pwc-archive/papers-with-abstracts huggingface dataset:
Intelligent Resource Allocation for UAV-Based Cognitive NOMA Networks: An Active Inference Approach
Authors: Felix Obite, Ali Krayani, Atm S. Alam, Lucio Marcenaro, Arumugam Nallanathan, Carlo Regazzoni
Date: 2023-10-17 00:00:00
Tasks: Decision Making

Future wireless networks will need to improve adaptive resource allocation and decision-making to handle the increasing number of intelligent devices. Unmanned aerial vehicles (UAVs) are being explored for their potential in real-time decision-making. Moreover, cognitive non-orthogonal multiple access (Cognitive-NOMA) is envisioned as a remedy to address spectrum scarcity and enable massive connectivity. This paper investigates the design of joint subchannel and power allocation in an uplink UAV-based cognitive NOMA network. We aim to maximize the cumulative sum rate by jointly optimizing the subchannel and power allocation based on the UAV's mobility at each time step. This is often formulated as an optimization problem with random variables. However, conventional optimization algorithms normally introduce significant complexity, and machine learning methods often rely on large but partially representative datasets to build solution models, assuming stationary testing data. Consequently, inference strategies for non stationary events are often overlooked. In this study, we introduce a novel active inference-based learning approach, rooted in cognitive neuroscience, to solve this complex problem. The framework involves creating a training dataset using random or iterative methods to find suboptimal resource allocations. This dataset trains a mobile UAV offline, enabling it to learn a generative model of discrete subchannels and continuous power allocation. The UAV then uses this model for online inference. The method incrementally derives new generative models from training data by identifying dynamic equilibrium conditions between required actions and variables, represented within a unique dynamic Bayesian network. The proposed approach is validated through numerical simulations, showing efficient performance compared to suboptimal baseline schemes.
Aerial-Aided mmWave VANETs Using NOMA: Performance Analysis, Comparison, and Insights
Authors: Abdullah Abu Zaid, Baha Eddine Youcef Belmekki, Mohamed-Slim Alouini
Date: 2023-10-17 00:00:00

In this paper, we propose the integration of tethered flying platforms in cooperative vehicular ad hoc networks (VANETs) to alleviate the problems of rapid urbanization. In this context, we study the performance of VANETs by deriving approximate outage probability and average achievable rate expressions using tools from stochastic geometry. We compare between the usage of networked tethered flying platforms (NTFPs) and traditional roadside units (RSUs). On the other hand, the rapid increase of smart devices in vehicles and the upcoming urban air mobility (UAM) vision will congest the spectrum and require increased data rates. Hence, we use non-orthogonal multiple access (NOMA) to improve spectral efficiency and compare its performance to orthogonal access schemes. Furthermore, we utilize millimeter-wave (mmWave) frequencies for high data rates and implement a sectored beamforming model. We extensively study the system using three transmission schemes: direct, relay, and hybrid transmission. The results show that when acting as relays, NTFPs outperform RSUs for larger distances between the transmitting and the receiving vehicles, while RSUs outperform NTFPs for short distances. However, NTFPs are the best solution when acting as a source. Moreover, we find that, in most cases, direct transmission is preferred to achieve a high rate compared to other schemes. Finally, the results are summarized in two tables that provide insights into connecting VANETs by selecting the most suitable platform and type of communication for a given set of parameters, configurations, and requirements.
Channel Autocorrelation Estimation for IRS-Aided Wireless Communications Based on Power Measurements
Authors: Ge Yan, Lipeng Zhu, Rui Zhang
Date: 2023-10-17 00:00:00

