pwc-archive/papers-with-abstracts huggingface dataset:
Alternative Chirp Spread Spectrum Techniques for LPWANs
Authors: Ivo Bizon Franco de Almeida, Marwa Chafii, Ahmad Nimr, Gerhard Fettweis
Date: 2021-02-18 00:00:00

Chirp spread spectrum (CSS) is the modulation technique currently employed by Long-Range (LoRa), which is one of the most prominent Internet of things wireless communications standards. The LoRa physical layer (PHY) employs CSS on top of a variant of frequency shift keying, and non-coherent detection is employed at the receiver. While it offers a good trade-off among coverage, data rate and device simplicity, its maximum achievable data rate is still a limiting factor for some applications. Moreover, the current LoRa standard does not fully exploit the CSS generic case, i.e., when data to be transmitted is encoded in different waveform parameters. Therefore, the goal of this paper is to investigate the performance of CSS while exploring different parameter settings aiming to increase the maximum achievable throughput, and hence increase spectral efficiency. Moreover, coherent and non-coherent reception algorithm design is presented under the framework of maximum likelihood estimation. For the practical receiver design, the formulation of a channel estimation technique is also presented. The performance evaluation of the different variants of CSS is carried out by inspection of the symbol error ratio as a function of the signal-to-noise ratio together with the maximum achievable throughput each scheme can achieve.
Reinforcement Learning for Beam Pattern Design in Millimeter Wave and Massive MIMO Systems
Authors: Yu Zhang, Muhammad Alrabeiah, Ahmed Alkhateeb
Date: 2021-02-18 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning (RL)

Employing large antenna arrays is a key characteristic of millimeter wave (mmWave) and terahertz communication systems. However, due to the adoption of fully analog or hybrid analog/digital architectures, as well as non-ideal hardware or arbitrary/unknown array geometries, the accurate channel state information becomes hard to acquire. This impedes the design of beamforming/combining vectors that are crucial to fully exploit the potential of large-scale antenna arrays in providing sufficient receive signal power. In this paper, we develop a novel framework that leverages deep reinforcement learning (DRL) and a Wolpertinger-variant architecture and learns how to iteratively optimize the beam pattern (shape) for serving one or a small set of users relying only on the receive power measurements and without requiring any explicit channel knowledge. The proposed model accounts for key hardware constraints such as the phase-only, constant-modulus, and quantized-angle constraints. Further, the proposed framework can efficiently optimize the beam patterns for systems with non-ideal hardware and for arrays with unknown or arbitrary array geometries. Simulation results show that the developed solution is capable of finding near-optimal beam patterns based only on the receive power measurements.
Millimeter-Wave Circular Synthetic Aperture Radar Imaging
Authors: Shahrokh Hamidi, Safieddin Safavi-Naeini
Date: 2020-12-22 00:00:00

In this paper, we present a high resolution microwave imaging technique using a compact and low cost single channel Frequency Modulated Continuous Wave (FMCW) radar based on Circular Synthetic Aperture Radar (CSAR) technique. We develop an algorithm to reconstruct the image from the raw data and analyse different aspects of the system analytically. Furthermore, we discuss the differences between the proposed systems in the literature and the one presented in this work. Finally, we apply the proposed approach to the experimental data collected from a single channel FMCW radar operating at $\rm 79 \;GHz$ and present the results.
Performance Dependency of LSTM and NAR Beamformers With Respect to Sensor Array Properties in V2I Scenario
Authors: Prateek Bhadauria, Ravi Kumar, Sanjay Sharma
Date: 2021-02-17 00:00:00
Tasks: Time Series, Time Series Analysis

