pwc-archive/papers-with-abstracts huggingface dataset:
Near Field Computational Imaging with RIS Generated Virtual Masks
Authors: Yuhua Jiang, Feifei Gao, Yimin Liu, Shi Jin, Tiejun Cui
Date: 2023-04-23 00:00:00

Near field computational imaging has been recognized as a promising technique for non-destructive and highly accurate detection of the target. Meanwhile, reconfigurable intelligent surface (RIS) can flexibly control the scattered electromagnetic (EM) fields for sensing the target and can thus help computational imaging in the near field. In this paper, we propose a near-field imaging scheme based on holograghic aperture RIS. Specifically, we first establish an end-to-end EM propagation model from the perspective of Maxwell equations. To mitigate the inherent ill conditioning of the inverse problem in the imaging system, we design the EM field patterns as masks that help translate the inverse problem into a forward problem. Next, we utilize RIS to generate different virtual EM masks on the target surface and calculate the cross-correlation between the mask patterns and the electric field strength at the receiver. We then provide a RIS design scheme for virtual EM masks by employing a regularization technique. The cross-range resolution of the proposed method is analyzed based on the spatial spectrum of the generated masks. Simulation results demonstrate that the proposed method can achieve high-quality imaging. Moreover, the imaging quality can be improved by generating more virtual EM masks, by increasing the signal-to-noise ratio (SNR) at the receiver, or by placing the target closer to the RIS.
Maximum Spherical Mean Value (mSMV) Filtering for Whole Brain Quantitative Susceptibility Mapping
Authors: Alexandra G. Roberts, Dominick J. Romano, Mert Şişman, Alexey V. Dimov, Pascal Spincemaille, Thanh D. Nguyen, Ilhami Kovanlikaya, Susan A. Gauthier, Yi Wang
Date: 2023-04-22 00:00:00

To develop a tissue field filtering algorithm, called maximum Spherical Mean Value (mSMV), for reducing shadow artifacts in quantitative susceptibility mapping (QSM) of the brain without requiring brain tissue erosion. Residual background field is a major source of shadow artifacts in QSM. The mSMV algorithm filters large field values near the border, where the maximum value of the harmonic background field is located. The effectiveness of mSMV for artifact removal was evaluated by comparing with existing QSM algorithms in numerical brain simulation as well as using in vivo human data acquired from 11 healthy volunteers and 93 patients. Numerical simulation showed that mSMV reduces shadow artifacts and improves QSM accuracy. Better shadow reduction, as demonstrated by lower QSM variation in the gray matter and higher QSM image quality score, was also observed in healthy subjects and in patients with hemorrhages, stroke and multiple sclerosis. The mSMV algorithm allows QSM maps that are substantially equivalent to those obtained using SMV-filtered dipole inversion without eroding the volume of interest.
WATT-EffNet: A Lightweight and Accurate Model for Classifying Aerial Disaster Images
Authors: Gao Yu Lee, Tanmoy Dam, Md Meftahul Ferdaus, Daniel Puiu Poenar, Vu N. Duong
Date: 2023-04-21 00:00:00
Tasks: Classification, Computational Efficiency, image-classification, Image Classification, Management, Scene Classification

Incorporating deep learning (DL) classification models into unmanned aerial vehicles (UAVs) can significantly augment search-and-rescue operations and disaster management efforts. In such critical situations, the UAV's ability to promptly comprehend the crisis and optimally utilize its limited power and processing resources to narrow down search areas is crucial. Therefore, developing an efficient and lightweight method for scene classification is of utmost importance. However, current approaches tend to prioritize accuracy on benchmark datasets at the expense of computational efficiency. To address this shortcoming, we introduce the Wider ATTENTION EfficientNet (WATT-EffNet), a novel method that achieves higher accuracy with a more lightweight architecture compared to the baseline EfficientNet. The WATT-EffNet leverages width-wise incremental feature modules and attention mechanisms over width-wise features to ensure the network structure remains lightweight. We evaluate our method on a UAV-based aerial disaster image classification dataset and demonstrate that it outperforms the baseline by up to 15 times in terms of classification accuracy and 38.3% in terms of computing efficiency as measured by Floating Point Operations per second (FLOPs). Additionally, we conduct an ablation study to investigate the effect of varying the width of WATT-EffNet on accuracy and computational efficiency. Our code is available at \url{https://github.com/TanmDL/WATT-EffNet}.
Boosting multiple sclerosis lesion segmentation through attention mechanism
Authors: Alessia Rondinella, Elena Crispino, Francesco Guarnera, Oliver Giudice, Alessandro Ortis, Giulia Russo, Clara Di Lorenzo, Davide Maimone, Francesco Pappalardo, Sebastiano Battiato
Date: 2023-04-21 00:00:00
Tasks: Lesion Segmentation

