pwc-archive/papers-with-abstracts huggingface dataset:
Mutual Coupling-Aware Channel Estimation and Beamforming for RIS-Assisted Communications
Authors: Pinjun Zheng, Simon Tarboush, Hadi Sarieddeen, Tareq Y. Al-Naffouri
Date: 2024-10-05 00:00:00
Tasks: compressed sensing

This work studies the problems of channel estimation and beamforming for active reconfigurable intelligent surface (RIS)-assisted multiple-input multiple-output (MIMO) communication, incorporating the mutual coupling (MC) effect through an electromagnetically consistent model based on scattering parameters. We first demonstrate that MC can be incorporated into a compressed sensing (CS) estimation formulation, albeit with an increase in the dimensionality of the sensing matrix. To overcome this increased complexity, we propose a two-stage strategy. Initially, a low-complexity MC-unaware CS estimation is performed to obtain a coarse channel estimate, which is then used to implement a dictionary reduction (DR), effectively reducing the dimensionality of the sensing matrices. This method achieves low complexity comparable to the conventional MC-unaware approach while providing estimation accuracy close to that of the high-complexity MC-aware CS method. Furthermore, we consider the joint optimization of RIS configuration, base station precoding, and user combining in an single-user MIMO system. We employ an alternating optimization strategy to optimize these three beamformers. The primary challenge lies in optimizing the RIS configuration, as the MC effect renders the problem non-convex and intractable. To address this, we propose a novel algorithm based on the successive convex approximation (SCA) and the Neumann series expansion. Within the SCA framework, we propose a surrogate function that rigorously satisfies both convexity and equal-gradient conditions to update the iteration direction. Numerical results validate our proposal, demonstrating that the proposed channel estimation and beamforming methods effectively manage the MC in RIS, achieving higher spectral efficiency compared to state-of-the-art approaches.
WiDistill: Distilling Large-scale Wi-Fi Datasets with Trajectory Matching
Authors: Tiantian Wang, Fei Wang
Date: 2024-10-05 00:00:00
Tasks: Activity Recognition, Human Activity Recognition

Wi-Fi based human activity recognition is a technology with immense potential in home automation, advanced caregiving, and enhanced security systems. It can distinguish human activity in environments with poor lighting and obstructions. However, most current Wi-Fi based human activity recognition methods are data-driven, leading to a continuous increase in the size of datasets. This results in a significant increase in the resources and time required to store and utilize these datasets. To address this issue, we propose WiDistill, a large-scale Wi-Fi datasets distillation method. WiDistill improves the distilled dataset by aligning the parameter trajectories of the distilled data with the recorded expert trajectories. WiDistill significantly reduces the need for the original large-scale Wi-Fi datasets and allows for faster training of models that approximate the performance of the original network, while also demonstrating robust performance in cross-network environments. Extensive experiments on the Widar3.0, XRF55, and MM-Fi datasets demonstrate that WiDistill outperforms other methods. The code can be found in https://github.com/the-sky001/WiDistill.
MIMO Detection with Spatial Sigma-Delta ADCs: A Variational Bayesian Approach
Authors: Toan-Van Nguyen, Sajjad Nassirpour, Italo Atzeni, Antti Tolli, A. Lee Swindlehurst, Duy H. N. Nguyen
Date: 2024-10-04 00:00:00
Tasks: Quantization

The spatial Sigma-Delta ($\Sigma\Delta$) architecture can be leveraged to reduce the quantization noise and enhance the effective resolution of few-bit analog-to-digital converters (ADCs) at certain spatial frequencies of interest. Utilizing the variational Bayesian (VB) inference framework, this paper develops novel data detection algorithms tailored for massive multiple-input multiple-output (MIMO) systems with few-bit $\Sigma\Delta$ ADCs and angular channel models, where uplink signals are confined to a specific angular sector. We start by modeling the corresponding Bayesian networks for the $1^{\mathrm{st}}$- and $2^{\mathrm{nd}}$-order $\Sigma\Delta$ receivers. Next, we propose an iterative algorithm, referred to as Sigma-Delta variational Bayes (SD-VB), for MIMO detection, offering low-complexity updates through closed-form expressions of the variational densities of the latent variables. Simulation results show that the proposed $2^{\mathrm{nd}}$-order SD-VB algorithm delivers the best symbol error rate (SER) performance while maintaining the same computational complexity as in unquantized systems, matched-filtering VB with conventional quantization, and linear minimum mean-squared error (LMMSE) methods. Moreover, the $1^{\mathrm{st}}$- and $2^{\mathrm{nd}}$-order SD-VB algorithms achieve their lowest SER at an antenna separation of one-fourth wavelength for a fixed number of antenna elements. The effects of the steering angle of the $\Sigma\Delta$ architecture, the number of ADC resolution bits, and the number of antennas and users are also extensively analyzed.
On Secret-Message Transmission by Echoing Encrypted Probes
Authors: Yingbo Hua
Date: 2024-10-04 00:00:00

