pwc-archive/papers-with-abstracts huggingface dataset:
Energy Model for UAV Communications: Experimental Validation and Model Generalization
Authors: Ning Gao, Yong Zeng, Jian Wang, Di wu, Chaoyue Zhang, Qingheng Song, Jiachen Qian, Shi Jin
Date: 2020-05-04 00:00:00
Tasks: model

Wireless communication involving unmanned aerial vehicles (UAVs) is expected to play an important role in future wireless networks. However, different from conventional terrestrial communication systems, UAVs typically have rather limited onboard energy on one hand, and require additional flying energy consumption on the other hand, which renders energy-efficient UAV communication with smart energy expenditure of paramount importance. In this paper, via extensive flight experiments, we aim to firstly validate the recently derived theoretical energy model for rotary-wing UAVs, and then develop a general model for those complicated flight scenarios where rigorous theoretical model derivation is quite challenging, if not impossible. Specifically, we first investigate how UAV power consumption varies with its flying speed for the simplest straight-and-level flight. With about 12,000 valid power-speed data points collected, we first apply the model-based curve fitting to obtain the modelling parameters based on the theoretical closed-form energy model in the existing literature. In addition, in order to exclude the potential bias caused by the theoretical energy model, the obtained measurement data is also trained using a model-free deep neural network. It is found that the obtained curve from both methods can match quite well with the theoretical energy model. Next, we further extend the study to arbitrary 2-dimensional (2-D) flight, where, to our best knowledge, no rigorous theoretical derivation is available for the closed-form energy model as a function of its flying speed, direction, and acceleration. To fill the gap, we first propose a heuristic energy model for these more complicated cases, and then provide experimental validation based on the measurement results for circular level flight.
Spectral Efficiency and Energy Efficiency Tradeoff in Massive MIMO Downlink Transmission with Statistical CSIT
Authors: Li You, Jiayuan Xiong, Alessio Zappone, Wenjin Wang, Xiqi Gao
Date: 2020-04-07 00:00:00

As a key technology for future wireless networks, massive multiple-input multiple-output (MIMO) can significantly improve the energy efficiency (EE) and spectral efficiency (SE), and the performance is highly dependant on the degree of the available channel state information (CSI). While most existing works on massive MIMO focused on the case where the instantaneous CSI at the transmitter (CSIT) is available, it is usually not an easy task to obtain precise instantaneous CSIT. In this paper, we investigate EE-SE tradeoff in single-cell massive MIMO downlink transmission with statistical CSIT. To this end, we aim to optimize the system resource efficiency (RE), which is capable of striking an EE-SE balance. We first figure out a closed-form solution for the eigenvectors of the optimal transmit covariance matrices of different user terminals, which indicates that beam domain is in favor of performing RE optimal transmission in massive MIMO downlink. Based on this insight, the RE optimization precoding design is reduced to a real-valued power allocation problem. Exploiting the techniques of sequential optimization and random matrix theory, we further propose a low-complexity suboptimal two-layer water-filling-structured power allocation algorithm. Numerical results illustrate the effectiveness and near-optimal performance of the proposed statistical CSI aided RE optimization approach.
Data center top of rack switch to multiple spine switches optical wireless uplinks
Authors: Abrar S. Alhazmi, Osama Zwaid Alsulami, T. E. H. El-Gorashi, Mohammed T. Alresheedi, Jaafar M. H. Elmirghani
Date: 2020-05-03 00:00:00
Tasks: Diversity

Infrared (IR) uplinks can achieve high data rates, which are essential in a range of applications. This paper introduces a novel approach to enable data centre uplink communication. We introduce a novel method to enable communication between racks and spine switches. In our proposed data centre, we consider three racks, each of which has its own angle diversity transmitter (ADT) that is located on top of the rack. Four wide field of view receivers are fixed to the ceiling of the data centre. Each such receiver is connected to a spine switch. We evaluate the performance of our proposed system when each link operates at a data rate above 2.8 Gb/s. Multiple links can be used to achieve higher data rates using the space or wavelength dimensions. The results show that our proposed system has the ability to work at a high data rate with good performance while using simple on-off-keying.
Deep Neural Network-Based Quantized Signal Reconstruction for DOA Estimation
Authors: Weifeng Han, Peng Chen, Zhenxin Cao
Date: 2020-05-03 00:00:00
Tasks: Denoising, Quantization