Intelligent reflecting surface (IRS) can bring significant performance enhancement for wireless communication systems by reconfiguring wireless channels via passive signal reflection. However, such performance improvement generally relies on the knowledge of channel state information (CSI) for IRS-associated links. Prior IRS channel estimation strategies mainly estimate IRS-cascaded channels based on the excessive pilot signals received at the users/base station (BS) with time-varying IRS reflections, which, however, are not compatible with the existing channel training/estimation protocol for cellular networks. To address this issue, we propose in this paper a new channel estimation scheme for IRS-assisted communication systems based on the received signal power measured at the user, which is practically attainable without the need of changing the current protocol. Specifically, due to the lack of signal phase information in power measurements, the autocorrelation matrix of the BS-IRS-user cascaded channel is estimated by solving equivalent matrix-rank-minimization problems. Simulation results are provided to verify the effectiveness of the proposed channel estimation algorithm as well as the IRS passive reflection design based on the estimated channel autocorrelation matrix.
Multiscale Fusion for Abnormality Detection and Localization of Distributed Parameter Systems
Authors: Peng Wei, Han-Xiong Li
Date: 2023-10-10 00:00:00
Tasks: Anomaly Detection, Fault Detection

Numerous industrial thermal processes and fluid processes can be described by distributed parameter systems (DPSs), wherein many process parameters and variables vary in space and time. Early internal abnormalities in the DPS may develop into uncontrollable thermal failures, causing serious safety incidents. In this study, the multiscale information fusion is proposed for internal abnormality detection and localization of DPSs under different scenarios. We introduce the dissimilarity statistic as a means to identify anomalies for lumped variables, whereas spatial and temporal statistic measures are presented for the anomaly detection for distributed variables. Through appropriate parameter optimization, these statistic functions are integrated into the comprehensive multiscale detection index, which outperforms traditional single-scale detection methods. The proposed multiscale statistic has good physical interpretability from the system disorder degree. Experiments on the internal short circuit (ISC) of a battery system have demonstrated that our proposed method can swiftly identify ISC abnormalities and accurately pinpoint problematic battery cells under various working conditions.
Implicit Representation of GRAPPA Kernels for Fast MRI Reconstruction
Authors: Daniel Abraham, Mark Nishimura, Xiaozhi Cao, Congyu Liao, Kawin Setsompop
Date: 2023-10-16 00:00:00
Tasks: Image Reconstruction, MRI Reconstruction

MRI data is acquired in Fourier space/k-space. Data acquisition is typically performed on a Cartesian grid in this space to enable the use of a fast Fourier transform algorithm to achieve fast and efficient reconstruction. However, it has been shown that for multiple applications, non-Cartesian data acquisition can improve the performance of MR imaging by providing fast and more efficient data acquisition, and improving motion robustness. Nonetheless, the image reconstruction process of non-Cartesian data is more involved and can be time-consuming, even through the use of efficient algorithms such as non-uniform FFT (NUFFT). Reconstruction complexity is further exacerbated when imaging in the presence of field imperfections. This work (implicit GROG) provides an efficient approach to transform the field corrupted non-Cartesian data into clean Cartesian data, to achieve simpler and faster reconstruction which should help enable non-Cartesian data sampling to be performed more widely in MRI.
Constant Modulus Waveform Design with Block-Level Interference Exploitation for DFRC Systems
Authors: Byunghyun Lee, Anindya Bijoy Das, David J. Love, Christopher G. Brinton, James V. Krogmeier
Date: 2023-10-16 00:00:00

Dual-functional radar-communication (DFRC) is a promising technology where radar and communication functions operate on the same spectrum and hardware. In this paper, we propose an algorithm for designing constant modulus waveforms for DFRC systems. Particularly, we jointly optimize the correlation properties and the spatial beam pattern. For communication, we employ constructive interference-based block-level precoding (CI-BLP) to exploit distortion due to multi-user and radar transmission. We propose a majorization-minimization (MM)-based solution to the formulated problem. To accelerate convergence, we propose an improved majorizing function that leverages a novel diagonal matrix structure. We then evaluate the performance of the proposed algorithm through rigorous simulations. Simulation results demonstrate the effectiveness of the proposed approach and the proposed majorizer.
Indoor Wireless Signal Modeling with Smooth Surface Diffraction Effects
Authors: Ruichen Wang, Samuel Audia, Dinesh Manocha
Date: 2023-10-16 00:00:00

