pwc-archive/papers-with-abstracts huggingface dataset:
Near-Field LoS/NLoS Channel Estimation for RIS-Aided MU-MIMO Systems: Piece-Wise Low-Rank Approximation Approach
Authors: Jeongjae Lee, SongNam Hong
Date: 2024-10-17 00:00:00

We study the channel estimation problem for a reconfigurable intelligent surface (RIS)-assisted millimeter-wave (mmWave) multi-user multiple-input multiple-output (MU-MIMO) system. In particular, it is assumed that the channel between a RIS and a base station (BS) exhibits a near-field line-of-sight (LoS) channel, which is a dominant signal path in mmWave communication systems. Due to the high-rankness and non-sparsity of the RIS-BS channel matrix in our system, the state-of-the-art (SOTA) methods, which are constructed based on far-field or near-field non-LoS (NLoS) channel, cannot provide attractive estimation performances. We for the first time propose an efficient near-field LoS/NLoS channel estimation method for RIS-assisted MU-MIMO systems by means of a piece-wise low-rank approximation. Specifically, an effective channel (to be estimated) is partitioned into piece-wise effective channels containing low-rank structures and then, they are estimated via collaborative low-rank approximation. The proposed method is named PW-CLRA. Via simulations, we verify the effectiveness of the proposed PW-CLRA.
Maximal Transmission Rate in Omni-DRIS-Assisted Indoor Visible Light Communication Systems
Authors: Alain R. Ndjiongue, Octavia A. Dobre, Hyundong Shin
Date: 2024-10-17 00:00:00

Given the importance of reconfigurable intelligent surfaces (RISs) in next-generation mobile systems, several RIS variants have been proposed in recent years. Omni-digital-RIS (omni-DRIS) is one of the newly introduced variants of optical RIS that can successfully be driven by bit sequences to control lights emerging from simultaneous reflection and refraction processes, impacting both the achievable rate and the required number of omni-DRIS elements. In this paper, we analyze the effects of omni-DRIS-assisted transmission environment parameters to maximize the achievable rate and highlight the corresponding number of omni-DRIS elements. Furthermore, we show that the number of omni-DRIS elements that yields the highest achievable rate largely depends on the number of bits per omni-DRIS control sequence. On the other hand, this rate is determined by the remaining parameters of the transmission system and environmental factors, which include the total transmit power, transmission bandwidth, number of transmitters and users, and the channel DC gain.
AI-based 3-Lead to 12-Lead ECG Reconstruction: Towards Smartphone-based Public Healthcare
Authors: Aditya Mallick, Rahul L R, Albert Shaiju, Satya Deepika Neelapala, Lopamudra Giri, Rahuldeb Sarkar, Soumya Jana
Date: 2024-10-17 00:00:00
Tasks: Generative Adversarial Network

Clinicians generally diagnose cardiovascular diseases (CVDs) using standard 12-Lead electrocardiogram (ECG). However, for smartphone-based public healthcare systems, a reduced 3-lead system may be preferred because of (i) increased portability, and (ii) reduced requirement for power, storage and bandwidth. Subsequently, clinicians require accurate 3-lead to 12-Lead ECG reconstruction, which has so far been studied only in the personalized setting. When each device is dedicated to one individual, artificial intelligence (AI) methods such as temporal long short-term memory (LSTM) and a further improved spatio-temporal LSTM-UNet combine have proven effective. In contrast, in the current smartphone-based public health setting where a common device is shared by many, developing an AI lead-reconstruction model that caters to the extensive ECG signal variability in the general population appears a far greater challenge. In this direction, we take a first step, and observe that the performance improvement achieved by a generative model, specifically, 1D Pix2Pix GAN (generative adversarial network), over LSTM-UNet is encouraging.
Improving the Estimation of Attenuation in Q/V Band Systems with a Kalman-Based Scintillation Filter
Authors: Justin Cano, Julien Queyrel, Laurent Castanet, Michel Bousquet
Date: 2024-10-17 00:00:00
Tasks: Diversity

