pwc-archive/papers-with-abstracts huggingface dataset:
On real-time multi-stage speech enhancement systems
Authors: Lingjun Meng, Jozef Coldenhoff, Paul Kendrick, Tijana Stojkovic, Andrew Harper, Kiril Ratmanski, Milos Cernak
Date: 2023-12-19 00:00:00
Tasks: Speech Enhancement

Recently, multi-stage systems have stood out among deep learning-based speech enhancement methods. However, these systems are always high in complexity, requiring millions of parameters and powerful computational resources, which limits their application for real-time processing in low-power devices. Besides, the contribution of various influencing factors to the success of multi-stage systems remains unclear, which presents challenges to reduce the size of these systems. In this paper, we extensively investigate a lightweight two-stage network with only 560k total parameters. It consists of a Mel-scale magnitude masking model in the first stage and a complex spectrum mapping model in the second stage. We first provide a consolidated view of the roles of gain power factor, post-filter, and training labels for the Mel-scale masking model. Then, we explore several training schemes for the two-stage network and provide some insights into the superiority of the two-stage network. We show that the proposed two-stage network trained by an optimal scheme achieves a performance similar to a four times larger open source model DeepFilterNet2.
Full-reference Video Quality Assessment for User Generated Content Transcoding
Authors: Zihao Qi, Chen Feng, Duolikun Danier, Fan Zhang, Xiaozhong Xu, Shan Liu, David Bull
Date: 2023-12-19 00:00:00
Tasks: Video Quality Assessment, Visual Question Answering (VQA)

Unlike video coding for professional content, the delivery pipeline of User Generated Content (UGC) involves transcoding where unpristine reference content needs to be compressed repeatedly. In this work, we observe that existing full-/no-reference quality metrics fail to accurately predict the perceptual quality difference between transcoded UGC content and the corresponding unpristine references. Therefore, they are unsuited for guiding the rate-distortion optimisation process in the transcoding process. In this context, we propose a bespoke full-reference deep video quality metric for UGC transcoding. The proposed method features a transcoding-specific weakly supervised training strategy employing a quality ranking-based Siamese structure. The proposed method is evaluated on the YouTube-UGC VP9 subset and the LIVE-Wild database, demonstrating state-of-the-art performance compared to existing VQA methods.
Automatic bony structure segmentation and curvature estimation on ultrasound cervical spine images -- a feasibility study
Authors: Songhan Ge, Haoyuan Tian, Wei zhang, Rui Zheng
Date: 2023-12-19 00:00:00

The loss of cervical lordosis is a common degenerative disorder known to be associated with abnormal spinal alignment. In recent years, ultrasound (US) imaging has been widely applied in the assessment of spine deformity and has shown promising results. The objectives of this study are to automatically segment bony structures from the 3D US cervical spine image volume and to assess the cervical lordosis on the key sagittal frames. In this study, a portable ultrasound imaging system was applied to acquire cervical spine image volume. The nnU-Net was trained on to segment bony structures on the transverse images and validated by 5-fold-cross-validation. The volume data were reconstructed from the segmented image series. An energy function indicating intensity levels and integrity of bony structures was designed to extract the proxy key sagittal frames on both left and right sides for the cervical curve measurement. The mean absolute difference (MAD), standard deviation (SD) and correlation between the spine curvatures of the left and right sides were calculated for quantitative evaluation of the proposed method. The DSC value of the nnU-Net model in segmenting ROI was 0.973. For the measurement of 22 lamina curve angles, the MAD, SD and correlation between the left and right sides of the cervical spine were 3.591, 3.432 degrees and 0.926, respectively. The results indicate that our method has a high accuracy and reliability in the automatic segmentation of the cervical spine and shows the potential of diagnosing the loss of cervical lordosis using the 3D ultrasound imaging technique.
Quantized Decoder in Learned Image Compression for Deterministic Reconstruction
Authors: Esin Koyuncu, Timofey Solovyev, Johannes Sauer, Elena Alshina, André Kaup
Date: 2023-12-18 00:00:00
Tasks: Decoder, Image Compression, Quantization

