pwc-archive/papers-with-abstracts huggingface dataset:
Stochastic Hybrid Combining Design for Quantized Massive MIMO Systems
Authors: Yalin Wang, Xihan Chen, Yunlong Cai, Lajos Hanzo
Date: 2020-08-24 00:00:00
Tasks: Quantization, Stochastic Optimization

Both the power-dissipation and cost of massive multiple-input multiple-output (mMIMO) systems may be substantially reduced by using low-resolution analog-to-digital converters (LADCs) at the receivers. However, both the coarse quantization of LADCs and the inaccurate instantaneous channel state information (ICSI) degrade the performance of quantized mMIMO systems. To overcome these challenges, we propose a novel stochastic hybrid analog-digital combiner (SHC) scheme for adapting the hybrid combiner to the long-term statistics of the channel state information (SCSI). We seek to minimize the transmit power by jointly optimizing the SHC subject to average rate constraints. For the sake of solving the resultant nonconvex stochastic optimization problem, we develop a relaxed stochastic successive convex approximation (RSSCA) algorithm. Simulations are carried out to confirm the benefits of our proposed scheme over the benchmarkers.
Rate-Splitting Multiple Access for Multigroup Multicast and Multibeam Satellite Systems
Authors: Longfei Yin, Bruno Clerckx
Date: 2020-08-12 00:00:00
Tasks: Fairness, Management

This work focuses on the promising Rate-Splitting Multiple Access (RSMA) and its beamforming design problem to achieve max-min fairness (MMF) among multiple co-channel multicast groups with imperfect channel state information at the transmitter (CSIT). Contrary to the conventional linear precoding (NoRS) that relies on fully treating any residual interference as noise, we consider a novel multigroup multicast beamforming strategy based on RSMA. RSMA relies on linearly precoded Rate-Splitting (RS) at the transmitter and Successive Interference Cancellation (SIC) at the receivers, and has recently been shown to enable a flexible framework for non-orthogonal transmission and robust interference management in multi-antenna wireless networks. In this work, we characterize the MMF Degrees-of-Freedom (DoF) achieved by RS and NoRS in multigroup multicast with imperfect CSIT and demonstrate the benefits of RS strategies for both underloaded and overloaded scenarios. Motivated by the DoF analysis, we then formulate a generic transmit power constrained optimization problem to achieve MMF rate performance. The superiority of RS-based multigroup multicast beamforming compared with NoRS is demonstrated via simulations in both terrestrial and multibeam satellite systems. In particular, due to the characteristics and challenges of multibeam satellite communications, our proposed RS strategy is shown promising to manage its interbeam interference.
Widely-distributed Radar Imaging Based on Consensus ADMM
Authors: Ruizhi Hu, Bhavani Shankar Mysore Rama Rao, Ahmed Murtada, Mohammad Alaee-Kerahroodi, Björn Ottersten
Date: 2020-11-04 00:00:00
Tasks: Distributed Optimization, Diversity

A widely-distributed radar system is a promising architecture to enhance radar imaging performance. However, most existing algorithms rely on isotropic scattering assumption, which is only satisfied in collocated radar systems. Moreover, due to noise and imaging model imperfections, artifacts such as layovers are common in radar images. In this paper, a novel $l_1$-regularized, consensus alternating direction method of multipliers (CADMM) based algorithm is proposed to mitigate artifacts by exploiting a widely-distributed radar system's spatial diversity. By imposing the consensus constraints on the local images formed by distributed antenna clusters and solving the resulting distributed optimization problem, the scenario's spatial-invariant common features are retained. Simultaneously, the spatial-variant artifacts are mitigated, and it will finally converge to a high-quality global image in the consensus of all distributed measurements. The proposed algorithm outperforms the joint sparsity-based composite imaging (JSC) algorithm in terms of artifacts mitigation. It can also reduce the computation and storage burden of large-scale imaging problems through its distributed and parallelizable optimization scheme.
Joint Constellation Rotation and Symbol-level Precoding Optimization in the Downlink of Multiuser MISO Channels
Authors: Maha Alodeh, Björn Ottersten
Date: 2020-11-08 00:00:00