Magnetic resonance imaging is a fundamental tool to reach a diagnosis of multiple sclerosis and monitoring its progression. Although several attempts have been made to segment multiple sclerosis lesions using artificial intelligence, fully automated analysis is not yet available. State-of-the-art methods rely on slight variations in segmentation architectures (e.g. U-Net, etc.). However, recent research has demonstrated how exploiting temporal-aware features and attention mechanisms can provide a significant boost to traditional architectures. This paper proposes a framework that exploits an augmented U-Net architecture with a convolutional long short-term memory layer and attention mechanism which is able to segment and quantify multiple sclerosis lesions detected in magnetic resonance images. Quantitative and qualitative evaluation on challenging examples demonstrated how the method outperforms previous state-of-the-art approaches, reporting an overall Dice score of 89% and also demonstrating robustness and generalization ability on never seen new test samples of a new dedicated under construction dataset.
Medical Image Deidentification, Cleaning and Compression Using Pylogik
Authors: Adrienne Kline, Vinesh Appadurai, Yuan Luo, Sanjiv Shah
Date: 2023-04-20 00:00:00
Tasks: De-identification, Text Detection

Leveraging medical record information in the era of big data and machine learning comes with the caveat that data must be cleaned and de-identified. Facilitating data sharing and harmonization for multi-center collaborations are particularly difficult when protected health information (PHI) is contained or embedded in image meta-data. We propose a novel library in the Python framework, called PyLogik, to help alleviate this issue for ultrasound images, which are particularly challenging because of the frequent inclusion of PHI directly on the images. PyLogik processes the image volumes through a series of text detection/extraction, filtering, thresholding, morphological and contour comparisons. This methodology de-identifies the images, reduces file sizes, and prepares image volumes for applications in deep learning and data sharing. To evaluate its effectiveness in processing ultrasound data, a random sample of 50 cardiac ultrasounds (echocardiograms) were processed through PyLogik, and the outputs were compared with the manual segmentations by an expert user. The Dice coefficient of the two approaches achieved an average value of 0.976. Next, an investigation was conducted to ascertain the degree of information compression achieved using the algorithm. Resultant data was found to be on average ~72% smaller after processing by PyLogik. Our results suggest that PyLogik is a viable methodology for data cleaning and de-identification, determining ROI, and file compression which will facilitate efficient storage, use, and dissemination of ultrasound data. Variants of the pipeline have also been created for use with other medical imaging data types.
Artificial Intelligence for Satellite Communication and Non-Terrestrial Networks: A Survey
Authors: G. Fontanesi, F. Ortíz, E. Lagunas, V. Monzon Baeza, M. Á. Vázquez, J. A. Vásquez-Peralvo, M. Minardi, H. N. Vu, P. J. Honnaiah, C. Lacoste, Y. Drif, T. S. Abdu, G. Eappen, J. Rehman, L. M. Garcés-Socorrás, W. A. Martins, P. Henarejos, H. Al-Hraishawi, J. C. Merlano Duncan, T. X. Vu, S. Chatzinotas
Date: 2023-04-25 00:00:00

This paper surveys the application and development of Artificial Intelligence (AI) in Satellite Communication (SatCom) and Non-Terrestrial Networks (NTN). We first present a comprehensive list of use cases, the relative challenges and the main AI tools capable of addressing those challenges. For each use case, we present the main motivation, a system description, the available non-AI solutions and the potential benefits and available works using AI. We also discuss the pros and cons of an on-board and on-ground AI-based architecture, and we revise the current commercial and research activities relevant to this topic. Next, we describe the state-of-the-art hardware solutions for developing ML in real satellite systems. Finally, we discuss the long-term developments of AI in the SatCom and NTN sectors and potential research directions. This paper provides a comprehensive and up-to-date overview of the opportunities and challenges offered by AI to improve the performance and efficiency of NTNs.
Multi-Objective Optimization for 3D Placement and Resource Allocation in OFDMA-based Multi-UAV Networks
Authors: Asad Mahmood, Thang X. Vu, Shree Krishna Sharma, Symeon Chatzinotas, Björn Ottersten
Date: 2023-04-25 00:00:00

