pwc-archive/papers-with-abstracts huggingface dataset:
Global Positioning: the Uniqueness Question and a New Solution Method
Authors: Mireille Boutin, Gregor Kemper
Date: 2023-10-13 00:00:00
Tasks: All

We provide a new algebraic solution procedure for the global positioning problem in $n$ dimensions using $m$ satellites. We also give a geometric characterization of the situations in which the problem does not have a unique solution. This characterization shows that such cases can happen in any dimension and with any number of satellites, leading to counterexamples to some open conjectures. We fill a gap in the literature by giving a proof for the long-held belief that when $m \ge n+2$, the solution is unique for almost all user positions. Even better, when $m \ge 2n+2$, almost all satellite configurations will guarantee a unique solution for all user positions. Some of our results are obtained using tools from algebraic geometry.
Histogram-less LiDAR through SPAD response linearization
Authors: Alessandro Tontini, Sonia Mazzucchi, Roberto Passerone, Nicolò Broseghini, Leonardo Gasparini
Date: 2023-10-13 00:00:00

We present a new method to acquire the 3D information from a SPAD-based direct-Time-of-Flight (d-ToF) imaging system which does not require the construction of a histogram of timestamps and can withstand high flux operation regime. The proposed acquisition scheme emulates the behavior of a SPAD detector with no distortion due to dead time, and extracts the Tof information by a simple average operation on the photon timestamps ensuring ease of integration in a dedicated sensor and scalability to large arrays. The method is validated through a comprehensive mathematical analysis, whose predictions are in agreement with a numerical Monte Carlo model of the problem. Finally, we show the validity of the predictions in a real d-ToF measurement setup under challenging background conditions well beyond the typical pile-up limit of 5% detection rate up to a distance of 3.8 m.
Multi-Sensor Multi-Scan Radar Sensing of Multiple Extended Targets
Authors: Martin V. Vejling, Christophe A. N. Biscio, Petar Popovski
Date: 2023-10-13 00:00:00
Tasks: Clustering, State Estimation

We propose an efficient solution to the state estimation problem in multi-scan multi-sensor multiple extended target sensing scenarios. We first model the measurement process by a doubly inhomogeneous-generalized shot noise Cox process and then estimate the parameters using a jump Markov chain Monte Carlo sampling technique. The proposed approach scales linearly in the number of measurements and can take spatial properties of the sensors into account, herein, sensor noise covariance, detection probability, and resolution. Numerical experiments using radar measurement data suggest that the algorithm offers improvements in high clutter scenarios with closely spaced targets over state-of-the-art clustering techniques used in existing multiple extended target tracking algorithms.
A unified framework for STAR-RIS coefficients optimization
Authors: Hancheng Zhu, Yuanwei Liu, Yik Chung Wu, Vincent K. N. Lau
Date: 2023-10-13 00:00:00

Simultaneously transmitting and reflecting (STAR) reconfigurable intelligent surface (RIS), which serves users located on both sides of the surface, has recently emerged as a promising enhancement to the traditional reflective only RIS. Due to the lack of a unified comparison of communication systems equipped with different modes of STAR-RIS and the performance degradation caused by the constraints involving discrete selection, this paper proposes a unified optimization framework for handling the STAR-RIS operating mode and discrete phase constraints. With a judiciously introduced penalty term, this framework transforms the original problem into two iterative subproblems, with one containing the selection-type constraints, and the other subproblem handling other wireless resource. Convergent point of the whole algorithm is found to be at least a stationary point under mild conditions. As an illustrative example, the proposed framework is applied to a sum-rate maximization problem in the downlink transmission. Simulation results show that the algorithms from the proposed framework outperform other existing algorithms tailored for different STAR-RIS scenarios. Furthermore, it is found that 4 or even 2 discrete phases STAR-RIS could achieve almost the same sum-rate performance as the continuous phase setting, showing for the first time that discrete phase is not necessarily a cause of significant performance degradation.
A Two-Stage 2D Channel Extrapolation Scheme for TDD 5G NR Systems
Authors: Yubo Wan, An Liu
Date: 2023-10-13 00:00:00
Tasks: parameter estimation

