pwc-archive/papers-with-abstracts huggingface dataset:
Deep Image prior with StruCtUred Sparsity (DISCUS) for dynamic MRI reconstruction
Authors: Muhammad A. Sultan, Chong Chen, Yingmin Liu, Xuan Lei, Rizwan Ahmad
Date: 2023-12-01 00:00:00
Tasks: compressed sensing, MRI Reconstruction

High-quality training data are not always available in dynamic MRI. To address this, we propose a self-supervised deep learning method called deep image prior with structured sparsity (DISCUS) for reconstructing dynamic images. DISCUS is inspired by deep image prior (DIP) and recovers a series of images through joint optimization of network parameters and input code vectors. However, DISCUS additionally encourages group sparsity on frame-specific code vectors to discover the low-dimensional manifold that describes temporal variations across frames. Compared to prior work on manifold learning, DISCUS does not require specifying the manifold dimensionality. We validate DISCUS using three numerical studies. In the first study, we simulate a dynamic Shepp-Logan phantom with frames undergoing random rotations, translations, or both, and demonstrate that DISCUS can discover the dimensionality of the underlying manifold. In the second study, we use data from a realistic late gadolinium enhancement (LGE) phantom to compare DISCUS with compressed sensing (CS) and DIP, and to demonstrate the positive impact of group sparsity. In the third study, we use retrospectively undersampled single-shot LGE data from five patients to compare DISCUS with CS reconstructions. The results from these studies demonstrate that DISCUS outperforms CS and DIP, and that enforcing group sparsity on the code vectors helps discover true manifold dimensionality and provides additional performance gain.
Surface Coil Intensity Correction for MRI
Authors: Xuan Lei, Philip Schniter, Chong Chen, Muhammad A. Sultan, Rizwan Ahmad
Date: 2023-12-01 00:00:00
Tasks: Sensitivity

Modern MRI scanners utilize one or more arrays of small receive-only coils to collect k-space data. The sensitivity maps of the coils, when estimated using traditional methods, differ from the true sensitivity maps, which are generally unknown. Consequently, the reconstructed MR images exhibit undesired spatial variation in intensity. These intensity variations can be at least partially corrected using pre-scan data. In this work, we propose an intensity correction method that utilizes pre-scan data. For demonstration, we apply our method to a digital phantom, as well as to cardiac MRI data collected on a commercial scanner by Siemens Healthineers. The code is available at https://github.com/OSU-MR/SCC.
New Filters for Image Interpolation and Resizing
Authors: Amir Said
Date: 2023-12-01 00:00:00

We propose a new class of kernels to simplify the design of filters for image interpolation and resizing. Their properties are defined according to two parameters, specifying the width of the transition band and the height of a unique sidelobe. By varying these parameters it is possible to efficiently explore the space with only the filters that are suitable for image interpolation and resizing, and identify the filter that is best for a given application. These two parameters are also sufficient to obtain very good approximations of many commonly-used interpolation kernels. We also show that, because the Fourier transforms of these kernels have very fast decay, these filters produce better results when time-stretched for image downsizing.
Joint Multiple FMCW Chirp Sequence Processing for Velocity Estimation and Ambiguity Resolving
Authors: Tarik Kazaz, Karan Jayachandra, Arie Koppellar, Yiting Lu
Date: 2023-12-02 00:00:00
Tasks: Super-Resolution

In FMCW automotive radar applications, it is often a challenge to design a chirp sequence that satisfies the requirements set by practical driving scenarios and simultaneously enables high range resolution, large maximum range, and unambiguous velocity estimation. To support long-range scenarios the chirps should have a sufficiently long duration compared to their bandwidth. At the same time, the long chirps result in ambiguous velocity estimation for targets with high velocity. The problem of velocity ambiguity is often solved by using multiple chirp sequences with co-prime delay shifts between them. However, coherent processing of multiple chirp sequences is not possible using classical spectral estimation techniques based on Fast Fourier Transform (FFT). This results in statistically not efficient velocity estimation and loss of processing gain. In this work, we propose an algorithm that can jointly process multiple chirp sequences and resolve possible ambiguities present in the velocities estimates. The resulting algorithm is statistically efficient and gridless. Furthermore, it increases the resolution of velocity estimation beyond the natural resolution due to its super-resolution properties. These results are confirmed by both numerical simulations and experiments with automotive radar IC.
Metasurface Sensing Approach to DOA Estimation of Coherent Signals
Authors: Yishuo Zhao, Yan Hu, Yougen Xu
Date: 2023-12-05 00:00:00

