pwc-archive/papers-with-abstracts huggingface dataset:
THz Channels for Short-Range Mobile Networks: Multipath Clusters and Human Body Shadowing
Authors: Minseok Kim, Jun-ichi Takada, Minghe Mao, Che Chia Kang, Xin Du, Anirban Ghosh
Date: 2024-12-18 00:00:00

The THz band (0.1-10 THz) is emerging as a crucial enabler for sixth-generation (6G) mobile communication systems, overcoming the limitations of current technologies and unlocking new opportunities for low-latency and ultra-high-speed communications by utilizing several tens of GHz transmission bandwidths. However, extremely high spreading losses and other interaction losses pose significant challenges to establishing wide-area communication coverage, while human body shadowing further complicates maintaining stable communication links. Although point-to-point (P2P) fixed wireless access in the THz band has been successfully demonstrated, realizing fully mobile and reliable wireless access remains a challenge due to numerous issues to be solved for highly directional communication. To provide insights into the design of THz communication systems, this article addresses the challenges associated with THz short-range mobile access networks. It offers an overview of recent findings on the environment-dependence of multipath cluster channel properties and the impact of human body shadowing, based on measurements at 300 GHz using a double-directional high-resolution channel sounder and a motion capture-integrated channel sounder.
A Review on Deep Learning Autoencoder in the Design of Next-Generation Communication Systems
Authors: Omar Alnaseri, Laith Alzubaidi, Yassine Himeur, Jens Timmermann
Date: 2024-12-18 00:00:00

Traditional mathematical models used in designing next-generation communication systems often fall short due to inherent simplifications, narrow scope, and computational limitations. In recent years, the incorporation of deep learning (DL) methodologies into communication systems has made significant progress in system design and performance optimisation. Autoencoders (AEs) have become essential, enabling end-to-end learning that allows for the combined optimisation of transmitters and receivers. Consequently, AEs offer a data-driven methodology capable of bridging the gap between theoretical models and real-world complexities. The paper presents a comprehensive survey of the application of AEs within communication systems, with a particular focus on their architectures, associated challenges, and future directions. We examine 120 recent studies across wireless, optical, semantic, and quantum communication fields, categorising them according to transceiver design, channel modelling, digital signal processing, and computational complexity. This paper further examines the challenges encountered in the implementation of AEs, including the need for extensive training data, the risk of overfitting, and the requirement for differentiable channel models. Through data-driven approaches, AEs provide robust solutions for end-to-end system optimisation, surpassing traditional mathematical models confined by simplifying assumptions. This paper also summarises the computational complexity associated with AE-based systems by conducting an in-depth analysis employing the metric of floating-point operations per second (FLOPS). This analysis encompasses the evaluation of matrix multiplications, bias additions, and activation functions. This survey aims to establish a roadmap for future research, emphasising the transformative potential of AEs in the formulation of next-generation communication systems.
A Robust Anti-noise Scheme for RF Fingerprint Identification
Authors: Junxian Shi, Linning Peng, Wentao Jing, Lingnan Xie, Haichuan Peng, Aiqun Hu
Date: 2024-12-18 00:00:00
Tasks: Domain Adaptation

Radio frequency (RF) fingerprint technology is utilized for wireless device identification, extensively employed in the internet of things (IoT). The operating environment for IoT devices is challenging, with pervasive noise and distortion on the signals which blur the feature space of RF fingerprints. Consequently, the model accuracy obtained through training at high signal-to-noise ratio (SNR) scenarios decreases with the low SNR of the received signals in testing. To solve the noise domain adaptation problem, an anti-noise scheme is proposed to enhance identification accuracy of RF fingerprint at varying SNRs. The squared cross power spectral density (SCPSD) features are first proposed to obtain the same RF fingerprint representation. Subsequently, the specific effect of noise on SCPSD is theoretically derived and the rationality of the scheme is demonstrated through simulation experiments. Finally, 60 off-the-shelf ZigBee devices are employed to evaluate the performance of the anti-noise algorithm. The experimental results show that employing the random subspace k-nearest neighbors (RSKNN) classifier not only effectively classifies devices with multi-cluster feature, but combined with the anti-noise scheme can significantly improve the accuracy by approximately 46% for SNRs not less than 5 dB.
Exploiting Mutual Coupling Characteristics for Channel Estimation in Holographic MIMO
Authors: Nikolaos Kolomvakis, Emil Björnson
Date: 2024-12-18 00:00:00