This work considers the orthogonal frequency division multiple access (OFDMA) technology that enables multiple unmanned aerial vehicles (multi-UAV) communication systems to provide on-demand services. The main aim of this work is to derive the optimal allocation of radio resources, 3D placement of UAVs, and user association matrices. To achieve the desired objectives, we decoupled the original joint optimization problem into two sub-problems: i) 3D placement and user association and ii) sum-rate maximization for optimal radio resource allocation, which are solved iteratively. The proposed iterative algorithm is shown via numerical results to achieve fast convergence speed after less than 10 iterations. The benefits of the proposed design are demonstrated via superior sum-rate performance compared to existing reference designs. Moreover, the results declared that the optimal power and sub-carrier allocation helped mitigate the co-cell interference that directly impacts the system's performance.
BanditLinQ: A Scalable Link Scheduling for Dense D2D Networks with One-Bit Feedback
Authors: Daeun Kim, Namyoon Lee
Date: 2023-04-25 00:00:00
Tasks: Scheduling

This paper addresses cooperative link scheduling problems for base station (BS) aided device-to-device (D2D) communications using limited channel state information (CSI) at BS. We first derive the analytical form of ergodic sum-spectral efficiency as a function of network parameters, assuming statistical CSI at the BS. However, the optimal link scheduling, which maximizes the ergodic sum-spectral efficiency, becomes computationally infeasible when network density increases. To overcome this challenge, we present a low-complexity link scheduling algorithm that divides the D2D network into sub-networks and identifies the optimal link scheduling strategy per sub-network. Furthermore, we consider the scenario when the statistical CSI is not available to the BS. In such cases, we propose a quasi-optimal scalable link scheduling algorithm that utilizes one-bit feedback information from D2D receivers. The algorithm clusters the links and applies the UCB algorithm per cluster using the collected one-bit feedback information. We highlight that even with reduced scheduling complexity, the proposed algorithm identifies a link scheduling action that ensures optimality within a constant throughput gap. We also demonstrate through simulations that the proposed algorithm achieves higher sum-spectral efficiency than the existing link scheduling algorithms, even without explicit CSI or network parameters knowledge.
Environment-Aware Codebook for Reconfigurable Intelligent Surface-Aided MISO Communications
Authors: Xing Jia, Jiancheng An, Hao liu, Hongshu Liao, Lu Gan, Chau Yuen
Date: 2023-04-24 00:00:00

Reconfigurable intelligent surface (RIS) is a revolutionary technology that can customize the wireless channel and improve the energy efficiency of next-generation cellular networks. This letter proposes an environment-aware codebook design by employing the statistical channel state information (CSI) for RIS-assisted multiple-input single-output (MISO) systems. Specifically, first of all, we generate multiple virtual channels offline by utilizing the location information and design an environment-aware reflection coefficient codebook. Thus, we only need to estimate the composite channel and optimize the active transmit beamforming for each reflection coefficient in the pre-designed codebook, while simplifying the reflection optimization substantially. Moreover, we analyze the theoretical performance of the proposed scheme. Finally, numerical results verify the performance benefits of the proposed scheme over the cascaded channel estimation and passive beamforming as well as the existing codebook scheme in the face of channel estimation errors, albeit its significantly reduced overhead and complexity.
A Closed-form Expression for the Gaussian Noise Model in the Presence of Raman Amplification
Authors: H. Buglia, M. Jarmolovicius, L. Galdino, R. I. Killey, P. Bayvel
Date: 2023-04-24 00:00:00
Tasks: Form, Numerical Integration