The DOA estimation method of coherent signals based on periodical coding metasurface is proposed. After periodical coding, the DOA information of incident signals in the time domain is represented as the amplitude and phase information at different frequency points in the frequency domain. Finite time Fourier transform (FTFT) is performed on the received signal and appropriate frequency points are selected to reconstruct the frequency domain snapshot, then pattern smoothing (PS) technique is applied to execute DOA estimation. Compared with conventional DOA estimation methods, the proposed method has two main advantages: one is that only a single receiving channel is needed to avoid the appearance of channel mismatch errors, the other is that it can process with multiple coherent signals. The performance curves of the proposed method are analyzed under different conditions and compared with existing methods. Simulation results show the effectiveness of the proposed method.
Time-based vs. Fingerprinting-based Positioning Using Artificial Neural Networks
Authors: Anil Kirmaz, Taylan Sahin, Diomidis S. Michalopoulos, Wolfgang Gerstacker
Date: 2023-12-04 00:00:00

High-accuracy positioning has gained significant interest for many use-cases across various domains such as industrial internet of things (IIoT), healthcare and entertainment. Radio frequency (RF) measurements are widely utilized for user localization. However, challenging radio conditions such as non-line-of-sight (NLOS) and multipath propagation can deteriorate the positioning accuracy. Machine learning (ML)-based estimators have been proposed to overcome these challenges. RF measurements can be utilized for positioning in multiple ways resulting in time-based, angle-based and fingerprinting-based methods. Different methods, however, impose different implementation requirements to the system, and may perform differently in terms of accuracy for a given setting. In this paper, we use artificial neural networks (ANNs) to realize time-of-arrival (ToA)-based and channel impulse response (CIR) fingerprinting-based positioning. We compare their performance for different indoor environments based on real-world ultra-wideband (UWB) measurements. We first show that using ML techniques helps to improve the estimation accuracy compared to conventional techniques for time-based positioning. When comparing time-based and fingerprinting schemes using ANNs, we show that the favorable method in terms of positioning accuracy is different for different environments, where the accuracy is affected not only by the radio propagation conditions but also the density and distribution of reference user locations used for fingerprinting.
Optimal Dual-Polarized Planar Arrays for Massive Capacity Over Point-to-Point MIMO Channels
Authors: Amna Irshad, Alva Kosasih, Emil Björnson, Luca Sanguinetti
Date: 2023-12-04 00:00:00

Future wireless networks must provide ever higher data rates. The available bandwidth increases roughly linearly as we increase the carrier frequency, but the range shrinks drastically. This paper explores if we can instead reach massive capacities using spatial multiplexing over multiple-input multiple-output (MIMO) channels. In line-of-sight (LOS) scenarios, therank of the MIMO channel matrix depends on the polarization and antenna arrangement. We optimize the rank and condition number by identifying the optimal antenna spacing in dual-polarized planar antenna arrays with imperfect isolation. The result is sparely spaced antenna arrays that exploit radiative near-field properties. We further optimize the array geometry for minimum aperture length and aperture area, which leads to different configurations. Moreover, we prove analytically that for fixed-sized arrays, the MIMO rank grows quadratically with the carrier frequency in LOS scenarios, if the antennas are appropriately designed. Hence, MIMO technology contributes more to the capacity growth than the bandwidth. The numerical results show that massive data rates, far beyond 1 Tbps, can be reached both over fixed point-to-point links. It is also possible for a large base station to serve a practically-sized mobile device.
Intelligent Reflecting Surface-Aided Electromagnetic Stealth Against Radar Detection
Authors: Beixiong Zheng, Xue Xiong, Jie Tang, Rui Zhang
Date: 2023-12-04 00:00:00