For a massive multiple-input-multiple-output (MIMO) system using intelligent reflecting surface (IRS) equipped with radio frequency (RF) chains, the multi-channel RF chains are expensive compared to passive IRS, especially, when the high-resolution and high-speed analog to digital converters (ADC) are used in each RF channel. In this letter, a direction of angle (DOA) estimation problem is investigated with low-cost ADC in IRS, and we propose a deep neural network (DNN) as a recovery method for the low-resolution sampled signal. Different from the existing denoising convolutional neural network (DnCNN) for Gaussian noise, the proposed DNN with fully connected (FC) layers estimates the quantization noise caused by the ADC. Then, the denoised signal is subjected to the DOA estimation, and the recovery performance for the quantized signal is evaluated by DOA estimation. Simulation results show that under the same training conditions, the better reconstruction performance is achieved by the proposed network than state-of-the-art methods. The performance of the DOA estimation using 1-bit ADC is improved to exceed that using 2-bit ADC.
Energy Efficient User Clustering, Hybrid Precoding and Power Optimization in Terahertz MIMO-NOMA Systems
Authors: Haijun Zhang, Haisen Zhang, Wei Liu, Keping Long, Jiangbo Dong, Victor C. M. Leung
Date: 2020-05-03 00:00:00
Tasks: Clustering

Terahertz (THz) band communication has been widely studied to meet the future demand for ultra-high capacity. In addition, multi-input multi-output (MIMO) technique and non-orthogonal multiple access (NOMA) technique with multi-antenna also enable the network to carry more users and provide multiplexing gain. In this paper, we study the maximization of energy efficiency (EE) problem in THz-NOMA-MIMO systems for the first time. And the original optimization problem is divided into user clustering, hybrid precoding and power optimization. Based on channel correlation characteristics, a fast convergence scheme for user clustering in THz-NOMA-MIMO system using enhanced K-means machine learning algorithm is proposed. Considering the power consumption and implementation complexity, the hybrid precoding scheme based on the sub-connection structure is adopted. Considering the fronthaul link capacity constraint, we design a distributed alternating direction method of multipliers (ADMM) algorithm for power allocation to maximize the EE of THz-NOMA cache-enabled system with imperfect successive interference cancellation (SIC). The simulation results show that the proposed user clustering scheme can achieve faster convergence and higher EE, the design of the hybrid precoding of the sub-connection structure can achieve lower power consumption and power optimization can achieve a higher EE for the THz cache-enabled network.
Robust Adaptive Beam Tracking for Mobile Millimetre Wave Communications
Authors: Chunshan Liu, Min Li, Lou Zhao, Philip Whiting, Stephen V. Hanly, Iain B. Collings, MinJian Zhao
Date: 2020-05-03 00:00:00

Millimetre wave (mmWave) beam tracking is a challenging task because tracking algorithms are required to provide consistent high accuracy with low probability of loss of track and minimal overhead. To meet these requirements, we propose in this paper a new analog beam tracking framework namely Adaptive Tracking with Stochastic Control (ATSC). Under this framework, beam direction updates are made using a novel mechanism based on measurements taken from only two beam directions perturbed from the current data beam. To achieve high tracking accuracy and reliability, we provide a systematic approach to jointly optimise the algorithm parameters. The complete framework includes a method for adapting the tracking rate together with a criterion for realignment (perceived loss of track). ATSC adapts the amount of tracking overhead that matches well to the mobility level, without incurring frequent loss of track, as verified by an extensive set of experiments under both representative statistical channel models as well as realistic urban scenarios simulated by ray-tracing software. In particular, numerical results show that ATSC can track dominant channel directions with high accuracy for vehicles moving at 72 km/hour in complicated urban scenarios, with an overhead of less than 1\%.
Graphene Hypersurface for Manipulation of THz Waves
Authors: Sasmita Dash, Christos Liaskos, Ian F. Akyildiz, Andreas Pitsillides
Date: 2020-05-03 00:00:00