We present a novel algorithm that enhances the accuracy of electromagnetic field simulations in indoor environments by incorporating the Uniform Geometrical Theory of Diffraction (UTD) for surface diffraction. This additional diffraction phenomenology is important for the design of modern wireless systems and allows us to capture the effects of more complex scene geometries. Central to our methodology is the Dynamic Coherence-Based EM Ray Tracing Simulator (DCEM), and we augment that formulation with smooth surface UTD and present techniques to efficiently compute the ray paths. We validate our additions by comparing them to analytical solutions of a sphere, method of moments solutions from FEKO, and ray-traced indoor scenes from WinProp. Our algorithm improves shadow region predicted powers by about 5dB compared to our previous work, and captures nuanced field effects beyond shadow boundaries. We highlight the performance on different indoor scenes and observe 60% faster computation time over WinProp.
A Tutorial on Chirp Spread Spectrum for LoRaWAN: Basics and Key Advances
Authors: Alireza Maleki, Ha H. Nguyen, Ebrahim Bedeer, Robert Barton
Date: 2023-10-16 00:00:00

Chirps spread spectrum (CSS) modulation is the heart of long-range (LoRa) modulation used in the context of long-range wide area network (LoRaWAN) in internet of things (IoT) scenarios. Despite being a proprietary technology owned by Semtech Corp., LoRa modulation has drawn much attention from the research and industry communities in recent years. However, to the best of our knowledge, a comprehensive tutorial, investigating the CSS modulation in the LoRaWAN application, is missing in the literature. Therefore, in the first part of this paper, we provide a thorough analysis and tutorial of CSS modulation modified by LoRa specifications, discussing various aspects such as signal generation, detection, error performance, and spectral characteristics. Moreover, a summary of key recent advances in the context of CSS modulation applications in IoT networks is presented in the second part of this paper under four main categories of transceiver configuration and design, data rate improvement, interference modeling, and synchronization algorithms.
Performance Analysis of a Low-Complexity OTFS Integrated Sensing and Communication System
Authors: Tommaso Bacchielli, Lorenzo Pucci, Enrico Paolini, Andrea Giorgetti
Date: 2023-10-16 00:00:00
Tasks: Integrated sensing and communication, ISAC

This work proposes a low-complexity estimation approach for an orthogonal time frequency space (OTFS)-based integrated sensing and communication (ISAC) system. In particular, we first define four low-dimensional matrices used to compute the channel matrix through simple algebraic manipulations. Secondly, we establish an analytical criterion, independent of system parameters, to identify the most informative elements within these derived matrices, leveraging the properties of the Dirichlet kernel. This allows the distilling of such matrices, keeping only those entries that are essential for detection, resulting in an efficient, low-complexity implementation of the sensing receiver. Numerical results, which refer to a vehicular scenario, demonstrate that the proposed approximation technique effectively preserves the sensing performance, evaluated in terms of root mean square error (RMSE) of the range and velocity estimation, while concurrently reducing the computational effort enormously.
Kronecker-structured Sparse Vector Recovery with Application to IRS-MIMO Channel Estimation
Authors: Yanbin He, Geethu Joseph
Date: 2023-10-11 00:00:00
Tasks: Attribute, Denoising

This paper studies the problem of Kronecker-structured sparse vector recovery from an underdetermined linear system with a Kronecker-structured dictionary. Such a problem arises in many real-world applications such as the sparse channel estimation of an intelligent reflecting surface-aided multiple-input multiple-output system. The prior art only exploits the Kronecker structure in the support of the sparse vector and solves the entire linear system together leading to high computational complexity. Instead, we break down the original sparse recovery problem into multiple independent sub-problems and solve them individually. We obtain the sparse vector as the Kronecker product of the individual solutions, retaining its structure in both support and nonzero entries. Our simulations demonstrate the superior performance of our methods in terms of accuracy and run time compared with the existing works, using synthetic data and the channel estimation application. We attribute the low run time to the reduced solution space due to the additional structure and improved accuracy to the denoising effect owing to the decomposition step.
Soft Demodulator for Symbol-Level Precoding in Coded Multiuser MISO Systems
Authors: Yafei Wang, Hongwei Hou, Wenjin Wang, Xinping Yi, Shi Jin
Date: 2023-10-16 00:00:00