While traditional electromagnetic stealth materials/metasurfaces can render a target virtually invisible to some extent, they lack flexibility and adaptability, and can only operate within a limited frequency and angle/direction range, making it challenging to ensure the expected stealth performance. In view of this, we propose in this paper a new intelligent reflecting surface (IRS)-aided electromagnetic stealth system mounted on targets to evade radar detection, by utilizing the tunable passive reflecting elements of IRS to achieve flexible and adaptive electromagnetic stealth in a cost-effective manner. Specifically, we optimize the IRS's reflection at the target to minimize the sum received signal power of all adversary radars. We first address the IRS's reflection optimization problem using the Lagrange multiplier method and derive a semi-closed-form optimal solution for the single-radar setup, which is then generalized to the multi-radar case. To meet real-time processing requirements, we further propose low-complexity closed-form solutions based on the reverse alignment/cancellation and minimum mean-square error (MMSE) criteria for the single-radar and multi-radar cases, respectively. Additionally, we propose practical low-complexity estimation schemes at the target to acquire angle-of-arrival (AoA) and/or path gain information via a small number of receive sensing devices. Simulation results validate the performance advantages of our proposed IRS-aided electromagnetic stealth system with the proposed IRS reflection designs.
Highly Accelerated Weighted MMSE Algorithms for Designing Precoders in FDD Systems with Incomplete CSI
Authors: Donia Ben Amor, Michael Joham, Wolfgang Utschick
Date: 2023-12-04 00:00:00

In this work, we derive a lower bound on the training-based achievable downlink (DL) sum rate (SR) of a multi-user multiple-input-single-output (MISO) system operating in frequency-division-duplex (FDD) mode. Assuming linear minimum mean square error (LMMSE) channel estimation is used, we establish a connection of the derived lower bound on the signal-to-interference-noise-ratio (SINR) to an average MSE that allows to reformulate the SR maximization problem as the minimization of the augmented weighted average MSE (AWAMSE). We propose an iterative precoder design with three alternating steps, all given in closed form, drastically reducing the computation time. We show numerically the effectiveness of the proposed approach in challenging scenarios with limited channel knowledge, i.e., we consider scenarios with a very limited number of pilots. We additionally propose a more efficient version of the well-known stochastic iterative WMMSE (SIWMMSE) approach, where the precoder update is given in closed form.
Novel KLD-based Resource Allocation for Integrated Sensing and Communication
Authors: Yousef Kloob, Mohammad Al-jarrah, Emad Alsusa, Christos Masouros
Date: 2023-12-03 00:00:00
Tasks: Integrated sensing and communication, ISAC

In this paper, we introduce a novel resource allocation approach for integrated sensing-communication (ISAC) using the Kullback-Leibler divergence (KLD) metric. Specifically, we consider a base-station with limited power and antenna resources serving a number of communication users and detecting multiple targets simultaneously. First, we analyze the KLD for two possible antenna deployments, which are the separated and shared deployments, then use the results to optimize the resources of the base-station through minimising the average KLD for the network while satisfying a minimum predefined KLD requirement for each user equipment (UE) and target. To this end, the optimisation is formulated and presented as a mixed integer nonlinear programming (MINLP) problem and then solved using two approaches. In the first approach, we employ a genetic algorithm, which offers remarkable performance but demands substantial computational resources; and in the second approach, we propose a rounding-based interior-point method (RIPM) that provides a more computationally-efficient alternative solution at a negligible performance loss. The results demonstrate that the KLD metric can be an effective means for optimising ISAC networks, and that both optimisation solutions presented offer superior performance compared to uniform power and antenna allocation.
Self-Critical Alternate Learning based Semantic Broadcast Communication
Authors: Zhilin Lu, Rongpeng Li, Ming Lei, Chan Wang, Zhifeng Zhao, Honggang Zhang
Date: 2023-12-03 00:00:00
Tasks: Reinforcement Learning (RL), Semantic Communication, Semantic Similarity, Semantic Textual Similarity, Sentence, Sentence Similarity

