pwc-archive/papers-with-abstracts huggingface dataset:
Efficient Channel Estimation for Millimeter Wave and Terahertz Systems Enabled by Integrated Super-resolution Sensing and Communication
Authors: Jingran Xu, Huizhi Wang, Yong Zeng, Xiaoli Xu, Qingqing Wu, Fei Yang, Yan Chen, Abbas Jamalipour
Date: 2024-07-30 00:00:00
Tasks: Benchmarking, Super-Resolution

Integrated super-resolution sensing and communication (ISSAC) has emerged as a promising technology to achieve extremely high precision sensing for those key parameters, such as the angles of the sensing targets. In this paper, we propose an efficient channel estimation scheme enabled by ISSAC for millimeter wave (mmWave) and TeraHertz (THz) systems with a hybrid analog/digital beamforming architecture, where both the pilot overhead and the cost of radio frequency (RF) chains are significantly reduced. The key idea is to exploit the fact that subspace-based super-resolution algorithms such as multiple signal classification (MUSIC) can estimate channel parameters accurately without requiring dedicate a priori known pilots. In particular, the proposed method consists of two stages. First, the angles of the multi-path channel components are estimated in a pilot-free manner during the transmission of data symbols. Second, the multi-path channel coefficients are estimated with very few pilots. Compared to conventional channel estimation schemes that rely solely on channel training, our approach requires the estimation of much fewer parameters in the second stage. Furthermore, with channel multi-path angles obtained, the beamforming gain can be achieved when pilots are sent to estimate the channel path gains. To comprehensively investigate the performance of the proposed scheme, we consider both the basic line-of-sight (LoS) channels and more general multi-path channels. We compare the performance of the minimum mean square error (MMSE) of channel estimation and the resulting beamforming gains of our proposed scheme with the traditional scheme that rely exclusively on channel training. It is demonstrated that our proposed method significantly outperforms the benchmarking scheme. Simulation results are presented to validate our theoretical findings.
RIP sensing matrices construction for sparsifying dictionaries with application to MRI imaging
Authors: Jinn Ho, Wen-Liang Hwang, Andreas Heinecke
Date: 2024-07-30 00:00:00
Tasks: compressed sensing

Practical applications of compressed sensing often restrict the choice of its two main ingredients. They may (i) prescribe using particular redundant dictionaries for certain classes of signals to become sparsely represented, or (ii) dictate specific measurement mechanisms which exploit certain physical principles. On the problem of RIP measurement matrix design in compressed sensing with redundant dictionaries, we give a simple construction to derive sensing matrices whose compositions with a prescribed dictionary have a high probability of the RIP in the $k \log(n/k)$ regime. Our construction thus provides recovery guarantees usually only attainable for sensing matrices from random ensembles with sparsifying orthonormal bases. Moreover, we use the dictionary factorization idea that our construction rests on in the application of magnetic resonance imaging, in which also the sensing matrix is prescribed by quantum mechanical principles. We propose a recovery algorithm based on transforming the acquired measurements such that the compressed sensing theory for RIP embeddings can be utilized to recover wavelet coefficients of the target image, and show its performance on examples from the fastMRI dataset.
A Constrained Optimization approach for Ultrasound Shear Wave Speed Estimation with Time-Lateral Plane Cleaning
Authors: Md. Jahin Alam, Md. Kamrul Hasan
Date: 2024-07-30 00:00:00
Tasks: Denoising, Medical Diagnosis