Holographic multiple-input multiple-output (MIMO) systems represent a spatially constrained MIMO architecture with a massive number of antennas with small antenna spacing as a close approximation of a spatially continuous electromagnetic aperture. Accurate channel modeling is essential for realizing the full potential of this technology. In this paper, we investigate the impact of mutual coupling and spatial channel correlation on the estimation precision in holographic MIMO systems, as well as the importance of knowing their characteristics. We demonstrate that neglecting mutual coupling can lead to significant performance degradation for the minimum mean squared error estimator, emphasizing its critical consideration when designing estimation algorithms. Conversely, the least-squares estimator is resilient to mutual coupling but only yields good performance in high signal-to-noise ratio regimes. Our findings provide insights into how to design efficient estimation algorithms in holographic MIMO systems, aiding its practical implementation.
Parametric Channel Estimation for RIS-Assisted Wideband Systems
Authors: Alva Kosasih, Ozlem Tugfe Demir, Emil Bjornson
Date: 2024-12-18 00:00:00

A reconfigurable intelligent surface (RIS) alters the reflection of incoming signals based on the phase-shift configuration assigned to its elements. This feature can be used to improve the signal strength for user equipments (UEs), expand coverage, and enhance spectral efficiency in wideband communication systems. Having accurate channel state information (CSI) is indispensable to realize the full potential of RIS-aided wideband systems. Unfortunately, CSI is challenging to acquire due to the passive nature of the RIS elements, which cannot perform transmit/receive signal processing. Recently, a parametric maximum likelihood (ML) channel estimator has been developed and demonstrated excellent estimation accuracy. However, this estimator is designed for narrowband systems with no non-line-of-sight (NLOS) paths. In this paper, we develop a novel parametric ML channel estimator for RIS-assisted wideband systems, which can handle line-of-sight (LOS) paths in the base station (BS)-RIS and RIS-UE links as well as NLOS paths between the UE, BS, and RIS. We leverage the reduced subspace representation induced by the array geometry to suppress noise in unused dimensions, enabling accurate estimation of the NLOS paths. Our proposed algorithm demonstrates superior estimation performance for the BS-UE and RIS-UE channels, outperforming the existing ML channel estimator.
A Proximity-Based Approach for Dynamically Matching Industrial Assets and Their Operators Using Low-Power IoT Devices
Authors: Silvano Cortesi, Michele Crabolu, Prodromos-Vasileios Mekikis, Giovanni Bellusci, Christian Vogt, Michele Magno
Date: 2024-12-18 00:00:00

Asset tracking solutions have proven their significance in industrial contexts, as evidenced by their successful commercialization (e.g., Hilti On!Track). However, a seamless solution for matching assets with their users, such as operators of construction power tools, is still missing. By enabling assetuser matching, organizations gain valuable insights that can be used to optimize user health and safety, asset utilization, and maintenance. This paper introduces a novel approach to address this gap by leveraging existing Bluetooth Low Energy (BLE)-enabled low-power Internet of Things (IoT) devices. The proposed framework comprises the following components: i) a wearable device, ii) an IoT device attached to or embedded in the assets, iii) an algorithm to estimate the distance between assets and operators by exploiting simple received signal strength indicator (RSSI) measurements via an Extended Kalman Filter (EKF), and iv) a cloud-based algorithm that collects all estimated distances to derive the correct asset-operator matching. The effectiveness of the proposed system has been validated through indoor and outdoor experiments in a construction setting for identifying the operator of a power tool. A physical prototype was developed to evaluate the algorithms in a realistic setup. The results demonstrated a median accuracy of 0.49m in estimating the distance between assets and users, and up to 98.6% in correctly matching users with their assets.
Synesthesia of Machine (SoM)-Driven Analog Precoder Optimization for Enhanced ISAC Performance in Sub-THz Systems
Authors: Zonghui Yang, Shijian Gao, Xiang Cheng
Date: 2024-12-18 00:00:00
Tasks: Integrated sensing and communication, ISAC