Prediction and nullifying the interference is a challenging problem in vehicle to infrastructure scenarios . The implementation of practical V2I network is limited because of inevitability of interference due to random nature of the wireless channel. The interference introduces angle ambiguity between the road side units mounted base station and user equipment. This paper proposes an adaptive beamforming technique for mitigation of interference in V2I networks, especially in multiuser environment. In this work , Long short term based (LSTM) based deep learning and non linear auto regresive technique based regressor have been employed to predict the angles between the road side units and user equipment .Advance prediction of transmit and receive signals enables reliable vehicle to infrastructure communication. Instead of predicting the beamforming matrix directly, we predict the main features using LSTM for learning dependencies in the input time series ,where complex variables were taken as input states and final beamformed signal was the output. simulation results have confirmed that the proposed LSTM model achieves comparable performance in terms of system throughput when compared with the non linear auto regressive method implemented as an artificial neural network.
Supporting More Active Users for Massive Access via Data-assisted Activity Detection
Authors: Xinyu Bian, Yuyi Mao, Jun Zhang
Date: 2021-02-17 00:00:00
Tasks: Action Detection, Activity Detection

Massive machine-type communication (mMTC) has been regarded as one of the most important use scenarios in the fifth generation (5G) and beyond wireless networks, which demands scalable access for a large number of devices. While grant-free random access has emerged as a promising mechanism for massive access, its potential has not been fully unleashed. Particularly, the two key tasks in massive access systems, namely, user activity detection and data detection, were handled separately in most existing studies, which ignored the common sparsity pattern in the received pilot and data signal. Moreover, error detection and correction in the payload data provide additional mechanisms for performance improvement. In this paper, we propose a data-assisted activity detection framework, which aims at supporting more active users by reducing the activity detection error, consisting of false alarm and missed detection errors. Specifically, after an initial activity detection step based on the pilot symbols, the false alarm users are filtered by applying energy detection for the data symbols; once data symbols of some active users have been successfully decoded, their effect in activity detection will be resolved via successive pilot interference cancellation, which reduces the missed detection error. Simulation results show that the proposed algorithm effectively increases the activity detection accuracy, and it is able to support $\sim 20\%$ more active users compared to a conventional method in some sample scenarios.
A Novel Bayesian Approach for the Two-Dimensional Harmonic Retrieval Problem
Authors: Rohan R. Pote, Bhaskar D. Rao
Date: 2021-02-17 00:00:00
Tasks: Retrieval, Vocal Bursts Valence Prediction

Sparse signal recovery algorithms like sparse Bayesian learning work well but the complexity quickly grows when tackling higher dimensional parametric dictionaries. In this work we propose a novel Bayesian strategy to address the two dimensional harmonic retrieval problem, through remodeling and reparameterization of the standard data model. This new model allows us to introduce a block sparsity structure in a manner that enables a natural pairing of the parameters in the two dimensions. The numerical simulations demonstrate that the inference algorithm developed (H-MSBL) does not suffer from source identifiability issues and is capable of estimating the harmonic components in challenging scenarios, while maintaining a low computational complexity.
A Comprehensive Survey of 6G Wireless Communications
Authors: Yang Zhao, Wenchao Zhai, Jun Zhao, Tinghao Zhang, Sumei Sun, Dusit Niyato, Kwok-Yan Lam
Date: 2020-12-21 00:00:00
Tasks: Survey

While fifth-generation (5G) communications are being rolled out worldwide, sixth-generation (6G) communications have attracted much attention from both the industry and the academia. Compared with 5G, 6G will have a wider frequency band, higher transmission rate, spectrum efficiency, greater connection capacity, shorter delay, broader coverage, and more robust anti-interference capability to satisfy various network requirements. This survey presents an insightful understanding of 6G wireless communications by introducing requirements, features, critical technologies, challenges, and applications. First, we give an overview of 6G from perspectives of technologies, security and privacy, and applications. Subsequently, we introduce various 6G technologies and their existing challenges in detail, e.g., artificial intelligence (AI), intelligent surfaces, THz, space-air-ground-sea integrated network, cell-free massive MIMO, etc. Because of these technologies, 6G is expected to outperform existing wireless communication systems regarding the transmission rate, latency, global coverage, etc. Next, we discuss security and privacy techniques that can be applied to protect data in 6G. Since edge devices are expected to gain popularity soon, the vast amount of generated data and frequent data exchange make the leakage of data easily. Finally, we predict real-world applications built on the technologies and features of 6G; for example, smart healthcare, smart city, and smart manufacturing will be implemented by taking advantage of AI.
Transmitter Selection for Secrecy in Cognitive Small-Cell Networks with Backhaul Knowledge
Authors: Burhan Wafai, Chinmoy Kundu, Ankit Dubey, Jinghua Zhang, Mark F. Flanagan
Date: 2021-02-16 00:00:00