Learned image compression has a problem of non-bit-exact reconstruction due to different calculations of floating point arithmetic on different devices. This paper shows a method to achieve a deterministic reconstructed image by quantizing only the decoder of the learned image compression model. From the implementation perspective of an image codec, it is beneficial to have the results reproducible when decoded on different devices. In this paper, we study quantization of weights and activations without overflow of accumulator in all decoder subnetworks. We show that the results are bit-exact at the output, and the resulting BD-rate loss of quantization of decoder is 0.5 % in the case of 16-bit weights and 16-bit activations, and 7.9 % in the case of 8-bit weights and 16-bit activations.
Joint Range-Velocity-Azimuth Estimation for OFDM-Based Integrated Sensing and Communication
Authors: Zelin Hu, Qibin Ye, Yixuan Huang, Su Hu, Gang Yang
Date: 2023-12-20 00:00:00
Tasks: Integrated sensing and communication, ISAC, Super-Resolution

Orthogonal frequency division multiplexing (OFDM)-based integrated sensing and communication (ISAC) is promising for future sixth-generation mobile communication systems. Existing works focus on the joint estimation of the targets' range and velocity for OFDM-based ISAC systems. In contrast, this paper studies the three-dimensional joint estimation (3DJE) of range, velocity, and azimuth for OFDM-based ISAC systems with multiple receive antennas. First, we establish the signal model and derive the Cramer-Rao bounds (CRBs) on the 3DJE. Furthermore, an auto-paired super-resolution 3DJE algorithm is proposed by exploiting the reconstructed observation sub-signal's translational invariance property in the time, frequency, and space domains. Finally, with the 5G New Radio parameter setup, simulation results show that the proposed algorithm achieves better estimation performance and its root mean square error is closer to the root of CRBs than existing methods.
Class Information Guided Reconstruction for Automatic Modulation Open-Set Recognition
Authors: Ziwei Zhang, Mengtao Zhu, Jiabin Liu, Yunjie Li, Shafei Wang
Date: 2023-12-20 00:00:00
Tasks: Automatic Modulation Recognition, Denoising, Mathematical Proofs, Open Set Learning

Automatic Modulation Recognition (AMR) is a crucial technology in the domains of radar and communications. Traditional AMR approaches assume a closed-set scenario, where unknown samples are forcibly misclassified into known classes, leading to serious consequences for situation awareness and threat assessment. To address this issue, Automatic Modulation Open-set Recognition (AMOSR) defines two tasks as Known Class Classification (KCC) and Unknown Class Identification (UCI). However, AMOSR faces core challenges in terms of inappropriate decision boundaries and sparse feature distributions. To overcome the aforementioned challenges, we propose a Class Information guided Reconstruction (CIR) framework, which leverages reconstruction losses to distinguish known and unknown classes. To enhance distinguishability, we design Class Conditional Vectors (CCVs) to match the latent representations extracted from input samples, achieving perfect reconstruction for known samples while yielding poor results for unknown ones. We also propose a Mutual Information (MI) loss function to ensure reliable matching, with upper and lower bounds of MI derived for tractable optimization and mathematical proofs provided. The mutually beneficial CCVs and MI facilitate the CIR attaining optimal UCI performance without compromising KCC accuracy, especially in scenarios with a higher proportion of unknown classes. Additionally, a denoising module is introduced before reconstruction, enabling the CIR to achieve a significant performance improvement at low SNRs. Experimental results on simulated and measured signals validate the effectiveness and the robustness of the proposed method.
Reducing Satellite Interference to Radio Telescopes Using Beacons
Authors: Cuneyd Ozturk, Randall A. Berry, Dongning Guo, Michael L. Honig, Frank D. Lind
Date: 2023-12-20 00:00:00