Ultrasound shear wave elastography (SWE) is a noninvasive way to measure stiffness of soft tissue for medical diagnosis. In SWE imaging, an acoustic radiation force induces tissue displacement, which creates shear waves (SWs) that travel laterally through the medium. Finding the lateral arrival times of SWs at different tissue locations helps figure out the shear wave speed (SWS), which is directly linked to the stiffness of the medium. Traditional SWS estimation techniques, however, are not noise resilient enough handling noise and reflection artifacts filled data. This paper proposes new techniques to estimate SWS in both time and frequency domains. These new methods optimize a loss function that is based on the lateral signal shift parameter between known locations and is constrained by neighborhood displacement group shift determined from the time-lateral plane-denoised SW propagation data. The proposed constrained optimization is formed by coupling losses of local particles with a Gaussian kernel giving an optimum arrival time for the center particle by enforcing stiffness homogeneity in a small neighborhood to enable inherent noise resilience. The denoising scheme involves isolating the transitioning SW profile in each time-lateral plane, creating a parameterized mask. Moreover, lateral interpolation is performed to enhance reconstruction resolution and obtain increased displacement groups to enhance the reliability of the optimization. The proposed noise robust scheme is tested on a simulation and three experimental datasets. The performance of the method is compared with 3 ToF and 2 frequency-domain methods. The evaluations show visually and quantitatively superior and noise-robust reconstructions compared to state-of-the-art methods. Due to its high contrast and minimal error, the proposed technique can find its application in tissue health inspection and cancer diagnosis.
Single-BS Simultaneous Environment Sensing and UE Localization without LoS Path by Exploiting Near-Field Scatterers
Authors: Zhiwen Zhou, Zhiqiang Xiao, Yong Zeng
Date: 2024-07-30 00:00:00

As the mobile communication network evolves over the past few decades, localizing user equipment (UE) has become an important network service. While localization in line-of-sight (LoS) scenarios has reached a level of maturity, it is known that in far-field scenarios without a LoS path nor any prior information about the scatterers, accurately localizing the UE is impossible. In this letter, we show that this becomes possible if there are scatterers in the near-field region of the base station (BS) antenna arrays. Specifically, by exploiting the additional distance sensing capability of extremely large-scale antenna arrays (XL-arrays) provided by near-field effects, we propose a novel method that simultaneously performs environment sensing and non-line-of-sight (NLoS) UE localization using one single BS. In the proposed method, the BS leverages the near-field characteristics of XL-arrays to directly estimate the locations of the near-field scatterers with array signal processing, which then serves as virtual anchors for UE localization. Then, the propagation delay for each path is estimated and the position of the UE is obtained based on the positions of scatterers and the path delays. Simulation results demonstrate that the proposed method achieves superior accuracy and robustness with similar complexity compared with benchmark methods.
Channel Estimation for Beyond Diagonal RIS via Tensor Decomposition
Authors: André L. F. de Almeida, Bruno Sokal, Hongyu Li, Bruno Clerckx
Date: 2024-07-29 00:00:00
Tasks: Tensor Decomposition

This paper addresses the channel estimation problem for beyond diagonal reconfigurable intelligent surface (BD-RIS) from a tensor decomposition perspective. We first show that the received pilot signals can be arranged as a three-way tensor, allowing us to recast the cascaded channel estimation problem as a block Tucker decomposition problem that yields decoupled estimates for the involved channel matrices while offering a substantial performance gain over the conventional (matrix-based) least squares (LS) estimation method. More specifically, we develop two solutions to solve the problem. The first one is a closed-form solution that extracts the channel estimates via a block Tucker Kronecker factorization (BTKF), which boils down to solving a set of parallel rank-one matrix approximation problems. Exploiting such a low-rank property yields a noise rejection gain compared to the standard LS estimation scheme while allowing the two involved channels to be estimated separately. The second solution is based on a block Tucker alternating least squares (BTALS) algorithm that directly estimates the involved channel matrices using an iterative estimation procedure. We discuss the uniqueness and identifiability issues and their implications for training design. We also propose a tensor-based design of the BD-RIS training tensor for each algorithm that ensures unique decoupled channel estimates under trivial scaling ambiguities. Our numerical results shed light on the tradeoffs offered by BTKF and BTALS methods. Specifically, while the first enjoys fast and parallel extraction of the channel estimates in closed form, the second has a more flexible training design, allowing for a significantly reduced training overhead compared to the state-of-the-art LS method.
Trade-offs in Reliability and Performance Using Selective Beamforming for Ultra-Massive MIMO
Authors: Anis Hamadouche, Mathini Sellathurai
Date: 2024-07-29 00:00:00
Tasks: Fairness