A small-cell network with multiple transmitters and unreliable wireless backhaul is considered for secrecy enhancement. The small-cell network is operating under a spectrum sharing agreement with a primary network in a cognitive radio system. A constraint on the desired outage probability at the primary receiver is assumed as a part of the spectrum sharing agreement. The reliability of the wireless backhaul links are modeled by a set of independent and identically distributed Bernoulli random variables. A sub-optimal and an optimal small-cell transmitter selection (TS) scheme is proposed to improve the performance of the system, depending on the availability of channel state information. Selection schemes are designed for the scenario where knowledge is available regarding which backhaul links are active. The corresponding secrecy outage probabilities along with their asymptotic expressions are derived. It is shown that the secrecy performance is significantly improved compared to the case where knowledge of the active backhaul links is unavailable.
Recurrent Neural Network Assisted Transmitter Selection for Secrecy in Cognitive Radio Network
Authors: Shalini Tripathi, Chinmoy Kundu, Octavia A. Dobre, Ankur Bansal, Mark F. Flanagan
Date: 2021-02-16 00:00:00

In this paper, we apply the long short-term memory (LSTM), an advanced recurrent neural network based machine learning (ML) technique, to the problem of transmitter selection (TS) for secrecy in an underlay small-cell cognitive radio network with unreliable backhaul connections. The cognitive communication scenario under consideration has a secondary small-cell network that shares the same spectrum of the primary network with an agreement to always maintain a desired outage probability constraint in the primary network. Due to the interference from the secondary transmitter common to all primary transmissions, the secrecy rates for the different transmitters are correlated. LSTM exploits this correlation and matches the performance of the conventional technique when the number of transmitters is small. As the number grows, the performance degrades in the same manner as other ML techniques such as support vector machine, $k$-nearest neighbors, naive Bayes, and deep neural network. However, LSTM still significantly outperforms these techniques in misclassification ratio and secrecy outage probability. It also reduces the feedback overhead against conventional TS.
Through-the-Wall Radar under Electromagnetic Complex Wall: A Deep Learning Approach
Authors: Fardin Ghorbani, Hossein Soleimani
Date: 2021-02-16 00:00:00
Tasks: Deep Learning

This paper employed deep learning to do two-dimensional, multi-target locating in Through-the-Wall Radar under conditions where the wall is treated as a complex electromagnetic medium. We made five assumptions about the wall and two about the number of targets. There are two target modes available: single target and double targets. The wall scenarios include a homogeneous wall, a wall with an air gap, an inhomogeneous wall, an anisotropic wall, and an inhomogeneous-anisotropic wall. Target locating is accomplished through the use of a deep neural network technique. We constructed a dataset using the Python FDTD module and then modeled it using deep learning. Assuming the wall is a complex electromagnetic medium, we achieved 97.7% accuracy for single-target 2D locating and 94.1% accuracy for two-target locating. Additionally, we noticed a loss of 10% to 20% inaccuracy when noise was added at low SNRs, although this decrease dropped to less than 10% at high SNRs.
General Total Variation Regularized Sparse Bayesian Learning for Robust Block-Sparse Signal Recovery
Authors: Aditya Sant, Markus Leinonen, Bhaskar D. Rao
Date: 2021-02-13 00:00:00
Tasks: compressed sensing