Integrated sensing and communication (ISAC) is anticipated to be widely used in future sub-terahertz (sub-THz) systems. With the line-of-sight (LoS) propagation characteristics of sub-THz channels, ISAC transmitter design largely parallels analog precoder optimization. However, balancing both sensing and communication functionalities is challenging due to the beam squint effect in sub-THz systems, limiting ISAC performance gains. To overcome this, the unique design flexibility of sub-THz analog hardware is explored to better adapt to the electromagnetic characteristics of sub-THz channels. It is demonstrated that adjusting the equivalent channel through the analog precoder enhances dual-functional gains. Based on this, a near-optimal benchmark for analog precoder optimization is proposed. To address excessive algorithmic complexity, inspiration is drawn from the synesthesia of machine (SoM) to develop a lightweight complex-valued squint-aware network (CSP-Net). This network reduces complexity by utilizing both communication and sensing channel data, with an architecture tailored to specific data and task characteristics. The effectiveness of the proposed schemes is validated through simulations.
RIS-Aided Radar Imaging Utilizing the Virtual Source Principle
Authors: Furkan H. Ilgac, Mounia Bouabdellah, Aydin Sezgin
Date: 2024-12-17 00:00:00

This paper investigates signle-antenna radar imaging with a reconfigurable intelligent surface (RIS). Configuring phase shifts in a RIS-aided radar system can be thought as synthetic aperture radar (SAR) imaging with a moving virtual source. With this perspective, the problem is modeled in the wavenumber domain and image forming algorithms are formulated for near and far field regions. Thus, a novel and intuitive perspective has been introduced to the RIS-aided radar imaging formation problem, opening a new avenue for adapting well-known SAR methods in the literature to these systems.
Two-stage memetic algorithm for blind equalization in direct-sequence/code-division multiple-access Systems
Authors: Luis M. San-José-Revuelta, Pablo Casaseca-de-la-Higuera
Date: 2024-12-17 00:00:00

This paper proposes a novel memetic algorithm (MA) for the blind equalization of digital multiuser channels with Direct-Sequence / Code-Division Multiple-Access (DS/CDMA) sharing scheme. Equalization involves two different tasks, the estimation of: (1) channel response and (2) transmitted data. The corresponding channel model is first analyzed and then the MA is developed for this specific communication system. Convergence, population diversity and near-far resistance have been analyzed. Numerical experiments include comparative results with traditional multiuser detectors as well as with other nature-inspired approaches. Proposed receiver is proved to allow higher transmission rates over existing channels, while supporting stronger interferences as well as fading and time-variant effects. Required computation requisites are kept moderate in most cases. Proposed MA saves approximately 80% of computation time with respect to a standard genetic algorithm and about 15% with respect to a similar two-stage memetic algorithm, while keeping a statistically significant higher performance. Besides, complexity increases only by a factor of 5, when the number of active users doubles, instead of 32x found for the optimum maximum likelihood algorithm. The proposed method also exhibits high near-far resistance and achieves accurate channel response estimates, becoming an interesting and viable alternative to so far proposed methods.
Compressed Sensing Based Residual Recovery Algorithms and Hardware for Modulo Sampling
Authors: Shaik Basheeruddin Shah, Satish Mulleti, Yonina C. Eldar
Date: 2024-12-17 00:00:00
Tasks: compressed sensing, Computational Efficiency