This paper proposes the transmission of beacon signals to alert potential interferers of an ongoing or impending passive sensing measurement. We focus on the interference from Low-Earth Orbiting (LEO) satellites to a radio-telescope. We compare the beacon approach with two versions of Radio Quiet Zones (RQZs): fixed quiet zones on the ground and in the sky, and dynamic quiet zones that vary across satellites. The beacon-assisted approach can potentially exploit channel reciprocity, which accounts for short-term channel variations between the satellite and radio telescope. System considerations associated with beacon design and potential schemes for beacon transmission are discussed. The probability of excessive Radio Frequency Interference (RFI) at the radio telescope (outage probability) and the fraction of active links in the satellite network are used as performance metrics. Numerical simulations compare the performance of the approaches considered, and show that the beacon approach enables more active satellite links relative to quiet zones for a given outage probability.
Reconfigurable Intelligent Surface-Assisted Localization in OFDM Systems with Carrier Frequency Offset and Phase Noise
Authors: Hanfu Zhang, Erwu Liu, Rui Wang, Wei Ni, Zhe Xing, Yan Liu, Abbas Jamalipour
Date: 2023-12-19 00:00:00
Tasks: Position

Reconfigurable intelligent surface (RIS)-assisted communication systems have been extensively studied for providing high-precision location services. However, most studies have overlooked the impact of carrier frequency offset (CFO) and phase noise (PN) resulting from hardware impairments on localization. This paper presents a novel, alternating optimization (AO)-based algorithm to jointly estimate the CFO, PN, and user equipment (UE) position in orthogonal frequency division multiplexing (OFDM) systems, where, provided the UE position, closed-form expressions for the CFO and PN are derived per iteration, significantly reducing the complexity and enhancing the stability of the algorithm. Another important aspect is a new RIS phase shift optimization algorithm developed to minimize the analytical lower bound of localization accuracy, hence benefiting localization. The semidefinite relaxation method and Schur complement are utilized to convexify this challenging non-convex optimization problem to a semidefinite program. Simulations demonstrate the effectiveness of the proposed algorithms, with the localization accuracy enhanced by two orders of magnitude. The localization accuracy of the proposed algorithm is close to the analytical lower bound, with a root mean square error of lower than $\rm 10^{-2} \: m$.
Scalable Near-Field Localization Based on Partitioned Large-Scale Antenna Array
Authors: Xiaojun Yuan, Yuqing Zheng, Mingchen Zhang, Boyu Teng, Wenjun Jiang
Date: 2023-12-19 00:00:00

This paper studies a passive localization system, where an extremely large-scale antenna array (ELAA) is deployed at the base station (BS) to locate a user equipment (UE) residing in its near-field (Fresnel) region. We propose a novel algorithm, named array partitioning-based location estimation (APLE), for scalable near-field localization. The APLE algorithm is developed based on the basic assumption that, by partitioning the ELAA into multiple subarrays, the UE can be approximated as in the far-field region of each subarray. We establish a Bayeian inference framework based on the geometric constraints between the UE location and the angles of arrivals (AoAs) at different subarrays. Then, the APLE algorithm is designed based on the message-passing principle for the localization of the UE. APLE exhibits linear computational complexity with the number of BS antennas, leading to a significant reduction in complexity compared to existing methods. We further propose an enhanced APLE (E-APLE) algorithm that refines the location estimate obtained from APLE by following the maximum likelihood principle. The E-APLE algorithm achieves superior localization accuracy compared to APLE while maintaining a linear complexity with the number of BS antennas. Numerical results demonstrate that the proposed APLE and E-APLE algorithms outperform the existing baselines in terms of localization accuracy.
Waveform Design for OFDM-based ISAC Systems Under Resource Occupancy Constraint
Authors: Silvia Mura, Dario Tagliaferri, Marouan Mizmizi, Umberto Spagnolini, Athina Petropulu
Date: 2023-12-19 00:00:00
Tasks: Integrated sensing and communication, ISAC, Matrix Completion

Integrated Sensing and Communication (ISAC) is one of the key pillars envisioned for 6G wireless systems. ISAC systems combine communication and sensing functionalities over a single waveform, with full resource sharing. In particular, waveform design for legacy Orthogonal Frequency Division Multiplexing (OFDM) systems consists of a suitable time-frequency resource allocation policy balancing between communication and sensing performance. Over time and/or frequency, having unused resources leads to an ambiguity function with high sidelobes that significantly affect the performance of ISAC for OFDM waveforms. This paper proposes an OFDM-based ISAC waveform design that takes into account communication and resource occupancy constraints. The proposed method minimizes the Cram\'er-Rao Bound (CRB) on delay and Doppler estimation for two closely spaced targets. Moreover, the paper addresses the under-sampling issue by interpolating the estimated sensing channel based on matrix completion via Schatten $p$-norm approximation. Numerical results show that the proposed waveform outperforms the state-of-the-art methods.
Joint DOA estimation and distorted sensor detection under entangled low-rank and row-sparse constraints
Authors: Huiping Huang, Tianjian Zhang, Feng Yin, Bin Liao, Henk Wymeersch
Date: 2023-12-19 00:00:00
Tasks: Direction of Arrival Estimation