Recently, channel extrapolation has been widely investigated in frequency division duplex (FDD) massive MIMO systems. However, in time division duplex (TDD) fifth generation (5G) new radio (NR) systems, the channel extrapolation problem also arises due to the hopping uplink pilot pattern, which has not been fully researched yet. This paper addresses this gap by formulating a channel extrapolation problem in TDD massive MIMO-OFDM systems for 5G NR, incorporating imperfection factors. A novel two-stage two-dimensional (2D) channel extrapolation scheme in both frequency and time domain is proposed, designed to mitigate the negative effects of imperfection factors and ensure high-accuracy channel estimation. Specifically, in the channel estimation stage, we propose a novel multi-band and multi-timeslot based high-resolution parameter estimation algorithm to achieve 2D channel extrapolation in the presence of imperfection factors. Then, to avoid repeated multi-timeslot based channel estimation, a channel tracking stage is designed during the subsequent time instants, in which a sparse Markov channel model is formulated to capture the dynamic sparsity of massive MIMO-OFDM channels under the influence of imperfection factors. Next, an expectation-maximization (EM) based compressive channel tracking algorithm is designed to jointly estimate unknown imperfection and channel parameters by exploiting the high-resolution prior information of the delay/angle parameters from the previous timeslots. Simulation results underscore the superior performance of our proposed channel extrapolation scheme over baselines.
Spiking Semantic Communication for Feature Transmission with HARQ
Authors: Mengyang Wang, Jiahui Li, Mengyao Ma, Xiaopeng Fan
Date: 2023-10-13 00:00:00
Tasks: Semantic Communication

In Collaborative Intelligence (CI), the Artificial Intelligence (AI) model is divided between the edge and the cloud, with intermediate features being sent from the edge to the cloud for inference. Several deep learning-based Semantic Communication (SC) models have been proposed to reduce feature transmission overhead and mitigate channel noise interference. Previous research has demonstrated that Spiking Neural Network (SNN)-based SC models exhibit greater robustness on digital channels compared to Deep Neural Network (DNN)-based SC models. However, the existing SNN-based SC models require fixed time steps, resulting in fixed transmission bandwidths that cannot be adaptively adjusted based on channel conditions. To address this issue, this paper introduces a novel SC model called SNN-SC-HARQ, which combines the SNN-based SC model with the Hybrid Automatic Repeat Request (HARQ) mechanism. SNN-SC-HARQ comprises an SNN-based SC model that supports the transmission of features at varying bandwidths, along with a policy model that determines the appropriate bandwidth. Experimental results show that SNN-SC-HARQ can dynamically adjust the bandwidth according to the channel conditions without performance loss.
A Framework for Developing and Evaluating Algorithms for Estimating Multipath Propagation Parameters from Channel Sounder Measurements
Authors: Akbar Sayeed, Damla Guven, Michael Doebereiner, Sebastian Semper, Camillo Gentile, Anuraag Bodi, Zihang Cheng
Date: 2023-10-12 00:00:00

A framework is proposed for developing and evaluating algorithms for extracting multipath propagation components (MPCs) from measurements collected by channel sounders at millimeter-wave frequencies. Sounders equipped with an omnidirectional transmitter and a receiver with a uniform planar array (UPA) are considered. An accurate mathematical model is developed for the spatial frequency response of the sounder that incorporates the non-ideal cross-polar beampatterns for the UPA elements. Due to the limited Field-of-View (FoV) of each element, the model is extended to accommodate multi-FoV measurements in distinct azimuth directions. A beamspace representation of the spatial frequency response is leveraged to develop three progressively complex algorithms aimed at solving the singlesnapshot maximum likelihood estimation problem: greedy matching pursuit (CLEAN), space-alternative generalized expectationmaximization (SAGE), and RiMAX. The first two are based on purely specular MPCs whereas RiMAX also accommodates diffuse MPCs. Two approaches for performance evaluation are proposed, one with knowledge of ground truth parameters, and one based on reconstruction mean-squared error. The three algorithms are compared through a demanding channel model with hundreds of MPCs and through real measurements. The results demonstrate that CLEAN gives quite reasonable estimates which are improved by SAGE and RiMAX. Lessons learned and directions for future research are discussed.
How secure is the time-modulated array-enabled ofdm directional modulation?
Authors: Zhihao Tao, Zhaoyi Xu, Athina Petropulu
Date: 2023-10-12 00:00:00
Tasks: blind source separation

