pwc-archive/papers-with-abstracts huggingface dataset:
Space-Time Adaptive Detection at Low Sample Support
Authors: Benjamin D. Robinson, Robert Malinas, Alfred O. Hero III
Date: 2020-10-07 00:00:00

An important problem in space-time adaptive detection is the estimation of the large p-by-p interference covariance matrix from training signals. When the number of training signals n is greater than 2p, existing estimators are generally considered to be adequate, as demonstrated by fixed-dimensional asymptotics. But in the low-sample-support regime (n < 2p or even n < p) fixed-dimensional asymptotics are no longer applicable. The remedy undertaken in this paper is to consider the "large dimensional limit" in which n and p go to infinity together. In this asymptotic regime, a new type of estimator is defined (Definition 2), shown to exist (Theorem 1), and shown to be detection-theoretically ideal (Theorem 2). Further, asymptotic conditional detection and false-alarm rates of filters formed from this type of estimator are characterized (Theorems 3 and 4) and shown to depend only on data that is given, even for non-Gaussian interference statistics. The paper concludes with several Monte Carlo simulations that compare the performance of the estimator in Theorem 1 to the predictions of Theorems 2-4, showing in particular higher detection probability than Steiner and Gerlach's Fast Maximum Likelihood estimator.
End-to-end optimization of coherent optical communications over the split-step Fourier method guided by the nonlinear Fourier transform theory
Authors: Simone Gaiarin, Francesco Da Ros, Rasmus T. Jones, Darko Zibar
Date: 2020-09-11 00:00:00

Optimizing modulation and detection strategies for a given channel is critical to maximize the throughput of a communication system. Such an optimization can be easily carried out analytically for channels that admit closed-form analytical models. However, this task becomes extremely challenging for nonlinear dispersive channels such as the optical fiber. End-to-end optimization through autoencoders (AEs) can be applied to define symbol-to-waveform (modulation) and waveform-to-symbol (detection) mappings, but so far it has been mainly shown for systems relying on approximate channel models. Here, for the first time, we propose an AE scheme applied to the full optical channel described by the nonlinear Schr\{"o}dinger equation (NLSE). Transmitter and receiver are jointly optimized through the split-step Fourier method (SSFM) which accurately models an optical fiber. In this first numerical analysis, the detection is performed by a neural network (NN), whereas the symbol-to-waveform mapping is aided by the nonlinear Fourier transform (NFT) theory in order to simplify and guide the optimization on the modulation side. This proof-of-concept AE scheme is thus benchmarked against a standard NFT-based system and a threefold increase in achievable distance (from 2000 to 6640 km) is demonstrated.
RIS-Assisted MISO Communication: Optimal Beamformers and Performance Analysis
Authors: Neel Kanth Kundu, Matthew R. McKay
Date: 2020-07-16 00:00:00

We study a multiple-input single-output (MISO) communication system assisted by a reconfigurable intelligent surface (RIS). A base station (BS) having multiple antennas is assumed to be communicating to a single-antenna user equipment (UE), with the help of a RIS. We assume that the system operates in an environment with line-of-sight (LoS) between the BS and RIS, whereas the RIS-UE link experiences Rayleigh fading. We present a closed form expression for the optimal active and passive beamforming vectors at the BS and RIS respectively. Then, by characterizing the statistical properties of the received SNR at the UE, we apply them to derive analytical approximations for different system performance measures, including the outage probability, average achievable rate and average symbol error probability (SEP). Our results, in general, demonstrate that the gain due to RIS can be substantial, and can be significantly greater than the gains reaped by using multiple BS antennas.
Index-Modulated Circularly-Shifted Chirps for Dual-Function Radar & Communication Systems
Authors: Safi Shams Muhtasimul Hoque, Alphan Sahin
Date: 2020-10-07 00:00:00