The problem of joint direction-of-arrival estimation and distorted sensor detection has received a lot of attention in recent decades. Most state-of-the-art work formulated such a problem via low-rank and row-sparse decomposition, where the low-rank and row-sparse components were treated in an isolated manner. Such a formulation results in a performance loss. Differently, in this paper, we entangle the low-rank and row-sparse components by exploring their inherent connection. Furthermore, we take into account the maximal distortion level of the sensors. An alternating optimization scheme is proposed to solve the low-rank component and the sparse component, where a closed-form solution is derived for the low-rank component and a quadratic programming is developed for the sparse component. Numerical results exhibit the effectiveness and superiority of the proposed method.
Control Aspects for Using RIS in Latency-Constrained Mobile Edge Computing
Authors: Fabio Saggese, Victor Croisfelt, Francesca Costanzo, Junya Shiraishi, Radosław Kotaba, Paolo Di Lorenzo, Petar Popovski
Date: 2023-12-19 00:00:00
Tasks: Edge-computing

This paper investigates the role and the impact of control operations for dynamic mobile edge computing (MEC) empowered by Reconfigurable Intelligent Surfaces (RISs), in which multiple devices offload their computation tasks to an access point (AP) equipped with an edge server (ES), with the help of the RIS. While usually ignored, the control aspects related to channel estimation (CE), resource allocation (RA), and control signaling play a fundamental role in the user-perceived delay and energy consumption. In general, the higher the resources involved in the control operations, the higher their reliability; however, this introduces an overhead, which reduces the number of resources available for computation offloading, possibly increasing the overall latency experienced. Conversely, a lower control overhead translates to more resources available for computation offloading but impacts the CE accuracy and RA flexibility. This paper establishes a basic framework for integrating the impact of control operations in the performance evaluation of the RIS-aided MEC paradigm, clarifying their trade-offs through theoretical analysis and numerical simulations.
Optimal BER Minimum Precoder Design for OTFS-Based ISAC Systems
Authors: Jun Wu, Weijie Yuan, Zhiqiang Wei, Jinjin Yan, Derrick Wing Kwan Ng
Date: 2023-12-19 00:00:00
Tasks: ISAC

This paper investigates the bit error rate (BER) minimum pre-coder design for an orthogonal time frequency space (OTFS)-based integrated sensing and communications (ISAC) system, which is considered as a promising technique for enabling future wireless networks. In particular, the BER minimum problem takes into account the maximized available transmission power and the required sensing performance. We devise the precoder from the perspective of delay-Doppler (DD) domain by exploiting the equivalent DD channel. To address the non-convex design problem, we resort to minimizing the lower bound of the derived average BER. Afterwards, we propose a computationally iterative method to solve the dual problem at low cost. Simulation results verify the effectiveness of our proposed precoder and reveal the interplay between sensing and communication for dual-functional precoder design.
Secure Full Duplex Integrated Sensing and Communications
Authors: Ahmad Bazzi, Marwa Chafii
Date: 2023-12-18 00:00:00
Tasks: Integrated sensing and communication, ISAC