In this work, we investigated graphene hypersurface (HSF) for the manipulation of THz waves. The graphene HSF structure is consists of a periodic array of graphene unit cells deposited on silicon substrate and terminated by a metallic ground plane. The performance of the proposed HSF is numerically analyzed. Electromagnetic parameters of HSF such as permeability, permittivity, and impedance are studied. The proposed graphene HSF has active control over absorption, reflection, and transmission of THz waves. The graphene HSF provides perfect absorption, zero reflection and zero transmission at resonance. Moreover, the graphene HSF structure has the advantage of anomalous reflection and frequency reconfiguration. Incident waves can be reflected in the desired direction, depending on the phase gradient of the HSF and the perfect absorption is maintained at all reconfigurable frequencies upon reconfiguration. The results reveal the effectiveness of the graphene HSF for the manipulation of THz waves.
Reconfigurable Intelligent Surface assisted Two-Way Communications: Performance Analysis and Optimization
Authors: Saman Atapattu, Rongfei Fan, Prathapasinghe Dharmawansa, Gongpu Wang, Jamie Evans, Theodoros A. Tsiftsis
Date: 2020-01-22 00:00:00
Tasks: Vocal Bursts Valence Prediction

In this paper, we investigate the two-way communication between two users assisted by a re-configurable intelligent surface (RIS). The scheme that two users communicate simultaneously over Rayleigh fading channels is considered. The channels between the two users and RIS can either be reciprocal or non-reciprocal. For reciprocal channels, we determine the optimal phases at the RIS to maximize the signal-to-interference-plus-noise ratio (SINR). We then derive exact closed-form expressions for the outage probability and spectral efficiency for single-element RIS. By capitalizing the insights obtained from the single-element analysis, we introduce a gamma approximation to model the product of Rayleigh random variables which is useful for the evaluation of the performance metrics in multiple-element RIS. Asymptotic analysis shows that the outage decreases at $\left(\log(\rho)/\rho\right)^L$ rate where $L$ is the number of elements, whereas the spectral efficiency increases at $\log(\rho)$ rate at large average SINR $\rho$. For non-reciprocal channels, the minimum user SINR is targeted to be maximized. For single-element RIS, closed-form solution is derived whereas for multiple-element RIS the problem turns out to be non-convex. The latter one is solved through semidefinite programming relaxation and a proposed greedy-iterative method, which can achieve higher performance and lower computational complexity, respectively.
Doppler-Resilient 802.11ad-Based Ultra-Short Range Automotive Joint Radar-Communications System
Authors: Gaurav Duggal, Shelly Vishwakarma, Kumar Vijay Mishra, Shobha Sundar Ram
Date: 2019-02-04 00:00:00

We present an ultra-short range IEEE 802.11ad-based automotive joint radar-communications (JRC) framework, wherein we improve the radar's Doppler resilience by incorporating Prouhet-Thue-Morse sequences in the preamble. The proposed processing reveals detailed micro-features of common automotive objects verified through extended scattering center models of animated pedestrian, bicycle, and car targets. Numerical experiments demonstrate $2.5$% reduction in the probability-of-false-alarm at low signal-to-noise-ratios and improvement in the peak-to-sidelobe level dynamic range up to Doppler velocities of $\pm144$ km/hr over conventional 802.11ad JRC.
Open Set Wireless Transmitter Authorization: Deep Learning Approaches and Dataset Considerations
Authors: Samer Hanna, Samurdhi Karunaratne, Danijela Cabric
Date: 2020-05-03 00:00:00
Tasks: Anomaly Detection, Open Set Learning, Outlier Detection