This paper presents the design and implementation of the Scintillation Filter by Kalman-colored algorithm (SciFi), which is used to remove tropospheric scintillation from Q/V bands total attenuation data series. In contrast to the classical methods using low-pass filters, the SciFi algorithm allows to estimate both the attenuation, its slope and a confidence interval. Moreover, the linear observer structure of the Kalman filter allows it to operate in real time. Therefore, the states and uncertainties estimated by SciFi can be used as input for Fade Mitigation Techniques (FMT) such as Adaptive Coding and Modulation (ACM) or Site Diversity (SD). In this article, we propose a method to tune the noise level based on recommendations approved by the ITU-R. Finally, some results of filtering on Alphasat experimental data are discussed.
Joint Antenna Selection and Covariance Matrix Optimization for ISAC Systems
Authors: Michail Palaiologos, Mario H. Castãneda García, Tobias Laas, Richard A. Stirling-Gallacher, Giuseppe Caire
Date: 2024-10-17 00:00:00
Tasks: Integrated sensing and communication, ISAC

We consider an integrated sensing and communication (ISAC) system with a single communication user and multiple targets. For the communication functionality, the achievable rate is employed as the performance metric, while for sensing, we focus on minimizing the mean squared error (MSE) between the designed beampattern and a desired one for tracking the targets. Towards this, and by assuming that there are fewer radiofrequency (RF) chains than antenna elements at the transmitter (Tx), we focus on the joint antenna selection (AS) and covariance matrix (CM) optimization at the Tx. This is a mixed-integer optimization problem, yet we demonstrate that it can be efficiently solved, in polynomial time, by combining convex optimization tools with dynamic programming (DP). By introducing an adjustable trade-off parameter, we formulate a joint objective function that captures both the communication and sensing metric. In this way, different ISAC solutions can be obtained, considering the trade-off among the two functionalities. It is shown that selecting the active antennas with our proposed method is superior than assuming a uniform Tx array with fixed antenna positions. Notably, by individually considering the optimization of either the sensing or the communication system alone, our proposed algorithm outperforms the literature proposals, by incurring only a small increase in complexity.
Multi-frame Detection via Graph Neural Networks: A Link Prediction Approach
Authors: ZhiHao Lin, Chang Gao, Junkun Yan, Qingfu Zhang, Hongwei Liu
Date: 2024-10-17 00:00:00
Tasks: Link Prediction, Node Classification

Multi-frame detection algorithms can effectively utilize the correlation between consecutive echoes to improve the detection performance of weak targets. Existing efficient multi-frame detection algorithms are typically based on three sequential steps: plot extraction via a relative low primary threshold, track search and track detection. However, these three-stage processing algorithms may result in a notable loss of detection performance and do not fully leverage the available echo information across frames. As to applying graph neural networks in multi-frame detection, the algorithms are primarily based on node classification tasks, which cannot directly output target tracks. In this paper, we reformulate the multi-frame detection problem as a link prediction task in graphs. First, we perform a rough association of multi-frame observations that exceed the low threshold to construct observation association graphs. Subsequently, a multi-feature link prediction network is designed based on graph neural networks, which integrates multi-dimensional information, including echo structure, Doppler information, and spatio-temporal coupling of plots. By leveraging the principle of link prediction, we unifies the processes of track search and track detection into one step to reduce performance loss and directly output target tracks. Experimental results indicate that, compared with traditional single-frame and multi-frame detection algorithms, the proposed algorithm improves the detection performance of weak targets while suppressing false alarms. Additionally, interpretable analysis shows that the designed network effectively integrates the utilized features, allowing for accurate associations between targets and false alarms.
ChannelGPT: A Large Model to Generate Digital Twin Channel for 6G Environment Intelligence
Authors: Li Yu, Lianzheng Shi, Jianhua Zhang, Jialin Wang, Zhen Zhang, Yuxiang Zhang, Guangyi Liu
Date: 2024-10-17 00:00:00