In this study, we analyze index modulation (IM) based on circularly-shifted chirps (CSCs) for dual-function radar & communication (DFRC) systems. We develop a maximum likelihood (ML) range estimator that considers multiple scatters. To improve the correlation properties of the transmitted waveform and estimation accuracy, we propose index separation (IS) which separates the CSCs apart in time. We theoretically show that the separation can be large under certain conditions without losing the spectral efficiency (SE). Our numerical results show that the IS combined ML and linear minimum mean square error (LMMSE)-based estimators can provide approximately 3 dB signal-to-noise ratio (SNR) gain in some cases while improving estimation accuracy substantially without causing any bit-error ratio (BER) degradation at the communication receiver.
Free Space Optics for Next-Generation Satellite Networks
Authors: Aizaz U. Chaudhry, Halim Yanikomeroglu
Date: 2020-10-07 00:00:00

Free space optics (FSO) refers to optical wireless communications in outdoor environments. The aim of this paper is to analyze the role that FSO is envisaged to play in the creation of next-generation satellite networks. To begin with, the reader is introduced to the types of FSO links and functionalities of a basic FSO system. Next, a comparison of FSO and radio frequency (RF) technologies for inter-satellite links (ISLs) is provided, including a comparison between FSO and RF links when employed between low Earth orbit satellites. After that, the types of FSO or laser ISLs are considered, and the challenges in establishing laser ISLs, the properties of laser ISLs, and the capabilities of terminals for laser ISLs are discussed. Then, the parameters of a satellite constellation are highlighted, and different aspects of SpaceX's upcoming mega-constellation Starlink are explored. In addition, the optical wireless satellite network that is created by utilizing laser ISLs is examined. Finally, a use case is investigated for next-generation optical wireless satellite networks that are envisioned by the mid to late 2020s.
Clustering-based Joint Channel Estimation and Signal Detection for Grant-free NOMA
Authors: Ayoob Salari, Mahyar Shirvanimoghaddam, Muhammad Basit Shahab, Reza Arablouei, Sarah Johnson
Date: 2020-10-07 00:00:00
Tasks: Clustering

We propose a joint channel estimation and signal detection technique for the uplink non-orthogonal multiple access using an unsupervised clustering approach. We apply the Gaussian mixture model to cluster received signals and accordingly optimize the decision regions to enhance the symbol error rate (SER). We show that when the received powers of the users are sufficiently different, the proposed clustering-based approach with no channel state information (CSI) at the receiver achieves an SER performance similar to that of the conventional maximum likelihood detector with full CSI. Since the accuracy of the utilized clustering algorithm depends on the number of the data points available at the receiver, the proposed technique delivers a tradeoff between the accuracy and block length.
Detection of Ship Wakes in SAR Imagery Using Cauchy Regularisation
Authors: Tianqi Yang, Oktay Karakuş, Alin Achim
Date: 2020-02-12 00:00:00

Ship wake detection is of great importance in the characterisation of synthetic aperture radar (SAR) images of the ocean surface since wakes usually carry essential information about vessels. Most detection methods exploit the linear characteristics of the ship wakes and transform the lines in the spatial domain into bright or dark points in a transform domain, such as the Radon or Hough transforms. This paper proposes an innovative ship wake detection method based on sparse regularisation to obtain the Radon transform of the SAR image, in which the linear features are enhanced. The corresponding cost function utilizes the Cauchy prior, and on this basis, the Cauchy proximal operator is proposed. A Bayesian method, the Moreau-Yoshida unadjusted Langevin algorithm (MYULA), which is computationally efficient and robust is used to estimate the image in the transform domain by minimizing the negative log-posterior distribution. The detection accuracy of the Cauchy prior based approach is 86.7%, which is demonstrated by experiments over six COSMO-SkyMed images.
Machine Learning Empowered Trajectory and Passive Beamforming Design in UAV-RIS Wireless Networks
Authors: Xiao Liu, Yuanwei Liu, Yue Chen
Date: 2020-10-06 00:00:00
Tasks: BIG-bench Machine Learning, Q-Learning