Due to imperfections in transmitters' hardware, wireless signals can be used to verify their identity in an authorization system. While deep learning was proposed for transmitter identification, the majority of the work has focused on classification among a closed set of transmitters. Malicious transmitters outside this closed set will be misclassified, jeopardizing the authorization system. In this paper, we consider the problem of recognizing authorized transmitters and rejecting new transmitters. To address this problem, we adapt the most prominent approaches from the open set recognition and anomaly detection literature to the problem. We study how these approaches scale with the required number of authorized transmitters. We propose using a known set of unauthorized transmitters to assist the training and study its impact. The evaluation procedure takes into consideration that some transmitters might be more similar than others and nuances these effects. The robustness of the RF authorization with respect to temporal changes in fingerprints is also considered in the evaluation. When using 10 authorized and 50 known unauthorized WiFi transmitters from a publicly accessible testbed, we were able to achieve an outlier detection accuracy of 98% on the same day test set and 80% on the different day test set.
Reconfigurable Intelligent Surfaces for Wireless Communications: Principles, Challenges, and Opportunities
Authors: Mohamed A. ElMossallamy, Hongliang Zhang, Lingyang Song, Karim G. Seddik, Zhu Han, Geoffrey Ye Li
Date: 2020-05-02 00:00:00

Recently there has been a flurry of research on the use of reconfigurable intelligent surfaces (RIS) in wireless networks to create smart radio environments. In a smart radio environment, surfaces are capable of manipulating the propagation of incident electromagnetic waves in a programmable manner to actively alter the channel realization, which turns the wireless channel into a controllable system block that can be optimized to improve overall system performance. In this article, we provide a tutorial overview of reconfigurable intelligent surfaces (RIS) for wireless communications. We describe the working principles of reconfigurable intelligent surfaces (RIS) and elaborate on different candidate implementations using metasurfaces and reflectarrays. We discuss the channel models suitable for both implementations and examine the feasibility of obtaining accurate channel estimates. Furthermore, we discuss the aspects that differentiate RIS optimization from precoding for traditional MIMO arrays highlighting both the arising challenges and the potential opportunities associated with this emerging technology. Finally, we present numerical results to illustrate the power of an RIS in shaping the key properties of a MIMO channel.
An Ensemble Deep Learning-based Cyber-Attack Detection in Industrial Control System
Authors: Abdulrahman Al-Abassi, Hadis Karimipour, Ali Dehghantanha, Reza M. Parizi
Date: 2020-05-02 00:00:00
Tasks: Cyber Attack Detection, Intrusion Detection, Representation Learning

The integration of communication networks and the Internet of Things (IoT) in Industrial Control Systems (ICSs) increases their vulnerability towards cyber-attacks, causing devastating outcomes. Traditional Intrusion Detection Systems (IDSs), which are mainly developed to support Information Technology (IT) systems, count vastly on predefined models and are trained mostly on specific cyber-attacks. Besides, most IDSs do not consider the imbalanced nature of ICS datasets, thereby suffering from low accuracy and high false positive on real datasets. In this paper, we propose a deep representation learning model to construct new balanced representations of the imbalanced dataset. The new representations are fed into an ensemble deep learning attack detection model specifically designed for an ICS environment. The proposed attack detection model leverages Deep Neural Network (DNN) and Decision Tree (DT) classifiers to detect cyber-attacks from the new representations. The performance of the proposed model is evaluated based on 10-fold cross-validation on two real ICS datasets. The results show that the proposed method outperforms conventional classifiers, including Random Forest (RF), DNN, and AdaBoost, as well as recent existing models in the literature. The proposed approach is a generalized technique, which can be implemented in existing ICS infrastructures with minimum changes.
Compressed-Sensing based Beam Detection in 5G NR Initial Access
Authors: Junmo Sung, Brian L. Evans
Date: 2020-05-02 00:00:00
Tasks: compressed sensing

To support millimeter wave (mmWave) frequency bands in cellular communications, both the base station and the mobile platform utilize large antenna arrays to steer narrow beams towards each other to compensate the path loss and improve communication performance. The time-frequency resource allocated for initial access, however, is limited, which gives rise to need for efficient approaches for beam detection. For hybrid analog-digital beamforming (HB) architectures, which are used to reduce power consumption, we propose a compressed sensing (CS) based approach for 5G initial access beam detection that is for a HB architecture and that is compliant with the 3GPP standard. The CS-based approach is compared with the exhaustive search in terms of beam detection accuracy and by simulation is shown to outperform. Up to 256 antennas are considered, and the importance of a careful codebook design is reaffirmed.
Spectral Coexistence of LDACS and DME: Analysis via Hardware Software Co-Design in Presence of Real Channels and RF Impairments
Authors: Niharika Agrawal, S. J. Darak, Faouzi Bader
Date: 2019-10-10 00:00:00