Time-modulated arrays (TMA) transmitting orthogonal frequency division multiplexing (OFDM) waveforms achieve physical layer security by allowing the signal to reach the legitimate destination undistorted, while making the signal appear scrambled in all other directions. In this paper, we examine how secure the TMA OFDM system is, and show that it is possible for the eavesdropper to defy the scrambling. In particular, we show that, based on the scrambled signal, the eavesdropper can formulate a blind source separation problem and recover data symbols and TMA parameters via independent component analysis (ICA) techniques. We show how the scaling and permutation ambiguities arising in ICA can be resolved by exploiting the Toeplitz structure of the corresponding mixing matrix, and knowledge of data constellation, OFDM specifics, and the rules for choosing TMA parameters. We also introduce a novel TMA implementation to defend the scrambling against the eavesdropper.
Fusion framework and multimodality for the Laplacian approximation of Bayesian neural networks
Authors: Magnus Malmström, Isaac Skog, Daniel Axehill, Fredrik Gustafsson
Date: 2023-10-12 00:00:00
Tasks: image-classification, Image Classification

This paper considers the problem of sequential fusion of predictions from neural networks (NN) and fusion of predictions from multiple NN. This fusion strategy increases the robustness, i.e., reduces the impact of one incorrect classification and detection of outliers the \nn has not seen during training. This paper uses Laplacian approximation of Bayesian NNs (BNNs) to quantify the uncertainty necessary for fusion. Here, an extension is proposed such that the prediction of the NN can be represented by multimodal distributions. Regarding calibration of the estimated uncertainty in the prediction, the performance is significantly improved by having the flexibility to represent a multimodal distribution. Two class classical image classification tasks, i.e., MNIST and CFAR10, and image sequences from camera traps of carnivores in Swedish forests have been used to demonstrate the fusion strategies and proposed extension to the Laplacian approximation.
Low Complexity Algorithms for Mission Completion Time Minimization in UAV-Based ISAC Systems
Authors: Mateen Ashraf, Anna Gaydamaka, Bo Tan, Dmitri Moltchanov, Yevgeni Koucheryavy
Date: 2023-10-12 00:00:00
Tasks: ISAC

The inherent support of sixth-generation (6G) systems enabling integrated sensing and communications (ISAC) paradigm greatly enhances the application area of intelligent transportation systems (ITS). One of the mission-critical applications enabled by these systems is disaster management, where ISAC functionality may not only provide localization but also provide users with supplementary information such as escape routes, time to rescue, etc. In this paper, by considering a large area with several locations of interest, we formulate and solve the optimization problem of delivering task parameters of the ISAC system by optimizing the UAV speed and the order of visits to the locations of interest such that the mission time is minimized. The formulated problem is a mixed integer non-linear program which is quite challenging to solve. To reduce the complexity of the solution algorithms, we propose two circular trajectory designs. The first algorithm finds the optimal UAV velocity and radius of the circular trajectories. The second algorithm finds the optimal connecting points for joining the individual circular trajectories. Our numerical results reveal that, with practical simulation parameters, the first algorithm provides a time saving of at least $20\%$, while the second algorithm cuts down the total completion time by at least $7$ times.
Underwater Sound Speed Profile Construction: A Review
Authors: Wei Huang, Jixuan Zhou, Fan Gao, Jiajun Lu, Sijia Li, Pengfei Wu, Junting Wang, Hao Zhang, Tianhe Xu
Date: 2023-10-12 00:00:00
Tasks: Compressive Sensing