This paper addresses the optimization challenges in Ultra-Massive MIMO communication systems, focusing on array selection and beamforming in dynamic and diverse operational contexts. We introduce a novel array selection criterion that incorporates antenna health information into the optimization process, distinguishing our approach from traditional methods. Our methodology employs dual proximal-gradient ascent to effectively tackle the constrained non-convex and non-smooth nature of sparse array selection problems. A central feature of our strategy is the implementation of proportional fairness among communication users, aligning with system resource limitations while ensuring minimum rate requirements for all users. This approach not only enhances system efficiency and responsiveness but also ensures equitable resource distribution. Extensive simulations validate the effectiveness of the proposed solutions in optimizing Ultra-Massive MIMO system performance, demonstrating their applicability in complex communication scenarios. Our findings reveal key trade-offs influenced by the sparsity promotion weight (\(\gamma\)). As \(\gamma\) increases, spectral efficiency (SE) and communication rate (Ri) decrease, while beamforming matrix density (BMD) reduces and antenna reliability (RL) significantly improves. These results highlight the critical balance between performance and reliability, essential for the practical deployment of Ultra-Massive MIMO systems. This work advances the field by providing innovative solutions and new insights into array selection and beamforming optimization, setting a foundation for future research in Ultra-Massive MIMO communication systems.
Context-Enhanced CSI Tracking Using Koopman-Inspired Dual Autoencoders in Dynamic Wireless Environments
Authors: Anis Hamadouche, Mathini Sellathurai
Date: 2024-07-29 00:00:00
Tasks: Computational Efficiency, Privacy Preserving

This paper introduces a novel framework for tracking and predicting Channel State Information (CSI) by leveraging Physics-Informed Autoencoders (PIAE) integrated with a learned Koopman operator. The proposed approach models CSI as a nonlinear dynamical system governed by both intrinsic channel behavior and exogenous contextual factors such as position, temperature, and atmospheric conditions. The architecture comprises dual autoencoders-one dedicated to CSI and another to contextual inputs-linked via a shared latent state space, within which the Koopman operator captures the linear temporal evolution of CSI dynamics. This coupling enables accurate, data-driven forecasting of CSI trajectories while maintaining interpretability through a structured, physics-consistent representation. The framework supports real-time updates to the Channel Knowledge Map (CKM), enhancing the adaptability and reliability of communication systems in complex and time-varying environments. By unifying Koopman theory with learned latent representations, the proposed method provides a scalable and privacy-preserving solution for next-generation wireless networks. Empirical results demonstrate its effectiveness in delivering high-fidelity CSI predictions under diverse channel conditions.
Orbital Angular Momentum Active Anti-Jamming in Radio Wireless Communications
Authors: Kexin Zheng, Wenchi Cheng, Liping Liang
Date: 2024-07-29 00:00:00

Orbital angular momentum (OAM), providing the orthogonality among different OAM modes, has attracted much attention to significantly increase spectrum efficiencies (SEs) and enhance the anti-jamming results of wireless communications. However, the SE of wireless communications is severely degraded under co-frequency and co-mode hostile jamming. Focused on this issue, we propose a novel OAM active anti-jamming scheme to significantly enhance the anti-jamming results of wireless communications under broadband hostile jamming. Specifically, the OAM transmitter with energy detection senses jamming signals to identify which OAM modes are jammed and unjammed. Based on the recognition of OAM modes, useful signals are modulated by reflecting the received co-frequency and co-mode jamming signals with the assistance of a programmable gain amplifier (PGA) to the OAM receiver, thus utilizing both the OAM modes jammed by hostile attacks and the energy of jamming signals. Meanwhile, the unjammed OAM modes allocated with total transmit power are multiplexed for useful signal transmission. Numerical results demonstrate that our proposed OAM active anti-jamming scheme can achieve high OAM mode utilization and significantly increase the SEs.
Sustainable Task Offloading in Secure UAV-assisted Smart Farm Networks: A Multi-Agent DRL with Action Mask Approach
Authors: Tingnan Bao, Aisha Syed, William Sean Kennedy, Melike Erol-Kantarci
Date: 2024-07-29 00:00:00
Tasks: Deep Reinforcement Learning, Edge-computing, Management