A closed-form model for the nonlinear interference (NLI) in Raman amplified links is presented, the formula accounts for both forward (FW) and backward (BW) pumping schemes and inter-channel stimulated Raman scattering (ISRS) effect. The formula also accounts for an arbitrary number of pumps, wavelength-dependent fibre parameters, launch-power profiles, and is tested over a distributed Raman-amplified system setup. The formula is suitable for ultra-wideband (UWB) optical transmission systems and is applied in a signal with 13~THz optical bandwidth corresponding to transmission over the S-, C-, and L- band. The accuracy of the closed-form formula is validated through comparison with numerical integration of the Gaussian noise (GN) model and split-step Fourier method (SSFM) simulations in a point-to-point transmission link.
Rapidly time-varying reconfigurable intelligent surfaces for downlink multiuser transmissions
Authors: Francesco Verde, Donatella Darsena, Vincenzo Galdi
Date: 2023-04-24 00:00:00
Tasks: Fairness, Scheduling

Until now, researchers in wireless communications have mainly focused their attention on slowly time-varying designs of reconfigurable intelligent surfaces (RISs), where the spatial-phase gradient across the RIS is varied at the rate equal to the inverse of the channel coherence time. Additional degrees of freedom for controlling EM waves can be gained by applying a time modulation to the reflection response of RISs during the channel coherence time interval, thereby attaining rapidly time-varying RISs. In this paper, we develop a general framework where a downlink multiuser transmission over single-input single-output slow fading channels is assisted by a digitally controlled rapidly time-varying RIS. We show that reconfiguring the RIS at a rate greater than the inverse of the channel coherence time might be beneficial from a communication perspective depending on the considered network utility function and the available channel state information at the transmitter (CSIT). The conclusions of our analysis in terms of system design guidelines are as follows: (i) if the network utility function is the sum-rate time-averaged network capacity, without any constraint on fair resource allocation, and full CSIT is available, it is unnecessary to change the electronic properties of the RIS within the channel coherence time interval; (ii) if partial CSIT is assumed only, a rapidly time-varying randomized RIS allows to achieve a suitable balance between sum-rate time-averaged capacity and user fairness, especially for a sufficiently large number of users; (iii) regardless of the available amount of CSIT, the design of rapid temporal variations across the RIS is instrumental for developing scheduling algorithms aimed at maximizing the network capacity subject to some fairness constraints.
Variational Bayesian Multiuser Tracking for Reconfigurable Intelligent Surface Aided MIMO-OFDM Systems
Authors: Boyu Teng, Xiaojun Yuan, Rui Wang
Date: 2023-04-24 00:00:00

Reconfigurable intelligent surface (RIS) has attracted enormous interest for its potential advantages in assisting both wireless communication and environmental sensing. In this paper, we study a challenging multiuser tracking problem in the multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) system aided by multiple RISs. In particular, we assume that a multi-antenna base station (BS) receives the OFDM symbols from single-antenna users reflected by multiple RISs and tracks the positions of these users. Considering the users' mobility and the blockage of light-of-sight (LoS) paths, we establish a probability transition model to characterize the tracking process, where the geometric constraints between channel parameters and multiuser positions are utilized. We further develop an online message passing algorithm, termed the Bayesian multiuser tracking (BMT) algorithm, to estimate the multiuser positions, the angles-of-arrivals (AoAs) at multiple RISs, and the time delay and the blockage of the LoS path. The Bayesian Cramer Rao bound (BCRB) is derived as the fundamental performance limit of the considered tracking problem. Based on the BCRB, we optimize the passive beamforming (PBF) of the multiple RISs to improve the tracking performance. Simulation results show that the proposed PBF design significantly outperforms the counterpart schemes, and our BMT algorithm can achieve up to centimeter-level tracking accuracy.
Dynamic Compressive Sensing based on RLS for Underwater Acoustic Communications
Authors: Zhen Qin
Date: 2023-04-24 00:00:00
Tasks: Compressive Sensing