A novel framework is proposed for integrating reconfigurable intelligent surfaces (RIS) in unmanned aerial vehicle (UAV) enabled wireless networks, where an RIS is deployed for enhancing the service quality of the UAV. Non-orthogonal multiple access (NOMA) technique is invoked to further improve the spectrum efficiency of the network, while mobile users (MUs) are considered as roaming continuously. The energy consumption minimizing problem is formulated by jointly designing the movement of the UAV, phase shifts of the RIS, power allocation policy from the UAV to MUs, as well as determining the dynamic decoding order. A decaying deep Q-network (D-DQN) based algorithm is proposed for tackling this pertinent problem. In the proposed D-DQN based algorithm, the central controller is selected as an agent for periodically observing the state of UAV-enabled wireless network and for carrying out actions to adapt to the dynamic environment. In contrast to the conventional DQN algorithm, the decaying learning rate is leveraged in the proposed D-DQN based algorithm for attaining a tradeoff between accelerating training speed and converging to the local optimal. Numerical results demonstrate that: 1) In contrast to the conventional Q-learning algorithm, which cannot converge when being adopted for solving the formulated problem, the proposed D-DQN based algorithm is capable of converging with minor constraints; 2) The energy dissipation of the UAV can be significantly reduced by integrating RISs in UAV-enabled wireless networks; 3) By designing the dynamic decoding order and power allocation policy, the RIS-NOMA case consumes 11.7% less energy than the RIS-OMA case.
Joint Collaboration and Compression Design for Distributed Sequential Estimation in a Wireless Sensor Network
Authors: Xiancheng Cheng, Prashant Khanduri, Boxiao Chen, Pramod K. Varshney
Date: 2020-10-06 00:00:00

In this work, we propose a joint collaboration-compression framework for sequential estimation of a random vector parameter in a resource constrained wireless sensor network (WSN). Specifically, we propose a framework where the local sensors first collaborate (via a collaboration matrix) with each other. Then a subset of sensors selected to communicate with the FC linearly compress their observations before transmission. We design near-optimal collaboration and linear compression strategies under power constraints via alternating minimization of the sequential minimum mean square error. We show that the objective function for collaboration design can be non-convex depending on the network topology. We reformulate and solve the collaboration design problem using quadratically constrained quadratic program (QCQP). Moreover, the compression design problem is also formulated as a QCQP. We propose two versions of compression design, one centralized where the compression strategies are derived at the FC and the other decentralized, where the local sensors compute their individual compression matrices independently. It is noted that the design of decentralized compression strategy is a non-convex problem. We obtain a near-optimal solution by using the bisection method. In contrast to the one-shot estimator, our proposed algorithm is capable of handling dynamic system parameters such as channel gains and energy constraints. Importantly, we show that the proposed methods can also be used for estimating time-varying random vector parameters. Finally, numerical results are provided to demonstrate the effectiveness of the proposed framework.
Single-Sideband Time-Modulated Phased Array With 2-bit Phased Shifters
Authors: Yanchang Gao, Gang Ni, Kun Wang, Yiqing Liu, Chong He, Ronghong Jin, Xianling Liang
Date: 2020-10-06 00:00:00

A novel single-sideband (SSB) time-modulated technique with 2-bit phase shifters is proposed. The timemodulated module is implemented by adding periodic phase modulation to 2-bit phase shifters, which is simpler without performance loss compared to existing SSB time-modulated method. During one modulation period, four phase states (0, {\pi}/2, {\pi}, 3{\pi}/2) of 2-bit phase shifters are switched in sequence. After the modulation, the SSB time modulation is realized and the main power is distributed to the first harmonic component. The feasibility of the proposed method is verified by experiments. The undesired harmonics are efficiently suppressed. Meanwhile, 80{\deg} beam scanning range are realized through the proposed module.
Progressive InSAR phase estimation
Authors: Francesco De Zan
Date: 2020-10-06 00:00:00

This paper introduces a novel scheme to progressively estimate interferometric phases from a stack of synthetic aperture radar (SAR) images. The scheme is shown to yield comparable performance to full-covariance algorithms for a realistic decorrelation scenario. The implementation is suited for continuous processing and updating of phase products, without compromising long-term phase accuracy. It also limits the requirements in terms of data transfer between archive and processing facility, a significant issue for processing large archives of SAR data.
Displaced Sensor Automotive Radar Imaging
Authors: Guohua Wang, Kumar Vijay Mishra
Date: 2020-10-06 00:00:00