Real--time and accurate construction of regional sound speed profiles (SSP) is important for building underwater positioning, navigation, and timing (PNT) systems as it greatly affect the signal propagation modes such as trajectory. In this paper, we summarizes and analyzes the current research status in the field of underwater SSP construction, and the mainstream methods include direct SSP measurement and SSP inversion. In the direct measurement method, we compare the performance of popular international commercial temperature, conductivity, and depth profilers (CTD). While for the inversion methods, the framework and basic principles of matched field processing (MFP), compressive sensing (CS), and deep learning (DL) for constructing SSP are introduced, and their advantages and disadvantages are compared. The traditional direct measurement method has good accuracy performance, but it usually takes a long time. The proposal of SSP inversion method greatly improves the convenience and real--time performance, but the accuracy is not as good as the direct measurement method. Currently, the SSP inversion relies on sonar observation data, making it difficult to apply to areas that couldn't be covered by underwater observation systems, and these methods are unable to predict the distribution of sound velocity at future times. How to comprehensively utilize multi-source data and provide elastic sound velocity distribution estimation services with different accuracy and real-time requirements for underwater users without sonar observation data is the mainstream trend in future research on SSP construction.
Fast Ray-Tracing-Based Precise Underwater Acoustic Localization without Prior Acknowledgment of Target Depth
Authors: Wei Huang, Hao Zhang, Kaitao Meng, Fan Gao, Wenzhou Sun, Jianxu Shu, Tianhe Xu, Deshi Li
Date: 2023-10-12 00:00:00

Underwater localization is of great importance for marine observation and building positioning, navigation, timing (PNT) systems that could be widely applied in disaster warning, underwater rescues and resources exploration. The uneven distribution of underwater sound velocity poses great challenge for precise underwater positioning. The current soundline correction positioning method mainly aims at scenarios with known target depth. However, for nodes that are non-cooperative nodes or lack of depth information, soundline tracking strategies cannot work well due to nonunique positional solutions. To tackle this issue, we propose an iterative ray tracing 3D underwater localization (IRTUL) method for stratification compensation. To demonstrate the feasibility of fast stratification compensation, we first derive the signal path as a function of glancing angle, and then prove that the signal propagation time and horizontal propagation distance are monotonic functions of the initial grazing angle, so that fast ray tracing can be achieved. Then, we propose an sound velocity profile (SVP) simplification method, which reduces the computational cost of ray tracing. Experimental results show that the IRTUL has the most significant distance correction in the depth direction, and the average accuracy of IRTUL has been improved by about 3 meters compared to localization model with constant sound velocity. Also, the simplified SVP can significantly improve real-time performance with average accuracy loss less than 0.2 m when used for positioning.
Sensing-assisted Accurate and Fast Beam Management for Cellular-connected mmWave UAV Network
Authors: Yanpeng Cui, Qixun Zhang, Zhiyong Feng, Qin Wen, Ying Zhou, Zhiqing Wei, Ping Zhang
Date: 2023-10-12 00:00:00
Tasks: Integrated sensing and communication, Management

Beam management, including initial access (IA) and beam tracking, is essential to the millimeter-wave Unmanned Aerial Vehicle (UAV) network. However, conventional communication-only and feedback-based schemes suffer a high delay and low accuracy of beam alignment since they only enable the receiver to passively hear the information of the transmitter from the radio domain. This paper presents a novel sensing-assisted beam management approach, the first solution that fully utilizes the information from the visual domain to improve communication performance. We employ both integrated sensing and communication and computer vision techniques and design an extended Kalman filtering method for beam tracking and prediction. Besides, we also propose a novel dual identity association solution to distinguish multiple UAVs in dynamic environments. Real-world experiments and numerical results show that the proposed solution outperforms the conventional methods in IA delay, association accuracy, tracking error, and communication performance.
3D terrain mapping and filtering from coarse resolution data cubes extracted from real-aperture 94 GHz radar
Authors: William D. Harcourt, David G. Macfarlane, Duncan A. Robertson
Date: 2023-10-12 00:00:00