The following paper models a secure full duplex (FD) integrated sensing and communication (ISAC) scenario, where malicious eavesdroppers aim at intercepting the downlink (DL) as well as the uplink (UL) information exchanged between the dual functional radar and communication (DFRC) base station (BS) and a set of communication users. The DFRC BS, on the other hand, aims at illuminating radar beams at the eavesdroppers in order to sense their physical parameters, while maintaining high UL/DL secrecy rates. Based on the proposed model, we formulate a power efficient secure ISAC optimization framework design, which is intended to guarantee both UL and DL secrecy rates requirements, while illuminating radar beams towards eavesdroppers. The framework exploits artificial noise (AN) generation at the DFRC BS, along with UL/DL beamforming design and UL power allocation. We propose a beamforming design solution to the secure ISAC optimization problem. Finally, we corroborate our findings via simulation results and demonstrate the feasibility, as well as the superiority of the proposed algorithm, under different situations. We also reveal insightful trade-offs achieved by our approach.
Target detection using underwater acoustic communication links
Authors: Lu Shen, Yuriy Zakharov, Benjamin Henson, Nils Morozs, Benoît Parrein, Paul D. Mitchell
Date: 2023-12-18 00:00:00

Underwater monitoring and surveillance systems are essential for underwater target detection, localization and classification. The aim of this work is to investigate the possibility of target detection by using data transmission between communication nodes in an underwater acoustic (UWA) network, i.e, re-using acoustic communication signals for target detection. A new target detection method based on estimation of the time-varying channel impulse response between the communication transmitter(s) and receiver(s) is proposed and investigated. This is based on a lake experiment and numerical experiments using a simulator developed for modeling the time-varying UWA channel in the presence of a moving target. The proposed detection method provides a clear indication of a target crossing the communication link. A good similarity between results obtained in the numerical and lake experiments is observed.
Radar Classification of Vehicles Using a Ground-Reflection Model
Authors: Sören Kohnert, Dominik Zoeke, Reinhard Stolle
Date: 2023-12-18 00:00:00
Tasks: Classification

Classification of road users is important for traffic monitoring. The usability of a height estimate based on the two-ray ground-reflection model as a feature for the classification of vehicles is analyzed in this paper. The four-ray ground-reflection model for fast chirp ramp sequence waveforms of FMCW radars is derived and simplified to the well-known two-ray ground-reflection model. A spectrum from which the height of a target can be derived is obtained using the Lomb-Scargle periodogram. Measurements with two vehicle classes illustrate the approach and show that the model could be used as a feature to distinguish vehicles based on their height.
Core consistency diagnosis for Block Term Decomposition in rank $(L_r, L_r, 1)$
Authors: Noramon Dron, Javier Escudero
Date: 2023-12-18 00:00:00
Tasks: Diagnostic

Determining the underlying number of components $R$ in tensor decompositions is challenging. Diverse techniques exist for various decompositions, notably the core consistency diagnostic (CORCONDIA) for Canonical Polyadic Decomposition (CPD). Here, we propose a model that intuitively adapts CORCONDIA for rank estimation in Block Term Decomposition (BTD) of rank $(L_r, L_r, 1)$: BTDCORCONDIA. Our metric was tested on simulated and real-world tensor data, including assessments of its sensitivity to noise and the indeterminacy of BTD $(L_r, L_r, 1)$. We found that selecting appropriate $R$ and $L_r$ led to core consistency reaching or close to 100%, and BTDCORCONDIA is efficient when the tensor has significantly more elements than the core. Our results confirm that CORCONDIA can be extended to BTD $(L_r, L_r, 1)$, and the resulting metric can assist in the process of determining the number of components in this tensor factorisation.
FMCW Radar Height Estimation of Moving Vehicles by Analyzing Multipath Reflections
Authors: Soren Kohnert, Michael Vogt, Reinhard Stolle
Date: 2023-12-18 00:00:00

Target classification is an important task of automotive radar systems. In this work, a concept for estimating the height of vehicles to allow for a differentiation between passenger cars, trucks, and others, is presented and discussed. Fixed installed radar sensors for traffic monitoring in the 77 GHz band are used to track and analyze radar echoes from individual vehicles as they move relative to the radar. Considering multipath propagation, which includes the ground reflection, the height of individual radar targets is estimated by analyzing the periodicity of the resulting amplitude modulation (AM) of the echo signal as a function of the horizontal distance from the radar. Two approaches have been realized to integrate the concept into an automotive FMCW signal processing scheme. Measurements in a test field using a trihedral corner reflector as an idealized target in heights from 0 to 2.5 m suggest, that the concept is suitable for height estimation in an automotive context.