Semantic communication (SemCom) has been deemed as a promising communication paradigm to break through the bottleneck of traditional communications. Nonetheless, most of the existing works focus more on point-to-point communication scenarios and its extension to multi-user scenarios is not that straightforward due to its cost-inefficiencies to directly scale the JSCC framework to the multi-user communication system. Meanwhile, previous methods optimize the system by differentiable bit-level supervision, easily leading to a "semantic gap". Therefore, we delve into multi-user broadcast communication (BC) based on the universal transformer (UT) and propose a reinforcement learning (RL) based self-critical alternate learning (SCAL) algorithm, named SemanticBC-SCAL, to capably adapt to the different BC channels from one transmitter (TX) to multiple receivers (RXs) for sentence generation task. In particular, to enable stable optimization via a nondifferentiable semantic metric, we regard sentence similarity as a reward and formulate this learning process as an RL problem. Considering the huge decision space, we adopt a lightweight but efficient self-critical supervision to guide the learning process. Meanwhile, an alternate learning mechanism is developed to provide cost-effective learning, in which the encoder and decoders are updated asynchronously with different iterations. Notably, the incorporation of RL makes SemanticBC-SCAL compliant with any user-defined semantic similarity metric and simultaneously addresses the channel non-differentiability issue by alternate learning. Besides, the convergence of SemanticBC-SCAL is also theoretically established. Extensive simulation results have been conducted to verify the effectiveness and superiorness of our approach, especially in low SNRs.
Integrating Communication, Sensing and Computing in Satellite Internet of Things: Challenges and Opportunities
Authors: Yong Zuo, Mingyang Yue, Huiyuan Yang, Liantao Wu, Xiaojun Yuan
Date: 2023-12-03 00:00:00

Satellite Internet of Things (IoT) is to use satellites as the access points for IoT devices to achieve the global coverage of future IoT systems, and is expected to support burgeoning IoT applications, including communication, sensing, and computing. However, the complex and dynamic satellite environments and limited network resources raise new challenges in the design of satellite IoT systems. In this article, we focus on the joint design of communication, sensing, and computing to improve the performance of satellite IoT, which is quite different from the case of terrestrial IoT systems. We describe how the integration of the three functions can enhance system capabilities, and summarize the state-of-the-art solutions. Furthermore, we discuss the main challenges of integrating communication, sensing, and computing in satellite IoT to be solved with pressing interest.
Stochastic Resource Allocation via Dual Tail Waterfilling
Authors: Gokberk Yaylali, Dionysios S. Kalogerias
Date: 2023-12-03 00:00:00

Optimal resource allocation in wireless systems still stands as a rather challenging task due to the inherent statistical characteristics of channel fading. On the one hand, minimax/outage-optimal policies are often overconservative and analytically intractable, despite advertising maximally reliable system performance. On the other hand, ergodic-optimal resource allocation policies are often susceptible to the statistical dispersion of heavy-tailed fading channels, leading to relatively frequent drastic performance drops. We investigate a new risk-aware formulation of the classical stochastic resource allocation problem for point-to-point power-constrained communication networks over fading channels with no cross-interference, by leveraging the Conditional Value-at-Risk (CV@R) as a coherent measure of risk. We rigorously derive closed-form expressions for the CV@R-optimal risk-aware resource allocation policy, as well as the optimal associated quantiles of the corresponding user rate functions by capitalizing on the underlying fading distribution, parameterized by dual variables. We then develop a purely dual tail waterfilling scheme, achieving significantly more rapid and assured convergence of dual variables, as compared with the primal-dual tail waterfilling algorithm, recently proposed in the literature. The effectiveness of the proposed scheme is also readily confirmed via detailed numerical simulations.
Perceptive, Resilient, and Efficient Networks assisted by Reconfigurable Intelligent Surfaces
Authors: Giorgos Stratidakis, Sotiris Droulias, Angeliki Alexiou
Date: 2023-12-02 00:00:00