Analog-to-Digital Converters (ADCs) are essential components in modern data acquisition systems. A key design challenge is accommodating high dynamic range (DR) input signals without clipping. Existing solutions, such as oversampling, automatic gain control (AGC), and compander-based methods, have limitations in handling high-DR signals. Recently, the Unlimited Sampling Framework (USF) has emerged as a promising alternative. It uses a non-linear modulo operator to map high-DR signals within the ADC range. Existing recovery algorithms, such as higher-order differences (HODs), prediction-based methods, and beyond bandwidth residual recovery (B2R2), have shown potential but are either noise-sensitive, require high sampling rates, or are computationally intensive. To address these challenges, we propose LASSO-B2R2, a fast and robust recovery algorithm. Specifically, we demonstrate that the first-order difference of the residual (the difference between the folded and original samples) is sparse, and we derive an upper bound on its sparsity. This insight allows us to formulate the recovery as a sparse signal reconstruction problem using the least absolute shrinkage and selection operator (LASSO). Numerical simulations show that LASSO-B2R2 outperforms prior methods in terms of speed and robustness, though it requires a higher sampling rate at lower DR. To overcome this, we introduce the bits distribution mechanism, which allocates 1 bit from the total bit budget to identify modulo folding events. This reduces the recovery problem to a simple pseudo-inverse computation, significantly enhancing computational efficiency. Finally, we validate our approach through numerical simulations and a hardware prototype that captures 1-bit folding information, demonstrating its practical feasibility.
Bit Efficient Toeplitz Covariance Estimation
Authors: Hongwei Xu, Zai Yang
Date: 2024-12-17 00:00:00

This paper addresses the challenge of Toeplitz covariance matrix estimation from partial entries of random quantized samples. To balance trade-offs among the number of samples, the number of entries observed per sample, and the data resolution, we propose a ruler-based quantized Toeplitz covariance estimator. We derive non-asymptotic error bounds and analyze the convergence rates of the proposed estimator. Our results show that the estimator is near-optimal and imply that reducing data resolution within a certain range has a limited impact on the estimation accuracy. Numerical experiments are provided that validate our theoretical findings and show effectiveness of the proposed estimator.
A Simplified Algorithm for Joint Real-Time Synchronization, NLoS Identification, and Multi-Agent Localization
Authors: Yili Deng, Jie Fan, Jiguang He, Baojia Luo, Miaomiao Dong, Zhongyi Huang
Date: 2024-12-17 00:00:00

Real-time, high-precision localization in large-scale wireless networks faces two primary challenges: clock offsets caused by network asynchrony and non-line-of-sight (NLoS) conditions. To tackle these challenges, we propose a low-complexity real-time algorithm for joint synchronization and NLoS identification-based localization. For precise synchronization, we resolve clock offsets based on accumulated time-of-arrival measurements from all the past time instances, modeling it as a large-scale linear least squares (LLS) problem. To alleviate the high computational burden of solving this LLS, we introduce the blockwise recursive Moore-Penrose inverse (BRMP) technique, a generalized recursive least squares approach, and derive a simplified formulation of BRMP tailored specifically for the real-time synchronization problem. Furthermore, we formulate joint NLoS identification and localization as a robust least squares regression (RLSR) problem and address it by using an efficient iterative approach. Simulations show that the proposed algorithm achieves sub-nanosecond synchronization accuracy and centimeter-level localization precision, while maintaining low computational overhead.
A Measurement-Based Spatially Consistent Channel Model for Distributed MIMO in Industrial Environments
Authors: Christian Nelson, Sara Willhammar, Fredrik Tufvesson
Date: 2024-12-17 00:00:00

Future wireless communication systems are envisioned to support ultra-reliable and low-latency communication (URLLC), which will enable new applications such as compute offloading, wireless real-time control, and reliable monitoring. Distributed multiple-input multiple-output (D-MIMO) is one of the most promising technologies for delivering URLLC. This paper classifies obstruction and derives a channel model from a D-MIMO measurement campaign carried out at a carrier frequency of 3.75 GHz with a bandwidth of 35 MHz using twelve distributed fully coherent dipole antennas in an industrial environment. Channel characteristics are investigated, including statistical measures such as small-scale fading, large-scale fading, delay spread, and transition rates between line-of-sight and obstructed line-of-sight conditions for the different antenna elements, laying the foundations for an accurate channel model for D-MIMO systems in industrial environments. Furthermore, correlations of large-scale fading between antennas, spatial correlation, and tail distributions are included to enable proper evaluations of reliability and rare events. Based on the results, a channel model for D-MIMO in industrial environments is presented together with a recipe for its implementation.
A Dipole Antenna with a Dynamic Balun for Wireless Security via Amplitude Based Directional Modulation
Authors: Amer Abu Arisheh, Jeffrey A. Nanzer
Date: 2024-12-17 00:00:00