Accurate, high-resolution 3D mapping of environmental terrain is critical in a range of disciplines. In this study, we develop a new technique, called the PCFilt-94 algorithm, to extract 3D point clouds from coarse resolution millimetre-wave radar data cubes and quantify their associated uncertainties. A technique to non-coherently average neighbouring waveforms surrounding each AVTIS2 range profile was developed in order to reduce speckle and was found to reduce point cloud uncertainty by 13% at long range and 20% at short range. Further, a Voronoi-based point cloud outlier removal algorithm was implemented which iteratively removes outliers in a point cloud until the process converges to the removal of 0 points. Taken together, the new processing methodology produces a stable point cloud, which means that: 1) it is repeatable even when using different point cloud extraction and filtering parameter values during pre-processing, and 2) is less sensitive to over-filtering through the point cloud processing workflow. Using an optimal number of Ground Control Points (GCPs) for georeferencing, which was determined to be 3 at close range (<1.5 km) and 5 at long range (>3 km), point cloud uncertainty was estimated to be approximately 1.5 m at 1.5 km to 3 m at 3 km and followed a Lorentzian distribution. These uncertainties are smaller than those reported for other close-range radar systems used for terrain mapping. The results of this study should be used as a benchmark for future application of millimetre-wave radar systems for 3D terrain mapping.
Channel-robust Automatic Modulation Classification Using Spectral Quotient Cumulants
Authors: Sai Huang, Yuting Chen, Jiashuo He, Shuo Chang, Zhiyong Feng
Date: 2023-10-12 00:00:00
Tasks: Classification

Automatic modulation classification (AMC) is to identify the modulation format of the received signal corrupted by the channel effects and noise. Most existing works focus on the impact of noise while relatively little attention has been paid to the impact of channel effects. However, the instability posed by multipath fading channels leads to significant performance degradation. To mitigate the adverse effects of the multipath channel, we propose a channel-robust modulation classification framework named spectral quotient cumulant classification (SQCC) for orthogonal frequency division multiplexing (OFDM) systems. Specifically, we first transform the received signal to the spectral quotient (SQ) sequence by spectral circular shift division operations. Secondly, an outlier detector is proposed to filter the outliers in the SQ sequence. At last, we extract spectral quotient cumulants (SQCs) from the filtered SQ sequence as the inputs to train the artificial neural network (ANN) classifier and use the trained ANN to make the final decisions. Simulation results show that our proposed SQCC method exhibits classification robustness and superiority under various unknown Rician multipath fading channels compared with other existing methods. Specifically, the SQCC method achieves nearly 90% classification accuracy at the signal to noise ratio (SNR) of 4dB when testing under multiple channels but training under AWGN channel.
On the Capacity of Reconfigurable Intelligence Surface: the Sparse Channel Case
Authors: Chenxi Zhu
Date: 2023-10-12 00:00:00

Reconfigurable intelligent surface (RIS) is an important candidate technology for 6G. We provide an analysis of RIS-assisted MIMO communication in sparse channel typically found in the mmW or THz range. By exploring the sparse property, we maximize the capacity in the singular space of the channel and developed efficient algorithms for SU-MIMO or DL MU-MIMO. We also proved it is more difficult to support high rank transmission in the RIS reflection channel than in the traditional MIMO channel.
Adaptive Quantization for Key Generation in Low-Power Wide-Area Networks
Authors: Chen Chen, Junqing Zhang, Yingying Chen
Date: 2023-10-11 00:00:00
Tasks: Quantization

Physical layer key generation based on reciprocal and random wireless channels has been an attractive solution for securing resource-constrained low-power wide-area networks (LPWANs). When quantizing channel measurements, namely received signal strength indicator (RSSI), into key bits, the existing works mainly adopt fixed quantization levels and guard band parameters, which fail to fully extract keys from RSSI measurements. In this paper, we propose a novel adaptive quantization scheme for key generation in LPWANs, taking LoRa as a case study. The proposed adaptive quantization scheme can dynamically adjust the quantization parameters according to the randomness of RSSI measurements estimated by Lempel-Ziv complexity (LZ76), while ensuring a predefined key disagreement ratio (KDR). Specifically, our scheme uses pre-trained linear regression models to determine the appropriate quantization level and guard band parameter for each segment of RSSI measurements. Moreover, we propose a guard band parameter calibration scheme during information reconciliation during real-time key generation operation. Experimental evaluations using LoRa devices show that the proposed adaptive quantization scheme outperforms the benchmark differential quantization and fixed quantization with up to 2.35$\times$ and 1.51$\times$ key generation rate (KGR) gains, respectively.