The integration of unmanned aerial vehicles (UAVs) with mobile edge computing (MEC) and Internet of Things (IoT) technology in smart farms is pivotal for efficient resource management and enhanced agricultural productivity sustainably. This paper addresses the critical need for optimizing task offloading in secure UAV-assisted smart farm networks, aiming to reduce total delay and energy consumption while maintaining robust security in data communications. We propose a multi-agent deep reinforcement learning (DRL)-based approach using a deep double Q-network (DDQN) with an action mask (AM), designed to manage task offloading dynamically and efficiently. The simulation results demonstrate the superior performance of our method in managing task offloading, highlighting significant improvements in operational efficiency by reducing delay and energy consumption. This aligns with the goal of developing sustainable and energy-efficient solutions for next-generation network infrastructures, making our approach an advanced solution for achieving both performance and sustainability in smart farming applications.
Near-Field Localization with an Exact Propagation Model in Presence of Mutual Coupling
Authors: Zohreh Ebadi, Amir Masoud Molaei, Muhammad Ali Babar Abbasi, Simon Cotton, Anvar Tukmanov, Okan Yurduseven
Date: 2024-07-28 00:00:00

Localizing near-field sources considering practical arrays is important in wireless communications. Array-based apertures exhibit mutual coupling between the array elements, which can significantly degrade the performance of the localization method. In this paper, we propose two methods to localize near-field sources by direction of arrival (DOA) and range estimations in the presence of mutual coupling. The first method utilizes a two-dimensional search to estimate DOA and the range of the source. Therefore, it suffers from a high computational load. The second method reduces the two-dimensional search to one-dimensional, thus decreasing the computational complexity while offering similar DOA and range estimation performance. Besides, our second method reduces computational time by over 50% compared to the multiple signal classification (MUSIC) algorithm.
Pixel-Level GPS Localization and Denoising using Computer Vision and 6G Communication Beams
Authors: Gouranga Charan, Tawfik Osman, Ahmed Alkhateeb
Date: 2024-07-28 00:00:00
Tasks: Autonomous Vehicles, Denoising

Accurate localization is crucial for various applications, including autonomous vehicles and next-generation wireless networks. However, the reliability and precision of Global Navigation Satellite Systems (GNSS), such as the Global Positioning System (GPS), are compromised by multi-path errors and non-line-of-sight scenarios. This paper presents a novel approach to enhance GPS accuracy by combining visual data from RGB cameras with wireless signals captured at millimeter-wave (mmWave) and sub-terahertz (sub-THz) basestations. We propose a sensing-aided framework for (i) site-specific GPS data characterization and (ii) GPS position de-noising that utilizes multi-modal visual and wireless information. Our approach is validated in a realistic Vehicle-to-Infrastructure (V2I) scenario using a comprehensive real-world dataset, demonstrating a substantial reduction in localization error to sub-meter levels. This method represents a significant advancement in achieving precise localization, particularly beneficial for high-mobility applications in 5G and beyond networks.
Innovative RIS Prototyping Enhancing Wireless Communication with Real-Time Spot Beam Tracking and OAM Wavefront Manipulation
Authors: Yufei Zhao, Yuan Feng, Afkar Mohamed Ismail, Ziyue Wang, Yong Liang Guan, Yongxin Guo, Chau Yuen
Date: 2024-07-28 00:00:00
Tasks: Blocking