We present a new approach to secure wireless operations using a simple dipole antenna with a dynamic unbalanced feeding structure. By rapidly switching between two states, a dynamic radiation pattern is generated, resulting in directional modulation. The current distribution on the arms of the dipole antenna are made asymmetric by the balun, which changes the relative phase between the feed currents. By rapidly switching between two mirrored states, the relative phase shift changes sign, causing the current distribution to manifest asymmetrically on the arms of the dipole antenna. The resultant far field radiation pattern is therefore asymmetric in both states, but mirrored between the two states. Rapid switching between the two states results in a far-field pattern that is dynamic in amplitude at all angles except for a narrow region of space, which is referred to as the information beam. The dynamic radiation pattern causes additional modulation on any transmitted or received signals, thereby obscuring the information at all angles outside the information beam. The proposed directional modulation technique is separate from both the antenna and the rest of the wireless system, and can thus be implemented in a black box form in wireless communications or sensing systems. We demonstrate the concept in a 1.86 GHz printed dipole antenna, demonstrating the transmission of 256-QAM signals.
Tensor-Based Receivers for the Bistatic Sensing and Communication Scenario
Authors: Walter da C. Freitas Jr., Gerard Favier, Andre L. F. de Almeida
Date: 2024-12-17 00:00:00

We propose receivers for bistatic sensing and communication that exploit a tensor modeling of the received signals. We consider a hybrid scenario where the sensing link knows the transmitted data to estimate the target parameters while the communication link operates semi-blindly in a direct data decoding approach without channel knowledge. We show that the signals received at the sensing receiver and communication receiver follow PARATUCK and PARAFAC tensor models, respectively. These models are exploited to obtain accurate estimates of the target parameters (at the sensing receiver) and the transmitted symbols and channels (at the user equipment). We discuss uniqueness conditions and provide some simulation results to evaluate the performance of the proposed receivers. Our experiments show that the sensing parameters are well estimated at moderate signal-to-noise ratio (SNR) while keeping good symbol error rate (SER) and channel normalized mean square error (NMSE) results for the communication link.
On the Time-Frequency Localization Characteristics of the Delay-Doppler Plane Orthogonal Pulse
Authors: Akram Shafie, Jinhong Yuan, Nan Yang, Hai Lin
Date: 2024-12-14 00:00:00

In this work, we study the time-frequency (TF) localization characteristics of the prototype pulse of orthogonal delay-Doppler (DD) division multiplexing modulation, namely, the DD plane orthogonal pulse (DDOP). The TF localization characteristics examine how concentrated or spread out the energy of a pulse is in the joint TF domain, the time domain (TD), and the frequency domain (FD). We first derive the TF localization metrics of the DDOP, including its TF area, its time and frequency dispersions, and its direction parameter. Based on these results, we demonstrate that the DDOP exhibits a high energy spread in the TD, FD, and the joint TF domain, while adhering to the Heisenberg uncertainty principle. Thereafter, we discuss the potential advantages brought by the energy spread of the DDOP, especially with regard to harnessing both time and frequency diversities and enabling fine-resolution sensing. Subsequently, we examine the relationships between the time and frequency dispersions of the DDOP and those of the envelope functions of DDOP's TD and FD representations, paving the way for simplified determination of the TF localization metrics for more generalized variants of the DDOP and the pulses used in other DD domain modulation schemes. Finally, using numerical results, we validate our analysis and find further insights.