Wireless communications are nowadays shifting to higher operation frequencies with the aim to meet the ever-increasing demand for bandwidth. While reconfigurable intelligent surfaces (RISs) are usually envisioned to restore the line-of-sight of blocked links and to efficiently counteract the increased pathloss, their functionalities can extend far beyond these basic operations. Owing to their large surface and the multitude of scatterers, RISs can be exploited to perform advanced wavefront engineering, essentially transforming the incident beam into a non-trivial reflected beam that is able to address the challenges of high frequencies more efficiently than conventional beam-forming. In this paper it is demonstrated how advanced wavefront engineering with RISs enables beam profiles that are able to focus, bend and self-heal, thus offering functionalities beyond the current state-of-the-art. Their potential as enablers of perceptive, resilient, and efficient networks is discussed, and a localization technique based on a hybrid beam-forming/beam-focusing scheme is demonstrated.
Securing the Sensing Functionality in ISAC Networks: An Artificial Noise Design
Authors: Jiaqi Zou, Christos Masouros, Fan Liu, Songlin Sun
Date: 2023-12-02 00:00:00
Tasks: ISAC

Integrated sensing and communications (ISAC) systems employ dual-functional signals to simultaneously accomplish radar sensing and wireless communication tasks. However, ISAC systems open up new sensing security vulnerabilities to malicious illegitimate eavesdroppers (Eves) that can also exploit the transmitted waveform to extract sensing information from the environment. In this paper, we investigate the beamforming design to enhance the sensing security of an ISAC system, where the communication user (CU) serves as a sensing Eve. Our objective is to maximize the mutual information (MI) for the legitimate radar sensing receiver while considering the constraint of the MI for the Eve and the quality of service to the CUs. Then, we consider the artificial noise (AN)-aided beamforming to further enhance the sensing security. Simulation results demonstrate that our proposed methods achieve MI improvement of the legitimate receiver while limiting the sensing MI of the Eve, compared with the baseline scheme, and that the utilization of AN further contributes to sensing security.
Rethinking Skip Connections in Spiking Neural Networks with Time-To-First-Spike Coding
Authors: Youngeun Kim, Adar Kahana, Ruokai Yin, Yuhang Li, Panos Stinis, George Em Karniadakis, Priyadarshini Panda
Date: 2023-12-01 00:00:00

Time-To-First-Spike (TTFS) coding in Spiking Neural Networks (SNNs) offers significant advantages in terms of energy efficiency, closely mimicking the behavior of biological neurons. In this work, we delve into the role of skip connections, a widely used concept in Artificial Neural Networks (ANNs), within the domain of SNNs with TTFS coding. Our focus is on two distinct types of skip connection architectures: (1) addition-based skip connections, and (2) concatenation-based skip connections. We find that addition-based skip connections introduce an additional delay in terms of spike timing. On the other hand, concatenation-based skip connections circumvent this delay but produce time gaps between after-convolution and skip connection paths, thereby restricting the effective mixing of information from these two paths. To mitigate these issues, we propose a novel approach involving a learnable delay for skip connections in the concatenation-based skip connection architecture. This approach successfully bridges the time gap between the convolutional and skip branches, facilitating improved information mixing. We conduct experiments on public datasets including MNIST and Fashion-MNIST, illustrating the advantage of the skip connection in TTFS coding architectures. Additionally, we demonstrate the applicability of TTFS coding on beyond image recognition tasks and extend it to scientific machine-learning tasks, broadening the potential uses of SNNs.
Optimal dividend payout with path-dependent drawdown constraint
Authors: Chonghu Guan, Jiacheng Fan, Zuo Quan Xu
Date: 2023-12-04 00:00:00

This paper studies an optimal dividend payout problem with drawdown constraint in a Brownian motion model, where the dividend payout rate must be no less than a fixed proportion of its historical running maximum. It is a stochastic control problem, where the admissible control depends on its past values, thus is path-dependent. The related Hamilton-Jacobi-Bellman equation turns out to be a new type of two-dimensional variational inequality with gradient constraint, which has only been studied by viscosity solution technique in the literature. In this paper, we use delicate PDE methods to obtain a strong solution. Different from the viscosity solution, based on our solution, we succeed in deriving an optimal feedback payout strategy, which is expressed in terms of two free boundaries and the running maximum surplus process. Furthermore, we have obtained many properties of the value function and the free boundaries such as the boundedness, continuity etc. Numerical examples are presented as well to verify our theoretical results and give some new but not proved financial insights.