A scheme for secure communications, called ``Secret-message Transmission by Echoing Encrypted Probes (STEEP)'', is revisited. STEEP is a round-trip scheme with a probing phase from one user to another and an echoing phase in the reverse direction. STEEP is shown to be broadly applicable to yield a positive secrecy rate in bits per channel use even if the receive channels at eavesdropper (Eve) are stronger than those between legitimate users in both forward and reverse directions. This paper focuses on STEEP in the following settings: using Gaussian probing signal and Gaussian linear encryption over MIMO Gaussian channel (G-STEEP); using phase-shift-keying probing signal and a nonlinear encryption over SISO channel (P-STEEP); and a variation of G-STEEP for multiple access communication (M-STEEP). In each of the settings, Eve is assumed to have any given number of antennas, and STEEP is shown to yield a positive secrecy rate subject to a sufficiently large power in the echoing phase, as long as Eve's receive channel in the probing phase is not noiseless. It is also shown that G-STEEP, subject to asymmetric large powers in forward and reverse directions, has its secrecy rate approaching the secret-key capacity based on Gaussian probing signal over MIMO Gaussian channel. STEEP does not require secure feedback channel, collaborative third party, in-band full-duplex or reciprocal channels between users, but only needs a design for echoing encrypted probes, asymmetric power allocation and/or collaborative round-trip coding.
Automating the Design of Multi-band Microstrip Antennas via Uniform Cross-Entropy Optimization
Authors: Ali Al-Zawqari, Ali Safa, Gert Vandersteen
Date: 2024-10-03 00:00:00
Tasks: Gaussian Processes

Automating the design of microstrip antennas has been an active area of research for the past decade. By leveraging machine learning techniques such as Genetic Algorithms (GAs) or, more recently, Deep Neural Networks (DNNs), a number of work have demonstrated the possibility of producing non-trivial antenna geometries that can be efficient in terms of area utilization or be used in complex multi-frequency-band scenarios. However, both GAs and DNNs are notoriously compute-expensive, often requiring hour-long run times in order to produce new antenna geometries. In this paper, we propose to explore the novel use of Cross-Entropy optimization as a Monte-Carlo sampling technique for optimizing the geometry of patch antennas given a target $S_{11}$ scattering parameter curve that a user wants to obtain. We compare our proposed Uniform Cross-Entropy (UCE) method against other popular Monte-Carlo optimization techniques such as Gaussian Processes, Forest optimization and baseline random search approaches. We demonstrate that the proposed UCE technique outperforms the competing methods while still having a reasonable compute complexity, taking around 16 minutes to converge. Finally, our code is released as open-source with the hope of being useful to future research.
Towards TMA-Based Transmissive RIS Transceiver Enabled Downlink Communication Networks: A Consensus-ADMM Approach
Authors: Zhendong Li, Wen Chen, Haoran Qin, Qingqing Wu, Xusheng Zhu, Ziheng Zhang, Jun Li
Date: 2024-10-04 00:00:00
Tasks: Fairness