Displaced automotive sensor imaging exploits joint processing of the data acquired from multiple radar units, each of which may have limited individual resources, to enhance the localization accuracy. Prior works either consider perfect synchronization among the sensors, employ single antenna radars, entail high processing cost, or lack performance analyses. Contrary to these works, we develop a displaced multiple-input multiple-output (MIMO) frequency-modulated continuous-wave (FMCW) radar signal model under coarse synchronization with only frame-level alignment. We derive Bayesian performance bounds for the common automotive radar processing modes such as point-cloud-based fusion as well as raw-signal-based non-coherent and coherent imaging. For the non-coherent mode, which offers a compromise between low computational load and improved localization, we exploit the block sparsity of range profiles for signal reconstruction to avoid direct computational imaging with massive data. For the high-resolution coherent imaging, we develop a method that automatically estimates the synchronization error and performs displaced radar imaging by exploiting sparsity-driven recovery models. Our extensive numerical experiments demonstrate these advantages. Our proposed non-coherent processing of displaced MIMO FMCW radars improves position estimation by an order over the conventional point-cloud fusion.
Learning to Localise Automated Vehicles in Challenging Environments using Inertial Navigation Systems (INS)
Authors: Uche Onyekpe, Vasile Palade, Stratis Kanarachos
Date: 2020-10-05 00:00:00
Tasks: Autonomous Vehicles

An algorithm based on Artificial Neural Networks is proposed in this paper to improve the accuracy of Inertial Navigation System (INS)/ Global Navigation Satellite System (GNSS) integrated navigation during the absence of GNSS signals. The INS which can be used to continuously position autonomous vehicles during GNSS signal losses around urban canyons, bridges, tunnels and trees, suffers from unbounded exponential error drifts cascaded over time during the integration of the gyroscope and double integration of the accelerometer to displacement. More so, the error drift is characterised by a pattern dependent on time. The Input Delay Neural Network (IDNN) has the ability to learn the error drift over time [1] and possesses the quality of being more computationally efficient than the Recurrent Neural Network (RNN), Long Short-Term Memory, and the Gated Recurrent Unit Network. Furthermore published literatures focus on travel routes which do not take complex driving scenarios into consideration, we therefore investigate in this paper the performance of the proposed algorithm on challenging scenarios, such as hard brake, roundabouts, sharp cornering, successive left and right turns and quick changes in vehicular acceleration across numerous test sequences. The results obtained show that the Neural Network-based approaches are able to provide up to 89.55 % improvement on the INS displacement estimation and 93.35 % on the INS orientation rate estimation.
Simulation-based Analysis of Multipath Delay Distributions in Urban Canyons
Authors: Simon Ollander, Friedrich-Wilhelm Bode, Marcus Baum
Date: 2020-06-26 00:00:00

Global navigation satellite systems provide accurate positioning nearly worldwide. However, in the urban canyons of dense cities, buildings block and reflect the signals, causing multipath errors. To mitigate multipath errors, knowledge of the distribution of the reflection delays is important. Measurements of this distribution have been done in several dense cities, but it is unknown how the delay distribution depends on the depth of the urban canyon. To fill this gap, we simulated reflection scenarios in 12 different environments: from suburban to deep urban canyon. Subsequently, we analyzed the resulting delay distributions. This paper presents these distributions, and a method to estimate them using the number of received satellites. According to our simulation, the multipath delays follow gamma distributions, whose shape parameters decrease when the urban canyon depth increases. A quadratic model can estimate the shape parameter using the number of received satellites. Consequently, depending on the number of received satellites, the distribution of the reflection delays can be estimated. This information can be combined with prior knowledge from other methods for improved multipath delay estimation. In the future, for more realistic results, the effects on signals that are reflected multiple times and environments other than urban canyons should be simulated.
Direct Signal Separation Via Extraction of Local Frequencies with Adaptive Time-Varying Parameters
Authors: Lin Li, Charles K. Chui, Qingtang Jiang
Date: 2020-10-05 00:00:00
Tasks: Time Series Analysis

