signal received by the array of antennas, is a critical problem in array signal processing. Numerous methods have been proposed to estimate the number of sources under white or colored Gaussian noise. However, their performance degrades significantly in the presence of a limited number of observations and/or a large number of sources. In this work, we propose a method based on the distribution of angles that performs well in (a) independent Gaussian, (b) spatially colored Gaussian, and (c) heavy-tailed noise, even when the number of sources is large. We support the supremacy of our algorithm over state-of-the-art methods with extensive simulation results.
Power-Efficient Full-Duplex Satellite Communications Aided by Movable Antennas
Authors: Lifeng Lin, Jingze Ding, Zijian Zhou, Bingli Jiao
Date: 2024-09-10 00:00:00
Tasks: Multiobjective Optimization

This letter investigates a movable antenna (MA)-aided full-duplex (FD) satellite communication system, where the satellite, equipped with both transmit and receive MAs, serves multiple uplink (UL) and downlink (DL) user terminals (UTs) in FD mode. Specifically, we formulate a multiobjective optimization problem to minimize the UL and DL transmit powers under imperfect channel state information (CSI). To jointly optimize the MA positions and transmit powers, we propose a two-loop particle swarm optimization (PSO) algorithm based on a multiobjective optimization framework. Simulation results show that flexible adjustments of MA positions can effectively reduce the total UL and DL transmit powers, while also alleviating the burden on self-interference (SI) cancellation modules.
A Wearable Multi-Modal Edge-Computing System for Real-Time Kitchen Activity Recognition
Authors: Mengxi Liu, Sungho Suh, Juan Felipe Vargas, Bo Zhou, Agnes Grünerbl, Paul Lukowicz
Date: 2024-09-10 00:00:00
Tasks: Activity Recognition, Edge-computing, Human Activity Recognition

In the human activity recognition research area, prior studies predominantly concentrate on leveraging advanced algorithms on public datasets to enhance recognition performance, little attention has been paid to executing real-time kitchen activity recognition on energy-efficient, cost-effective edge devices. Besides, the prevalent approach of segregating data collection and context extraction across different devices escalates power usage, latency, and user privacy risks, impeding widespread adoption. This work presents a multi-modal wearable edge computing system for human activity recognition in real-time. Integrating six different sensors, ranging from inertial measurement units (IMUs) to thermal cameras, and two different microcontrollers, this system achieves end-to-end activity recognition, from data capture to context extraction, locally. Evaluation in an unmodified realistic kitchen validates its efficacy in recognizing fifteen activities, including a null class. Employing a compact machine learning model (184.5 kbytes) yields an average accuracy of 87.83 \%, with model inference completed in 25.26 ms on the microcontroller. Comparative analysis with alternative microcontrollers showcases power consumption and inference speed performance, demonstrating the proposed system's viability.
Optimizing Placement and Power Allocation in Reconfigurable Intelligent Sensing Surfaces for Enhanced Sensing and Communication Performance
Authors: Cheng Luo, Jie Hu, Luping Xiang, Kun Yang, Bo Lei
Date: 2024-09-10 00:00:00

In this letter, we investigate the design of multiple reconfigurable intelligent sensing surfaces (RISSs) that enhance both communication and sensing tasks. An RISS incorporates additional active elements tailored to improve sensing accuracy. Our initial task involves optimizing placement of RISSs to mitigate signal interference. Subsequently, we establish power allocation schemes for sensing and communication within the system. Our final consideration involves examining how sensing results can be utilized to enhance communication, alongside an evaluation of communication performance under the impact of sensing inaccuracies. Numerical results reveal that the sensing task reaches its optimal performance with a finite number of RISSs, while the communication task exhibits enhanced performance with an increasing number of RISSs. Additionally, we identify an optimal communication spot under user movement.
ECG Biometric Authentication Using Self-Supervised Learning for IoT Edge Sensors
Authors: Guoxin Wang, Shreejith Shanker, Avishek Nag, Yong Lian, Deepu John
Date: 2024-09-09 00:00:00
Tasks: Contrastive Learning, CPU, Quantization, Self-Supervised Learning

Wearable Internet of Things (IoT) devices are gaining ground for continuous physiological data acquisition and health monitoring. These physiological signals can be used for security applications to achieve continuous authentication and user convenience due to passive data acquisition. This paper investigates an electrocardiogram (ECG) based biometric user authentication system using features derived from the Convolutional Neural Network (CNN) and self-supervised contrastive learning. Contrastive learning enables us to use large unlabeled datasets to train the model and establish its generalizability. We propose approaches enabling the CNN encoder to extract appropriate features that distinguish the user from other subjects. When evaluated using the PTB ECG database with 290 subjects, the proposed technique achieved an authentication accuracy of 99.15%. To test its generalizability, we applied the model to two new datasets, the MIT-BIH Arrhythmia Database and the ECG-ID Database, achieving over 98.5% accuracy without any modifications. Furthermore, we show that repeating the authentication step three times can increase accuracy to nearly 100% for both PTBDB and ECGIDDB. This paper also presents model optimizations for embedded device deployment, which makes the system more relevant to real-world scenarios. To deploy our model in IoT edge sensors, we optimized the model complexity by applying quantization and pruning. The optimized model achieves 98.67% accuracy on PTBDB, with 0.48% accuracy loss and 62.6% CPU cycles compared to the unoptimized model. An accuracy-vs-time-complexity tradeoff analysis is performed, and results are presented for different optimization levels.