This paper presents a novel multi-stream downlink communication system that utilizes a transmissive reconfigurable intelligent surface (RIS) transceiver. Specifically, we elaborate the downlink communication scheme using time-modulated array (TMA) technology, which enables high order modulation and multi-stream beamforming. Then, an optimization problem is formulated to maximize the minimum signal-to-interference-plusnoise ratio (SINR) with user fairness, which takes into account the constraint of the maximum available power for each transmissive element. Due to the non-convex nature of the formulated problem,finding optimal solution is challenging. To mitigate the complexity,we propose a linear-complexity beamforming algorithm based on consensus alternating direction method of multipliers (ADMM).Specifically, by introducing a set of auxiliary variables, the problem can be decomposed into multiple sub-problems that are amenable to parallel computation, where the each sub-problem can yield closed-form expressions, bringing a significant reduction in the computational complexity. The overall problem achieves convergence by iteratively addressing these sub-problems in an alternating manner. Finally, the convergence of the proposed algorithm and the impact of various parameter configurations on the system performance are validated through numerical simulations.
Development of a Digital Front-End for Electrooculography Circuits to Facilitate Digital Communication in Individuals with Communicative and Motor Disabilities
Authors: Andre Heid Rocha da Costa, Keiran Robert O'Keeffe
Date: 2024-10-03 00:00:00

This project developed a cost-effective, digital-viable front-end for electrooculography (EOG) circuits aimed at enabling communication for individuals with Locked-in Syndrome (LIS) and Amyotrophic Lateral Sclerosis (ALS). Using the TL072 operational amplifier, the system amplifies weak EOG signals and processes them through an Arduino Uno for real-time monitoring. The circuit includes preamplification, filtering between 0.1 Hz and 30 Hz, and final amplification stages, achieving accurate eye movement tracking with a 256 Hz sampling rate. The approach to this was described in detail, with a comparison drawn between the theoretical expectations of our circuit design and its viability in contrast to the actual values measured. Our readings aimed to create an interface that optimized max-gaze angle readings by outputting a maximum reading at values above the baseline theory of our amplification circuit. From this, we measured the latency between the serial output and action, analyzing video recordings of such readings. The Latency value read reached around 20ms, which is within the tolerance for proper communication and did not seriously affect the readings. Beyond this, challenges such as noise interference (with an SNR of 1.07 dB) remain despite achieving reliable signal amplification. This was during a test of the analog functionality of this circuit. However, its limitations mean that future improvements will focus on reducing environmental interference, optimizing electrode placement, applying a novel detection algorithm to optimize communication applications, and enhancing signal clarity to make the system more effective for real-world applications.
Prediabetes detection in unconstrained conditions using wearable sensors
Authors: Dimitra Tatli, Vasileios Papapanagiotou, Aris Liakos, Apostolos Tsapas, Anastasios Delopoulos
Date: 2024-10-03 00:00:00

Prediabetes is a common health condition that often goes undetected until it progresses to type 2 diabetes. Early identification of prediabetes is essential for timely intervention and prevention of complications. This research explores the feasibility of using wearable continuous glucose monitoring along with smartwatches with embedded inertial sensors to collect glucose measurements and acceleration signals respectively, for the early detection of prediabetes. We propose a methodology based on signal processing and machine learning techniques. Two feature sets are extracted from the collected signals, based both on a dynamic modeling of the human glucose-homeostasis system and on the Glucose curve, inspired by three major glucose related blood tests. Features are aggregated per individual using bootstrap. Support Vector Machines are used to classify normoglycemic vs. prediabetic individuals. We collected data from 22 participants for evaluation. The results are highly encouraging, demonstrating high sensitivity and precision. This work is a proof of concept, highlighting the potential of wearable devices in prediabetes assessment. Future directions involve expanding the study to a larger, more diverse population and exploring the integration of CGM and smartwatch functionalities into a unified device. Automated eating detecting algorithms can also be used.
Accurate Closed-Form Solution for Symbol Error Probability in Hexagonal QAM
Authors: Sukhsagar, Nagendra Kumar, Vimal Bhatia
Date: 2024-10-03 00:00:00
Tasks: Form

Future communication systems are anticipated to facilitate applications requiring high data transmission rates while maintaining energy efficiency. Hexagonal quadrature amplitude modulation (HQAM) offers this owing to its compact symbol arrangement within the two-dimensional (2D) plane. Building on the limitations of the current approaches, this letter presents a straightforward and precise approximation for calculating the symbol error probability (SEP) of HQAM in additive white Gaussian noise (AWGN) channel. The analytical and simulation results align across several signal-to-noise ratios (SNR). In addition, the proposed approximation is effective for accurately estimating the SEP of HQAM in scenarios with slow-fading, including those subject to Rayleigh fading.
Multi-View Integrated Imaging and Communication
Authors: Davide Tornielli Bellini, Dario Tagliaferri, Marouan Mizmizi, Stefano Tebaldini, Umberto Spagnolini
Date: 2024-10-03 00:00:00
Tasks: Integrated sensing and communication, ISAC