Real-world phenomena that can be formulated as signals are often affected by a number of factors and appear as multi-component modes. To understand and process such phenomena, "divide-and-conquer" is probably the most common strategy to address the problem. In other words, the captured signal is decomposed into signal components for each individual component to be processed. Unfortunately, for signals that are superimposition of non-stationary amplitude-frequency modulated (AM-FM) components, the "divide-and-conquer" strategy is bound to fail, since there is no way to be sure that the decomposed components take on the AM-FM formulations which are necessary for the extraction of their instantaneous frequencies (IFs) and amplitudes (IAs). In this paper, we propose an adaptive signal separation operation (ASSO) for effective and accurate separation of a single-channel blind-source multi-component signal, via introducing a time-varying parameter that adapts locally to IFs and using linear chirp (linear frequency modulation) signals to approximate components at each time instant. We derive more accurate component recovery formulae based on the linear chirp signal local approximation. In addition, a recovery scheme, together with a ridge detection method, is also proposed to extract the signal components one by one, and the time-varying parameter is updated for each component. The proposed method is suitable for engineering implementation, being capable of separating complicated signals into their components or sub-signals and reconstructing the signal trend directly. Numerical experiments on synthetic and real-world signals are presented to demonstrate our improvement over the previous attempts.
On Beat Frequency Oscillation of Two-Stage Wireless Power Receivers
Authors: Kerui Li, Siew-Chong Tan, Ron Shu Yuen Hui
Date: 2020-04-28 00:00:00
Tasks: Vocal Bursts Valence Prediction

Two-stage wireless power receivers, which typically include an AC-DC diode rectifier and a DC-DC regulator, are popular solutions in low-power wireless power transfer applications. However, the interaction between the rectifier and the regulator may introduce beat frequency oscillation on both the DC-link and output capacitors. In this paper, the cause of the beat frequency oscillation and its related issues are investigated with the corresponding design solution on alleviating the oscillation discussed. Theoretical and experimental results verifying the presence of beat frequency oscillation in the two-stage wireless receiver system are provided. Our study shows that the beat frequency oscillation can be significantly alleviated if appropriate design solutions are applied.
Ford Highway Driving RTK Dataset: 30,000 km of North American Highways
Authors: Sarah E. Houts, Nahid Pervez, Umair Ibrahim, Gaurav Pandey, Tyler G. R. Reid
Date: 2020-10-05 00:00:00
Tasks: Autonomous Driving

There is a growing need for vehicle positioning information to support Advanced Driver Assistance Systems (ADAS), Connectivity (V2X), and Autonomous Driving (AD) features. These range from a need for road determination ($<$5 meters), lane determination ($<$1.5 meters), and determining where the vehicle is within the lane ($<$0.3 meters). This paper presents the Ford Highway Driving RTK (Ford-HDR) dataset. This dataset includes nearly 30,000 km of data collected primarily on North American highways during a driving campaign designed to validate driver assistance features in 2018. This includes data from a representative automotive production GNSS used primarily for turn-by-turn navigation as well as an Inertial Navigation System (INS) which couples two survey-grade GNSS receivers with a tactical grade Inertial Measurement Unit (IMU) to act as ground truth. The latter utilized networked Real-Time Kinematic (RTK) GNSS corrections delivered over a cellular modem in real-time. This dataset is being released into the public domain to spark further research in the community.
Channel Prediction for mmWave Ground-to-Air Propagation under Blockage
Authors: Wahab Khawaja, Ozgur Ozdemir, Ismail Guvenc
Date: 2020-10-04 00:00:00

Ground-to-air (GA) communication using unmanned aerial vehicles (UAVs) has gained popularity in recent years and is expected to be part of 5G networks and beyond. However, the GA links are susceptible to frequent blockages at millimeter wave (mmWave) frequencies. During a link blockage, the channel information cannot be obtained reliably. In this work, we provide a novel method of channel prediction during the GA link blockage at 28 GHz. In our approach, the multipath components (MPCs) along a UAV flight trajectory are arranged into independent path bins based on the minimum Euclidean distance among the channel parameters of the MPCs. After the arrangement, the channel parameters of the MPCs in individual path bins are forecasted during the blockage. An autoregressive model is used for forecasting. The results obtained from ray tracing simulations indicate a close match between the actual and the predicted mmWave channel.