6G is envisaged to provide multimodal sensing, pervasive intelligence, global coverage, global coverage, etc., which poses extreme intricacy and new challenges to the network design and optimization. As the core part of 6G, wireless channel is the carrier and enabler for the flourishing technologies and novel services, which intrinsically determines the ultimate system performance. However, how to describe and utilize the complicated and high-dynamic characteristics of wireless channel accurately and effectively still remains great hallenges. To tackle this, digital twin is envisioned as a powerful technology to migrate the physical entities to virtual and computational world. In this article, we propose a large model driven digital twin channel generator (ChannelGPT) embedded with environment intelligence (EI) to enable pervasive intelligence paradigm for 6G network. EI is an iterative and interactive procedure to boost the system performance with online environment adaptivity. Firstly, ChannelGPT is capable of utilization the multimodal data from wireless channel and corresponding physical environment with the equipped sensing ability. Then, based on the fine-tuned large model, ChannelGPT can generate multi-scenario channel parameters, associated map information and wireless knowledge simultaneously, in terms of each task requirement. Furthermore, with the support of online multidimensional channel and environment information, the network entity will make accurate and immediate decisions for each 6G system layer. In practice, we also establish a ChannelGPT prototype to generate high-fidelity channel data for varied scenarios to validate the accuracy and generalization ability based on environment intelligence.
On the Sensing Performance of OFDM-based ISAC under the Influence of Oscillator Phase Noise
Authors: Lucas Giroto de Oliveira, Yueheng Li, Benedikt Geiger, Laurent Schmalen, Thomas Zwick, Benjamin Nuss
Date: 2024-10-17 00:00:00
Tasks: Integrated sensing and communication, ISAC

Integrated sensing and communication (ISAC) is a novel capability expected for sixth generation (6G) cellular networks. To that end, several challenges must be addressed to enable both mono- and bistatic sensing in existing deployments. A common impairment in both architectures is oscillator phase noise (PN), which not only degrades communication performance, but also severely impairs radar sensing. To enable a broader understanding of orthogonal-frequency division multiplexing (OFDM)-based sensing impaired by PN, this article presents an analysis of sensing peformance in OFDM-based ISAC for different waveform parameter choices and settings in both mono- and bistatic architectures. In this context, the distortion of the adopted digital constellation modulation is analyzed and the resulting PN-induced effects in range-Doppler radar images are investigated both without and with PN compensation. These effects include peak power loss of target reflections and higher sidelobe levels, especially in the Doppler shift direction. In the conducted analysis, these effects are measured by the peak power loss ratio, peak-to-sidelobe level ratio, and integrated sidelobe level ratio parameters, the two latter being evaluated in both range and Doppler shift directions. In addition, the signal-to-interference ratio is analyzed to allow not only quantifying the distortion of a target reflection, but also measuring the interference floor level in a radar image. The achieved results allow to quantify not only the PN-induced impairments to a single target, but also how the induced degradation may impair the sensing performance of OFDM-based ISAC systems in multi-target scenarios.
Fundamental Limits of Pulse Based UWB ISAC Systems: A Parameter Estimation Perspective
Authors: Fan Liu, Tingting Zhang, Zenan Zhang, Bin Cao, Yuan Shen, Qinyu Zhang
Date: 2024-10-17 00:00:00
Tasks: Integrated sensing and communication, ISAC, parameter estimation, Position

This paper investigates a bi-static integrated sensing and communication (ISAC) system for multi-target scenarios using impulse radio ultra-wideband (IR-UWB) signals, which offer fine temporal resolution, low power consumption, and strong resistance to multipath interference. Two typical modulation schemes, namely pulse position modulation (PPM) and binary phase shift keying (BPSK), are considered for communication over the delay and phase domains, respectively. Accordingly, we introduce a pilot-based decoupling approach that relies on known time-delays, as well as a differential decoupling strategy that uses a known starting symbol position ({no pilot required}). A key contribution of this work is the development of a unified analytical framework based on the Fisher Information Matrix (FIM), which characterizes the fundamental coupling between communication and sensing in both delay and Doppler domains. This coupling is examined through the singularity structure of the FIM, providing new theoretical insights into the joint performance limits of UWB-ISAC systems. Finally, we assess the sensing and communication performance under various modulation schemes under the constraints of current UWB standards. This assessment utilizes the Cramer-Rao Lower Bound (CRLB) for sensing and the data transmission rate for communication, offering theoretical insights into choosing suitable data signal processing methods in real-world applications.
Sum Secrecy Rate Maximization for Full Duplex ISAC Systems
Authors: Aleksandar Boljević, Ahmad Bazzi, Marwa Chafii
Date: 2024-10-17 00:00:00
Tasks: Benchmarking, Integrated sensing and communication, ISAC