Non-line-of-sight (NLOS) operation is one of the open issues to be solved for integrated sensing and communication (ISAC) systems to become a pillar of the future wireless infrastructure above 10 GHz. Existing NLOS countermeasures use either metallic mirrors, that are limited in coverage, or reconfigurable metasurfaces, that are limited in size due to cost. This paper focuses on integrated imaging and communication (IIAC) systems for NLOS exploration, where a base station (BS) serves the users while gathering a high-resolution image of the area. We exploit a large reflection plane, that is phase-configured in space and time jointly with a proper BS beam sweeping to provide a multi-view observation of the area and maximizing the image resolution. Remarkably, we achieve a near-field imaging through successive far-field acquisitions, limiting the design complexity and cost. Numerical results prove the benefits of our proposal.
Propagation Distance Estimation for Radio over Fiber with Cascaded Structure
Authors: Dexin Kong, Diana Pamela Moya Osorio, Erik G. Larsson
Date: 2024-10-03 00:00:00

Recent developments in polymer microwave fiber (PMF) have opened great opportunities for robust, low-cost, and high-speed sub-terahertz (THz) communications. Noticing this great potential, this paper addresses the problem of estimation of the propagation distance of a sub-Thz signal along a radio over fiber structure. Particularly, this paper considers a novel cascaded structure that interconnects multiple radio units (RUs) via fiber for applications in indoor scenarios. Herein, we consider the cascaded effects of distortions introduced by non-linear power amplifiers at the RUs, and the propagation channel over the fiber is based on measurements obtained from transmissions of sub-THz signals on high-density polyethylene fibers. For the estimation of the propagation distance, non-linear least-squares algorithms are proposed, and our simulation results demonstrate that the proposed estimators present a good performance on the propagation distance estimation even in the presence of the cascaded effect of non-linear PAs.
Lightweight Diffusion Models for Resource-Constrained Semantic Communication
Authors: Giovanni Pignata, Eleonora Grassucci, Giordano Cicchetti, Danilo Comminiello
Date: 2024-10-03 00:00:00
Tasks: Quantization, Semantic Communication

Recently, generative semantic communication models have proliferated as they are revolutionizing semantic communication frameworks, improving their performance, and opening the way to novel applications. Despite their impressive ability to regenerate content from the compressed semantic information received, generative models pose crucial challenges for communication systems in terms of high memory footprints and heavy computational load. In this paper, we present a novel Quantized GEnerative Semantic COmmunication framework, Q-GESCO. The core method of Q-GESCO is a quantized semantic diffusion model capable of regenerating transmitted images from the received semantic maps while simultaneously reducing computational load and memory footprint thanks to the proposed post-training quantization technique. Q-GESCO is robust to different channel noises and obtains comparable performance to the full precision counterpart in different scenarios saving up to 75% memory and 79% floating point operations. This allows resource-constrained devices to exploit the generative capabilities of Q-GESCO, widening the range of applications and systems for generative semantic communication frameworks. The code is available at https://github.com/ispamm/Q-GESCO.
Multiple-Frequency-Bands Channel Characterization for In-vehicle Wireless Networks
Authors: Mengting Li, Yifa Li, Qiyu Zeng, Kim Olesen, Fengchun Zhang, Wei Fan
Date: 2024-10-03 00:00:00