To meet the exponentially increasing air traffic, L-band (960-1164 MHz) digital aeronautical communication system (LDACS) has been introduced. The LDACS aims to exploit the vacant spectrum between incumbent Distance Measuring Equipment (DME) signals and envisioned to follow an orthogonal frequency division multiplexing (OFDM) approach to support high-speed delay-sensitive multimedia services. This paper deals with the design and implementation of end-to-end LDACS the transceiver on the Zynq System on Chip platform, consisting of FPGA as programmable logic (PL) and ARM as processing system (PS). We consider OFDM based LDACS and improve it further using windowing and/or filtering. We propose a hardware-software co-design approach and analyze various transceiver configurations by dividing it into PL and PS. We demonstrate the flexibility offered by such a co-design approach to choose the configuration as well as word-length for a given area, delay and power constraints. The transceiver is also integrated with the programmable analog front-end to validate its functionality in the presence of various RF impairments and wireless channels and interference specific to the LDACS environment. Via in-depth performance analysis concerning parameters such as out-of-band attenuation, DME interference, bit-error-rate, word-length, and complexity, we demonstrate wide bandwidth filtered OFDM as an attractive solution for the next generation LDACS.
Nonlinear analysis of charge-pump phase-locked loop: the hold-in and pull-in ranges
Authors: N. V. Kuznetsov, A. S. Matveev, M. V. Yuldashev, R. V. Yuldashev
Date: 2020-05-02 00:00:00

In this paper a fairly complete mathematical model of CP-PLL, which reliable enough to serve as a tool for credible analysis of dynamical properties of these circuits, is studied. We refine relevant mathematical definitions of the hold-in and pull-in ranges related to the local and global stability. Stability analysis of the steady state for the charge-pump phase locked loop is non-trivial: straight-forward linearization of available CP-PLL models may lead to incorrect conclusions, because the system is not smooth near the steady state and may experience overload. In this work necessary details for local stability analysis are presented and the hold-in range is computed. An upper estimate of the pull-in range is obtained via the analysis of limit cycles. The study provided an answer to Gardner's conjecture on the similarity of transient responses of CP-PLL and equivalent classical PLL and to conjectures on the infinite pull-in range of CP-PLL with proportionally-integrating filter.
Broadband Synchronization and Compressive Channel Estimation for Hybrid mmWave MIMO Systems
Authors: Javier Rodriguez-Fernandez
Date: 2020-04-22 00:00:00

Synchronization is a fundamental procedure in cellular systems whereby an UE acquires the time and frequency information required to decode the data transmitted by a BS. Due to the necessity of using large antenna arrays to obtain the beamforming gain required to compensate for small antenna aperture, synchronization must be performed either jointly with beam training as in 5G NR, or at the low SNR regime if the high-dimensional mmWave MIMO channel is to be estimated. To circumvent this problem, this work proposes the first synchronization framework for mmWave MIMO that is robust to both TO, CFO, and PN synchronization errors and, unlike prior work, implicitly considers the use of multiple RF chains at both transmitter and receiver. I provide a theoretical analysis of the estimation problem and derive the HCRLB for the estimation of both the CFO, PN, and equivalent beamformed channels seen by the different receive RF chains. I also propose two novel algorithms to estimate the different unknown parameters, which rely on approximating the MMSE estimator for the PN and the ML estimators for both the CFO and the equivalent beamformed channels. Thereafter, I propose to use the estimates for the equivalent beamformed channels to perform compressive estimation of the high-dimensional frequency-selective mmWave MIMO channel and thus undergo data transmission. For performance evaluation, I consider the QuaDRiGa channel simulator, which implements the 5G NR channel model, and show that both compressive channel estimation without prior synchronization is possible, and the proposed approaches outperform current solutions for joint beam training and synchronization currently considered in 5G NR.