In integrated sensing and communication (ISAC) systems, the target of interest may \textit{intentionally disguise itself as an eavesdropper}, enabling it to intercept and tap into the communication data embedded in the ISAC waveform. The following paper considers a full duplex (FD)-ISAC system, which involves multiple malicious targets attempting to intercept both uplink (UL) and downlink (DL) communications between the dual-functional radar and communication (DFRC) base station (BS) and legitimate UL/DL communication users (CUs). For this, we formulate an optimization framework that allows maximization of both UL and DL sum secrecy rates, under various power budget constraints for sensing and communications. As the proposed optimization problem is non-convex, we develop a method called Iterative Joint Taylor-Block cyclic coordinate descent (IJTB) by proving essential lemmas that transform the original problem into a more manageable form. In essence, IJTB alternates between two sub-problems: one yields UL beamformers in closed-form, while the other approximates the solution for UL power allocation, artificial noise covariance, and DL beamforming vectors. This is achieved through a series of Taylor approximations that effectively \textit{"convexify"} the problem, enabling efficient optimization. Simulation results demonstrate the effectiveness of the proposed solver when compared with benchmarking ones. Our findings reveal that the IJTB algorithm shows fast convergence, reaching stability within approximately $10$ iterations. In addition, all benchmarks reveal a substantial decline in the sum secrecy rate, approaching zero as the eavesdropper distance reaches $17$ meters, underscoring their vulnerability in comparison to IJTB.
A new approach for Weather Radars
Authors: Mohit Kumar, V Chandrasekar
Date: 2021-04-02 00:00:00

This paper elaborates the signal processing techniques for weather radars and their relative merits with respect to a similar phased array configuration. As will be shown in paper that this sub-aperture based configuration gives spatial resolution improvement compared to its phased array counterpart. This is the major benefit and a number of smaller benefits which are elaborated here for weather radar system.
DOA Estimation-Oriented Joint Array Partitioning and Beamforming Designs for ISAC Systems
Authors: Rang Liu, Ming Li, Qian Liu, A. Lee Swindlehurst
Date: 2024-10-16 00:00:00
Tasks: Integrated sensing and communication, ISAC

Integrated sensing and communication has been identified as an enabling technology for forthcoming wireless networks. In an effort to achieve an improved performance trade-off between multiuser communications and radar sensing, this paper considers a dynamically-partitioned antenna array architecture for monostatic ISAC systems, in which each element of the array at the base station can function as either a transmit or receive antenna. To fully exploit the available spatial degrees of freedom for both communication and sensing functions, we jointly design the partitioning of the array between transmit and receive antennas together with the transmit beamforming in order to minimize the direction-of-arrival (DOA) estimation error, while satisfying constraints on the communication signal-to-interference-plus-noise ratio and the transmit power budget. An alternating algorithm based on Dinkelbach's transform, the alternative direction method of multipliers, and majorization-minimization is developed to solve the resulting complicated optimization problem. To reduce the computational complexity, we also present a heuristic three-step strategy that optimizes the transmit beamforming after determining the antenna partitioning. Simulation results confirm the effectiveness of the proposed algorithms in significantly reducing the DOA estimation error.
Optimal Beamforming Design for ISAC with Sensor-Aided Active RIS
Authors: Ahmed Magbool, Vaibhav Kumar, Mark F. Flanagan
Date: 2024-10-16 00:00:00
Tasks: Integrated sensing and communication, ISAC