In this paper, we consider symbol-level precoding (SLP) in channel-coded multiuser multi-input single-output (MISO) systems. It is observed that the received SLP signals do not always follow Gaussian distribution, rendering the conventional soft demodulation with the Gaussian assumption unsuitable for the coded SLP systems. It, therefore, calls for novel soft demodulator designs for non-Gaussian distributed SLP signals with accurate log-likelihood ratio (LLR) calculation. To this end, we first investigate the non-Gaussian characteristics of both phase-shift keying (PSK) and quadrature amplitude modulation (QAM) received signals with existing SLP schemes and categorize the signals into two distinct types. The first type exhibits an approximate-Gaussian distribution with the outliers extending along the constructive interference region (CIR). In contrast, the second type follows some distribution that significantly deviates from the Gaussian distribution. To obtain accurate LLR, we propose the modified Gaussian soft demodulator and Gaussian mixture model (GMM) soft demodulators to deal with two types of signals respectively. Subsequently, to further reduce the computational complexity and pilot overhead, we put forward a novel neural soft demodulator, named pilot feature extraction network (PFEN), leveraging the transformer mechanism in deep learning. Simulation results show that the proposed soft demodulators dramatically improve the throughput of existing SLPs for both PSK and QAM transmission in coded systems.
A Low Complexity Block-oriented Functional Link Adaptive Filtering Algorithm
Authors: Pavankumar Ganjimala, Subrahmanyam Mula
Date: 2023-10-16 00:00:00

The high computation complexity of nonlinear adaptive filtering algorithms poses significant challenges at the hardware implementation level. In order to tackle the computational complexity problem, this paper proposes a novel block-oriented functional link adaptive filter (BO-FLAF) to model memoryless nonlinear systems. Through theoretical complexity analysis, we show that the proposed Hammerstein BO trigonometric FLAF (HBO-TFLAF) has 47% lesser multiplications than the original TFLAF for a filter order of 1024. Moreover, the HBO-TFLAF exhibits a faster convergence rate and achieved 3-5 dB lesser steady-state mean square error (MSE) compared to the original TFLAF for a memoryless nonlinear system identification task.
Hierarchical MTC User Activity Detection and Channel Estimation with Unknown Spatial Covariance
Authors: Hamza Djelouat, Mikko J. Sillanpää, Markus Leinonen, Markku Juntti
Date: 2023-10-16 00:00:00
Tasks: Action Detection, Activity Detection, Bayesian Inference, User Identification

This paper addresses the joint user identification and channel estimation (JUICE) problem in machine-type communications under the practical spatially correlated channels model with unknown covariance matrices. Furthermore, we consider an MTC network with hierarchical user activity patterns following an event-triggered traffic mode. Therein the users are distributed over clusters with a structured sporadic activity behaviour that exhibits both cluster-level and intra-cluster sparsity patterns. To solve the JUICE problem, we first leverage the concept of strong priors and propose a hierarchical-sparsity-inducing spike-and-slab prior to model the structured sparse activity pattern. Subsequently, we derive a Bayesian inference scheme by coupling the expectation propagation (EP) algorithm with the expectation maximization (EM) framework. Second, we reformulate the JUICE as a maximum a posteriori (MAP) estimation problem and propose a computationally-efficient solution based on the alternating direction method of multipliers (ADMM). More precisely, we relax the strong spike-and-slab prior with a cluster-sparsity-promoting prior based on the long-sum penalty. We then derive an ADMM algorithm that solves the MAP problem through a sequence of closed-form updates. Numerical results highlight the significant performance significant gains obtained by the proposed algorithms, as well as their robustness against various assumptions on the users sparse activity behaviour.
Semi-Supervised End-to-End Learning for Integrated Sensing and Communications
Authors: José Miguel Mateos-Ramos, Baptiste Chatelier, Christian Häger, Musa Furkan Keskin, Luc Le Magoarou, Henk Wymeersch
Date: 2023-10-15 00:00:00
Tasks: ISAC, Position