Block-sparse signal recovery without knowledge of block sizes and boundaries, such as those encountered in multi-antenna mmWave channel models, is a hard problem for compressed sensing (CS) algorithms. We propose a novel Sparse Bayesian Learning (SBL) method for block-sparse recovery based on popular CS based regularizers with the function input variable related to total variation (TV). Contrary to conventional approaches that impose the regularization on the signal components, we regularize the SBL hyperparameters. This iterative TV-regularized SBL algorithm employs a majorization-minimization approach and reduces each iteration to a convex optimization problem, enabling a flexible choice of numerical solvers. The numerical results illustrate that the TV-regularized SBL algorithm is robust to the nature of the block structure and able to recover signals with both block-patterned and isolated components, proving useful for various signal recovery systems.
Online Joint State Inference and Learning of Partially Unknown State-Space Models
Authors: Anton Kullberg, Isaac Skog, Gustaf Hendeby
Date: 2021-02-15 00:00:00
Tasks: State Space Models

A computationally efficient method for online joint state inference and dynamical model learning is presented. The dynamical model combines an a priori known, physically derived, state-space model with a radial basis function expansion representing unknown system dynamics and inherits properties from both physical and data-driven modeling. The method uses an extended Kalman filter approach to jointly estimate the state of the system and learn the unknown system dynamics, via the parameters of the basis function expansion. The key contribution is a computational complexity reduction compared to a similar approach with globally supported basis functions. By using compactly supported radial basis functions and an approximate Kalman gain, the computational complexity is considerably reduced and is essentially determined by the support of the basis functions. The approximation works well when the system dynamics exhibit limited correlation between points well separated in the state-space domain. The method is exemplified via two intelligent vehicle applications where it is shown to: (i) have competitive system dynamics estimation performance compared to the globally supported basis function method, and (ii) be real-time applicable to problems with a large-scale state-space.
Reconfigurable Intelligent Surface Aided Mobile Edge Computing: From Optimization-Based to Location-Only Learning-Based Solutions
Authors: Xiaoyan Hu, Christos Masouros, Kai-Kit Wong
Date: 2021-02-15 00:00:00
Tasks: Edge-computing

In this paper, we explore optimization-based and data-driven solutions in a reconfigurable intelligent surface (RIS)-aided multi-user mobile edge computing (MEC) system, where the user equipment (UEs) can partially offload their computation tasks to the access point (AP). We aim at maximizing the total completed task-input bits (TCTB) of all UEs with limited energy budgets during a given time slot, through jointly optimizing the RIS reflecting coefficients, the AP's receive beamforming vectors, and the UEs' energy partition strategies for local computing and offloading. A three-step block coordinate descending (BCD) algorithm is first proposed to effectively solve the non-convex TCTB maximization problem with guaranteed convergence. In order to reduce the computational complexity and facilitate lightweight online implementation of the optimization algorithm, we further construct two deep learning architectures. The first one takes channel state information (CSI) as input, while the second one exploits the UEs' locations only for online inference. The two data-driven approaches are trained using data samples generated by the BCD algorithm via supervised learning. Our simulation results reveal a close match between the performance of the optimization-based BCD algorithm and the low-complexity learning-based architectures, all with superior performance to existing schemes in both cases with perfect and imperfect input features. Importantly, the location-only deep learning method is shown to offer a particularly practical and robust solution alleviating the need for CSI estimation and feedback when line-of-sight (LoS) direct links exist between UEs and the AP.
Compressive dual-comb spectroscopy
Authors: Akira Kawai, Takahiro Kageyama, Ryoichi Horisaki, Takuro Ideguchi
Date: 2020-07-03 00:00:00
Tasks: Compressive Sensing, Data Compression

Broadband, high resolution and rapid measurement of dual-comb spectroscopy (DCS) generates a large amount of data stream. We numerically demonstrate significant data compression of DCS spectra by using a compressive sensing technique. Our numerical simulation shows a compression rate of more than 100 with 3% error in mole fraction estimation of mid-infrared (MIR) DCS of two molecular species in a broadband (~30 THz) and high resolution (~115 MHz) condition. We also numerically demonstrate a massively parallel MIR DCS spectrum of 10 different molecular species can be reconstructed with a compression rate of 10.5 with a transmittance error of 0.003 from the original spectrum.
RF PIX2PIX Unsupervised Wi-Fi to Video Translation
Authors: Michael Drob
Date: 2021-02-14 00:00:00
Tasks: Object, Translation