Active reconfigurable intelligent surfaces (RISs) can improve the performance of integrated sensing and communication (ISAC), and therefore enable simultaneous data transmission and target sensing. However, when the line-of-sight (LoS) link between the base station and the sensing target is blocked, the sensing signals suffer from severe path loss, resulting in an inferior sensing performance. To address this issue, this paper employs a sensor-aided active RIS to enhance ISAC system performance. The goal is to maximize the signal-to-noise ratio of the echo signal from the target at the sensor-array while meeting constraints on communication signal quality, power budgets, and RIS amplification limits. The optimization problem is challenging due to its non-convex nature and the coupling between the optimization variables. We propose a closed-form solution for receive beamforming, and a successive convex approximation based iterative method for transmit and reflection beamforming design. Simulation results demonstrate the advantage of the proposed sensor-aided active RIS-assisted system model over its non-sensor-aided counterpart.
GBSense: A GHz-Bandwidth Compressed Spectrum Sensing System
Authors: Zihang Song, Xingjian Zhang, Zhe Chen, Rahim Tafazolli, Yue Gao
Date: 2024-10-15 00:00:00

This paper presents GBSense, an innovative compressed spectrum sensing system designed for GHz-bandwidth signals in dynamic spectrum access (DSA) applications. GBSense introduces an efficient approach to periodic nonuniform sampling, capturing wideband signals using significantly lower sampling rates compared to traditional Nyquist sampling. By integrating time-interleaved analog-to-digital conversion, GBSense overcomes the hardware complexity typically associated with traditional multicoset sampling, providing precise, real-time adjustable sampling patterns without the need for analog delay circuits. The system's ability to process signals with a 2 GHz radio frequency bandwidth using only a 400 MHz average sampling rate enables more efficient spectrum monitoring and access in wideband cognitive radios. Lab tests demonstrate 100\% accurate spectrum detection when the spectrum occupancy is below 100 MHz and over 80\% accuracy for occupancy up to 200 MHz. Additionally, an integrated system utilizing a low-power Raspberry Pi processor achieves a low processing latency of around 30 ms per frame, demonstrating the system's potential for real-time applications in cognitive radio networks, 5G, and future 6G infrastructures.
Line Spectral Analysis Using the G-Filter: An Atomic Norm Minimization Approach
Authors: Bin Zhu
Date: 2024-10-16 00:00:00
Tasks: compressed sensing, subspace methods

The area of spectral analysis has a traditional dichotomy between continuous spectra (spectral densities) which correspond to purely nondeterministic processes, and line spectra (Dirac impulses) which represent sinusoids. While the former case is important in the identification of discrete-time linear stochastic systems, the latter case is essential for the analysis and modeling of time series with notable applications in radar systems. In this paper, we develop a novel approach for line spectral estimation which combines ideas of Georgiou's filter banks (G-filters) and atomic norm minimization (ANM), a mainstream method for line spectral analysis in the last decade following the theory of compressed sensing. Such a combination is only possible because a Carath\'{e}odory--Fej\'{e}r-type decomposition is available for the covariance matrix of the filter output. The ANM problem can be characterized via semidefinite programming which can be solved efficiently. As a consequence, our optimization theory can be seen as a substantial generalization of the standard ANM for line spectral estimation. Moreover, our ANM approach with a G-filter has significant advantages over subspace methods because it can work with just one output vector and without \emph{a priori} knowledge about the number of sinusoids in the input. Simulation results show that our approach performs reasonably well under different signal-to-noise ratios when the G-filter is suitably designed.
Modeling, Design, and Verification of An Active Transmissive RIS
Authors: Rongguang Song, Haifan Yin, Zipeng Wang, Taorui Yang, Xue Ren
Date: 2024-10-16 00:00:00

Reconfigurable Intelligent Surface (RIS) is a promising technology that may effectively improve the quality of signals in wireless communications. In practice, however, the ``double fading'' effect undermines the application of RIS and constitutes a significant challenge to its commercialization. To address this problem, we present a novel 2-bit programmable amplifying transmissive RIS with a power amplification function to enhance the transmission of electromagnetic signals. The transmissive function is achieved through a pair of radiation patches located on the upper and lower surfaces, respectively, while a microstrip line connects two patches. A power amplifier, SP4T switch, and directional coupler provide signal amplification and a 2-bit phase shift. To characterize the signal enhancement of active transmissive RIS, we propose a dual radar cross section (RCS)-based path loss model to predict the power of the received signal for active transmissive RIS-aided wireless communication systems. Simulation and experimental results verify the reliability of the RIS design, and the proposed path loss model is validated by measurements. Compared with the traditional passive RIS, the signal power gain in this design achieves 11.9 dB.