In-vehicle wireless networks are crucial for advancing smart transportation systems and enhancing interaction among vehicles and their occupants. However, there are limited studies in the current state of the art that investigate the in-vehicle channel characteristics in multiple frequency bands. In this paper, we present measurement campaigns conducted in a van and a car across below 7 GHz, millimeter-wave (mmWave), and sub-Terahertz (Sub-THz) bands. These campaigns aim to compare the channel characteristics for in-vehicle scenarios across various frequency bands. Channel impulse responses (CIRs) were measured at various locations distributed across the engine compartment of both the van and car. The CIR results reveal a high similarity in the delay properties between frequency bands below 7GHz and mmWave bands for the measurements in the engine bay. Sparse channels can be observed at Sub-THz bands in the engine bay scenarios. Channel spatial profiles in the passenger cabin of both the van and car are obtained by the directional scan sounding scheme for three bands. We compare the power angle delay profiles (PADPs) measured at different frequency bands in two line of sight (LOS) scenarios and one non-LOS (NLOS) scenario. Some major \added{multipath components (MPCs)} can be identified in all frequency bands and their trajectories are traced based on the geometry of the vehicles. The angular spread of arrival is also calculated for three scenarios. The analysis of channel characteristics in this paper can enhance our understanding of in-vehicle channels and foster the evolution of in-vehicle wireless networks.
TRIS-HAR: Transmissive Reconfigurable Intelligent Surfaces-assisted Cognitive Wireless Human Activity Recognition Using State Space Models
Authors: Junshuo Liu, Yunlong Huang, Xin Shi, Rujing Xiong, Jianan Zhang, Tiebin Mi, Robert C. Qiu
Date: 2024-10-03 00:00:00
Tasks: Activity Recognition, Human Activity Recognition, Mamba, State Space Models

Human activity recognition (HAR) using radio frequency (RF) signals has garnered considerable attention for its applications in smart environments. However, traditional systems often struggle with limited independent channels between transmitters and receivers, multipath fading, and environmental noise, which particularly degrades performance in through-the-wall scenarios. In this paper, we present a transmissive reconfigurable intelligent surface (TRIS)-assisted through-the-wall human activity recognition (TRIS-HAR) system. The system employs TRIS technology to actively reshape wireless signal propagation, creating multiple independent paths to enhance signal clarity and improve recognition accuracy in complex indoor settings. Additionally, we propose the Human intelligence Mamba (HiMamba), an advanced state space model that captures temporal and frequency-based information for precise activity recognition. HiMamba achieves state-of-the-art performance on two public datasets, demonstrating superior accuracy. Extensive experiments indicate that the TRIS-HAR system improves recognition performance from 85.00% to 98.06% in laboratory conditions and maintains high performance across various environments. This approach offers a robust solution for enhancing RF-based HAR, with promising applications in smart home and elderly care systems.
Model-Based GNN Enabled Energy-Efficient Beamforming for Ultra-Dense Wireless Networks
Authors: Rongsheng Zhang, Yang Lu, Wei Chen, Bo Ai, Zhiguo Ding
Date: 2024-10-03 00:00:00
Tasks: Graph Neural Network

This paper investigates deep learning enabled beamforming design for ultra-dense wireless networks by integrating prior knowledge and graph neural network (GNN), named model-based GNN. A energy efficiency (EE) maximization problem is formulated subject to power budget and quality of service (QoS) requirements, which is reformulated based on the minimum mean square error scheme and the hybrid zero-forcing and maximum ratio transmission schemes. Based on the reformulated problem, the model-based GNN to realize the mapping from channel state information to beamforming vectors. Particular, the multi-head attention mechanism and residual connection are adopted to enhance the feature extracting, and a scheme selection module is designed to improve the adaptability of GNN. The unsupervised learning is adopted, and a various-input training strategy is proposed to enhance the stability of GNN. Numerical results demonstrate the millisecond-level response with limited performance loss, the scalability to different users and the adaptability to various channel conditions and QoS requirements of the model-based GNN in ultra-dense wireless networks.
GNN-Enabled Optimization of Placement and Transmission Design for UAV Communications
Authors: Qinyu Wang, Yang Lu, Wei Chen, Bo Ai, Zhangdui Zhong, Dusit Niyato
Date: 2024-10-03 00:00:00

This paper applies graph neural networks (GNN) in UAV communications to optimize the placement and transmission design. We consider a multiple-user multiple-input-single-output UAV communication system where a UAV intends to find a placement to hover and serve users with maximum energy efficiency (EE). To facilitate the GNN-based learning, we adopt the hybrid maximum ratio transmission and zero forcing scheme to design the beamforming vectors and a feature augment is implemented by manually setting edge features. Furthermore, we propose a two-stage GNN-based model where the first stage and the second stage yield the placement and the transmission design, respectively. The two stages are connected via a residual and their learnable weights are jointly optimized by via unsupervised learning. Numerical results illustrate the effectiveness and validate the scalability to both UAV antennas and users of the proposed model.