With the proliferation of Wi-Fi devices in the environment, our surroundings are increasingly illuminated with low-level RF scatter. This scatter illuminates objects in the environment much like radar or LIDAR. We show that a novel unsupervised network, based on the PIX2PIX GAN architecture, can recover and visually reconstruct scene information solely from Wi-Fi background energy; in contrast to a significantly less accurate approach by Kefayati (et. all) which requires careful object labeling to recover object location from a scene. This is accomplished by learning a more robust mapping function between the channel state information (CSI) from Wi-Fi packets and Video image sample distributions.
Air-to-Ground Directional Channel Sounder With 64-antenna Dual-polarized Cylindrical Array
Authors: Jorge Gomez Ponce, Thomas Choi, Naveed A. Abbasi, Aldo Adame, Alexander Alvarado, Colton Bullard, Ruiyi Shen, Fred Daneshgaran, Harpreet S. Dhillon, Andreas F. Molisch
Date: 2021-02-13 00:00:00

Unmanned Aerial Vehicles (UAVs), popularly called drones, are an important part of future wireless communications, either as user equipment that needs communication with a ground station, or as base station in a 3D network. For both the analysis of the "useful" links, and for investigation of possible interference to other ground-based nodes, an understanding of the air-to-ground channel is required. Since ground-based nodes often are equipped with antenna arrays, the channel investigations need to account for it. This study presents a massive MIMO-based air-to-ground channel sounder we have recently developed in our lab, which can perform measurements for the aforementioned requirements. After outlining the principle and functionality of the sounder, we present sample measurements that demonstrate the capabilities, and give first insights into air-to-ground massive MIMO channels in an urban environment. Our results provide a platform for future investigations and possible enhancements of massive MIMO systems.
Learning from Natural Noise to Denoise Micro-Doppler Spectrogram
Authors: Chong Tang, Wenda Li, Shelly Vishwakarma, Karl Woodbridge, Simon Julier, Kevin Chetty
Date: 2021-02-13 00:00:00
Tasks: Denoising, Generative Adversarial Network

Micro-Doppler analysis has become increasingly popular in recent years owning to the ability of the technique to enhance classification strategies. Applications include recognising everyday human activities, distinguishing drone from birds, and identifying different types of vehicles. However, noisy time-frequency spectrograms can significantly affect the performance of the classifier and must be tackled using appropriate denoising algorithms. In recent years, deep learning algorithms have spawned many deep neural network-based denoising algorithms. For these methods, noise modelling is the most important part and is used to assist in training. In this paper, we decompose the problem and propose a novel denoising scheme: first, a Generative Adversarial Network (GAN) is used to learn the noise distribution and correlation from the real-world environment; then, a simulator is used to generate clean Micro-Doppler spectrograms; finally, the generated noise and clean simulation data are combined as the training data to train a Convolutional Neural Network (CNN) denoiser. In experiments, we qualitatively and quantitatively analyzed this procedure on both simulation and measurement data. Besides, the idea of learning from natural noise can be applied well to other existing frameworks and demonstrate greater performance than other noise models.
ICI-Aware Parameter Estimation for MIMO-OFDM Radar via APES Spatial Filtering
Authors: Musa Furkan Keskin, Henk Wymeersch, Visa Koivunen
Date: 2021-02-12 00:00:00
Tasks: parameter estimation

We propose a novel three-stage delay-Doppler-angle estimation algorithm for a MIMO-OFDM radar in the presence of inter-carrier interference (ICI). First, leveraging the observation that spatial covariance matrix is independent of target delays and Dopplers, we perform angle estimation via the MUSIC algorithm. For each estimated angle, we next formulate the radar delay-Doppler estimation as a joint carrier frequency offset (CFO) and channel estimation problem via an APES (amplitude and phase estimation) spatial filtering approach by transforming the delay-Doppler parameterized radar channel into an unstructured form. In the final stage, delay and Doppler of each target can be recovered from target-specific channel estimates over time and frequency. Simulation results illustrate the superior performance of the proposed algorithm in high-mobility scenarios.