This paper presents a sophisticated reconfigurable metasurface architecture that introduces an advanced concept of flexible full-array space-time wavefront manipulation with enhanced dynamic capabilities. The practical 2-bit phase-shifting unit cell on the RIS is distinguished by its ability to maintain four stable phase states, each with ${90^ \circ }$ differences, and features an insertion loss of less than 0.6 dB across a bandwidth of 200 MHz. All reconfigurable units are equipped with meticulously designed control circuits, governed by an intelligent core composed of multiple Micro-Controller Units (MCUs), enabling rapid control response across the entire RIS array. Owing to the capability of each unit cell on the metasurface to independently switch states, the entire RIS is not limited to controlling general beams with specific directional patterns, but also generates beams with more complex structures, including multi-focus 3D spot beams and vortex beams. This development substantially broadens its applicability across various industrial wireless transmission scenarios. Moreover, by leveraging the rapid-respond space-time coding and the full-array independent programmability of the RIS prototyping operating at 10.7 GHz, we have demonstrated that: 1) The implementation of 3D spot beams scanning facilitates dynamic beam tracking and real-time communication under the indoor near-field scenario; 2) The rapid wavefront rotation of vortex beams enables precise modulation of signals within the Doppler domain, showcasing an innovative approach to wireless signal manipulation; 3) The beam steering experiments for blocking users under outdoor far-field scenarios, verifying the beamforming capability of the RIS board.
Exploiting Target Location Distribution in MIMO Radar: PCRB vs. PSBP for Waveform Design
Authors: Lingyun Xu, Bowen Wang, Huiyong Li, Ziyang Cheng
Date: 2024-07-27 00:00:00
Tasks: Radar waveform design

This paper investigates the issue of how to exploit prior target location distribution for multiple input multiple output (MIMO) radar waveform design. We consider a MIMO radar aiming to estimate the random angular location parameters of a point target, whose prior distribution information can be exploited by the radar. First, we establish the models of the MIMO radar system and the target location distribution. Based on the considered models, we propose the first category of target location distribution exploitation methods by analyzing the radar direction-of-angle (DoA) estimation performance and deriving a general form of posterior Cramer-Rao bound (PCRB) as the lower bound of the mean square error of DoA estimation. Following this, to investigate the potential of leveraging prior information from two distinct perspectives, thereby providing deeper insights into the estimation process, we proposed the second category of target location distribution exploitation methods by introducing a novel radar metric, probability scaled beampattern (PSBP), from the perspective of radar beampattern. To compare the two methods, we formulate the PCRB and PSBP oriented radar waveform design problems and propose corresponding low-complexity and convergence-guaranteed algorithms to tackle them. Finally, numerical simulations are conducted in different scenarios to provide a comprehensive evaluation and comparison of the radar performance.
Guiding Wireless Signals with Arrays of Metallic Linear Fresnel Reflectors: A Low-cost, Frequency-versatile, and Practical Approach
Authors: Hieu Le, Oguz Bedir, Jian Tao, Sabit Ekin, Mostafa Ibrahim
Date: 2024-07-27 00:00:00

This study presents a novel mechanical metallic reflector array to guide wireless signals to the point of interest, thereby enhancing received signal quality. Comprised of numerous individual units, this device, which acts as a linear Fresnel reflector (LFR), facilitates the reflection of incoming signals to a desired location. Leveraging geometric principles, we present a systematic approach for redirecting beams from an Access Point (AP) toward User Equipment (UE) positions. This methodology is geared towards optimizing beam allocation, thereby maximizing the number of beams directed towards the UE. Ray tracing simulations conducted for two 3D wireless communication scenarios demonstrate significant increases in path gains and received signal strengths (RSS) by at least 50dB with strategically positioned devices.
OFDM-Based Positioning with Unknown Data Payloads: Bounds and Applications to LEO PNT
Authors: Andrew M. Graff, Todd E. Humphreys
Date: 2024-07-26 00:00:00

This paper presents bounds, estimators, and signal design strategies for exploiting both known pilot resources and unknown data payload resources in time-of-arrival (TOA)-based positioning systems with orthogonal frequency-division multiplexing (OFDM) signals. It is the first to derive the Ziv-Zakai bound (ZZB) on TOA estimation for OFDM signals containing both known pilot and unknown data resources. In comparison to the Cramer-Rao bounds (CRBs) derived in prior work, this ZZB captures the low-signal-to-noise ratio (SNR) thresholding effects in TOA estimation and accounts for an unknown carrier phase. The derived ZZB is evaluated against CRBs and empirical TOA error variances. It is then evaluated on signals with resource allocations optimized for pilot-only TOA estimation, quantifying the performance gain over the best-case pilot-only signal designs. Finally, the positioning accuracy of maximum-likelihood and decision-directed estimators is evaluated on simulated low-Earth-orbit non-terrestrial-network channels and compared against their respective ZZBs.