Sparse structures are widely recognized and utilized in channel estimation. Two typical mechanisms, namely proportionate updating (PU) and zero-attracting (ZA) techniques, achieve better performance, but their computational complexity are higher than non-sparse counterparts. In this paper, we propose a DCS technique based on the recursive least squares (RLS) algorithm which can simultaneously achieve improved performance and reduced computational complexity. Specifically, we develop the sparse adaptive subspace pursuit-improved RLS (SpAdSP-IRLS) algorithm by updating only the sparse structure in the IRLS to track significant coefficients. The complexity of the SpAdSP-IRLS algorithm is successfully reduced to $\mathcal{O}(L^2+2L(s+1)+10s)$, compared with the order of $\mathcal{O}(3L^2+4L)$ for the standard RLS. Here, $L$ represents the length of the channel, and $s$ represents the size of the support set. Our experiments on both synthetic and real data show the superiority of the proposed SpAdSP-IRLS, even though only $s$ elements are updated in the channel estimation.
Performance Comparison of Numerical Optimization Algorithms for RSS-TOA-Based Target Localization
Authors: Halim Lee, Jiwon Seo
Date: 2023-04-24 00:00:00

The maximum likelihood (ML) estimator can be applied to localize a target mobile device using the RSS and TOA. However, the ML estimator for the RSS-TOA-based target localization problem is nonconvex and nonlinear, having no analytical solution. Therefore, the ML estimator should be solved numerically, unless it is relaxed into a convex or linear form. This study investigates the target localization performance and computational complexity of numerical methods for solving an ML estimator. The three widely used numerical methods are: grid search, gradient descent, and particle swarm optimization. In the experimental evaluation, the grid search yielded the lowest target localization root-mean-squared error; however, the 95th percentile error of the grid search was larger than those of the other two algorithms. The average code computation time of the grid search was extremely large compared with those of the other two algorithms, and gradient descent exhibited the lowest computation time.
Co-GRU Enhanced End-to-End Design for Long-haul Coherent Transmission Systems
Authors: Jiayu Zheng, Tianhong Zhang, Yu Wenjing, Weiqin Zhou, Chuanchuan Yang, Fan Zhang
Date: 2023-04-23 00:00:00
Tasks: Decoder

In recent years, the end-to-end (E2E) scheme based on deep learning (DL) has been proposed as a potential scheme to jointly optimize the encoder and the decoder parameters of the optical communication system. Compared with conventional deep neural network (DNN) adopted in E2E design, center-oriented Gated Recurrent Unit (Co-GRU) network has the ability to learn and compensate for inter-symbol interference (ISI) with low computation cost while satisfying the gradient backpropagation (BP) condition. In this paper, the Co-GRU structure is adopted for both channel modeling and decoder implementation in E2E design for long-haul coherent wavelength division multiplexing (WDM) transmission systems, which can enhance the performance of general mutual information (GMI) and Q2-factor. For the E2E system with Co-GRU based decoder, the gain of GMI and Q2-factor are respectively improved 0.2 bits/sym and 0.48dB, compared to that of the conventional QAM system, for a 5-channel dual-polarization coherent system transmitting over 960km standard single mode fiber (SSMF). This work paves the way for further study of the application of the Co-GRU structure for both the data-driven channel modeling and the decoder performance improvement in E2E design.
CSI-Based Data-driven Localization Frameworking using Small-scale Training Datasets in Single-site MIMO Systems
Authors: Katarina Vuckovic, Farzam Hejazi, Nazanin Rahnavard
Date: 2023-04-22 00:00:00
Tasks: GPR

This work presents a date-driven user localization framework for single-site massive Multiple-Input-Multiple-Output (MIMO) systems. The framework is trained on a geo-tagged Channel State Information (CSI) dataset. Unlike the state-of-the-art Convolutional Neural Network (CNN) models, which require large training datasets to perform well, our method is specifically designed to operate with small-scale training datasets. This makes our approach more practical for real-world scenarios, where collecting a large amount of data can be challenging. Our proposed FC-AE-GPR framework combines two components: a Fully-Connected Auto-Encoder (FC-AE) and a Gaussian Process Regression (GPR) model. Our results show that the GPR model outperforms the CNN model when presented with small training datasets. However, the training complexity of GPR models can become an issue when the input sample size is large. To address this, we propose using the FC-AE to reduce the sample size by encoding the CSI before training the GPR model. Although the FC-AE model may require a larger training dataset initially, we demonstrate that the FC-AE is scenario independent. This means that it can be utilized in new and unseen scenarios without prior retraining. Therefore, adapting the FC-AE-GPR model to a new scenario requires only retraining the GPR model with a small training dataset.