Integrated sensing and communications (ISAC) is envisioned as one of the key enablers of next-generation wireless systems, offering improved hardware, spectral, and energy efficiencies. In this paper, we consider an ISAC transceiver with an impaired uniform linear array that performs single-target detection and position estimation, and multiple-input single-output communications. A differentiable model-based learning approach is considered, which optimizes both the transmitter and the sensing receiver in an end-to-end manner. An unsupervised loss function that enables impairment compensation without the need for labeled data is proposed. Semi-supervised learning strategies are also proposed, which use a combination of small amounts of labeled data and unlabeled data. Our results show that semi-supervised learning can achieve similar performance to supervised learning with 98.8% less required labeled data.
Enhance Security of Time-Modulated Array-Enabled Directional Modulation by Introducing Symbol Ambiguity
Authors: Zhihao Tao, Zhaoyi Xu, Athina Petropulu
Date: 2023-10-15 00:00:00

In this paper, if the time-modulated array (TMA)-enabled directional modulation (DM) communication system can be cracked is investigated and the answer is YES! We first demonstrate that the scrambling data received at the eavesdropper can be defied by using grid search to successfully find the only and actual mixing matrix generated by TMA. Then, we propose introducing symbol ambiguity to TMA to defend the defying of grid search, and design two principles for the TMA mixing matrix, i.e., rank deficiency and non-uniqueness of the ON-OFF switching pattern, that can be used to construct the symbol ambiguity. Also, we present a feasible mechanism to implement these two principles. Our proposed principles and mechanism not only shed light on how to design a more secure TMA DM system theoretically in the future, but also have been validated to be effective by bit error rate measurements.
Towards Structural Sparse Precoding: Dynamic Time, Frequency, Space, and Power Multistage Resource Programming
Authors: Zhongxiang Wei, Ping Wang, Qingjiang Shi, Xu Zhu, Christos Masouros
Date: 2023-10-15 00:00:00

In last decades, dynamic resource programming in partial resource domains has been extensively investigated for single time slot optimizations. However, with the emerging real-time media applications in fifth-generation communications, their new quality of service requirements are often measured in temporal dimension. This requires multistage optimization for full resource domain dynamic programming. Taking experience rate as a typical temporal multistage metric, we jointly optimize time, frequency, space and power domains resource for multistage optimization. To strike a good tradeoff between system performance and computational complexity, we first transform the formulated mixed integer non-linear constraints into equivalent convex second order cone constraints, by exploiting the coupling effect among the resources. Leveraging the concept of structural sparsity, the objective of max-min experience rate is given as a weighted 1-norm term associated with the precoding matrix. Finally, a low-complexity iterative algorithm is proposed for full resource domain programming, aided by another simple conic optimization for obtaining its feasible initial result. Simulation verifies that our design significantly outperform the benchmarks while maintaining a fast convergence rate, shedding light on full domain dynamic resource programming of multistage optimizations.
On the Radio Stripe Deployment for Indoor RF Wireless Power Transfer
Authors: Amirhossein Azarbahram, Onel L. A. Lopez, Petar Popovski, Matti Latva-aho
Date: 2023-10-14 00:00:00

One of the primary goals of future wireless systems is to foster sustainability, for which, radio frequency (RF) wireless power transfer (WPT) is considered a key technology enabler. The key challenge of RF-WPT systems is the extremely low end-to-end efficiency, mainly due to the losses introduced by the wireless channel. Distributed antenna systems are undoubtedly appealing as they can significantly shorten the charging distances, thus, reducing channel losses. Interestingly, radio stripe systems provide a cost-efficient and scalable way to deploy a distributed multi-antenna system, and thus have received a lot of attention recently. Herein, we consider an RF-WPT system with a transmit radio stripe network to charge multiple indoor energy hotspots, i.e., spatial regions where the energy harvesting devices are expected to be located, including near-field locations. We formulate the optimal radio stripe deployment problem aimed to maximize the minimum power received by the users and explore two specific predefined shapes, namely the straight line and polygon-shaped configurations. Then, we provide efficient solutions relying on geometric programming to optimize the location of the radio stripe elements. The results demonstrate that the proposed radio stripe deployments outperform a central fully-digital square array with the same number of elements and utilizing larger radio stripe lengths can enhance the performance, while increasing the system frequency may degrade it.