This paper tackles the problem of the simultaneous interference among the multiple users in the downlink of a wireless multiantenna system. In order to exploit the multiuser interference and transform it into useful power at the receiver side, symbol-level precoding is utilized through joint exploitation of the data from each individual user and the channel state information. This paper introduces a complexity reduction scheme that exploits the constellations symmetry to reduce the number of possible calculations and the number of constraints in the symbol-level optimization problem. This paper also proposes a new transmission technique that jointly optimizes the transmit symbol-level precoding and the constellation rotation of the data stream for each user. The purpose is to increase the probability of having constructive interference, and thus better performance. We focus on improving the energy efficiency by reducing the required power to satisfy certain quality of service constraints through solving a symbol-level precoding problem and finding the optimal phase by which each user constellation should be rotated. This problem is non-convex, therefore, we propose an efficient branch-and-bound algorithm combined with semidefinite relaxation to solve the problem and find an efficient solution. Numerical results are presented in a comparative fashion to show the effectiveness of the proposed technique, which outperforms the state-of-the-art symbol-level precoding schemes in terms of energy efficiency, and power consumption.
RFEye in the Sky
Authors: Maqsood Ahamed Abdul Careem, Jorge Gomez, Dola Saha, Aveek Dutta
Date: 2020-11-07 00:00:00
Tasks: Position

We introduce RFEye, a generalized technique to locate signals independent of the waveform, using a single Unmanned Aerial Vehicle (UAV) equipped with only one omnidirectional antenna. This is achieved by acquiring signals from uncoordinated positions within a sphere of 1-meter radius at two nearby locations and formulating an asynchronous, distributed receiver beamforming at the UAV to compute the Direction of Arrival (DoA) from the unknown transmitter. The proposed method includes four steps: 1) Blind detection and extraction of unique signature in the signal to be localized, 2) Asynchronous signal acquisition and conditioning, 3) DoA calculation by creating a virtual distributed antenna array at UAV and 4) Obtaining position fix of emitter using DoA from two locations. These steps are analyzed for various sources of error, computational complexity and compared with widely used signal subspace-based DoA estimation algorithms. RFEye is implemented using an Intel-Aero UAV, equipped with a USRP B205 software-defined radio to acquire signals from a ground emitter. Practical outdoor experiments show that RFEye achieves a median accuracy of 1.03m in 2D and 2.5m in 3D for Wi-Fi, and 1.15m in 2D and 2.7m in 3D for LoRa (Long Range) waveforms, and is robust to external factors like wind and UAV position errors.
Multi-Antenna Data-Driven Eavesdropping Attacks and Symbol-Level Precoding Countermeasures
Authors: Abderrahmane Mayouche, Wallace A. Martins, Christos G. Tsinos, Symeon Chatzinotas, Björn Ottersten
Date: 2020-11-07 00:00:00

In this work, we consider secure communications in wireless multi-user (MU) multiple-input single-output (MISO) systems with channel coding in the presence of a multi-antenna eavesdropper (Eve). In this setting, we exploit machine learning (ML) tools to design soft and hard decoding schemes by using precoded pilot symbols as training data. In this context, we propose ML frameworks for decoders that allow an Eve to determine the transmitted message with high accuracy. We thereby show that MU-MISO systems are vulnerable to such eavesdropping attacks even when relatively secure transmission techniques are employed, such as symbol-level precoding (SLP). To counteract this attack, we propose two novel SLP-based schemes that increase the bit-error rate at Eve by impeding the learning process. We design these two security-enhanced schemes to meet different requirements regarding complexity, security, and power consumption. Simulation results validate both the ML-based eavesdropping attacks as well as the countermeasures, and show that the gain in security is achieved without affecting the decoding performance at the intended users.
Two Types of Mixed Orthogonal Frequency Division Multiplexing (X-OFDM) Waveform for Optical Wireless Communication
Authors: Xu Li, Jingjing Huang, Yibo Lyu, Rui Ni, Jiajin Luo, Junping Zhang
Date: 2020-11-07 00:00:00

The optical wireless communication (OWC) with the intensity modulation (IM), requires the modulated radio frequency (RF) signal to be real and non-negative. To satisfy the requirements, this paper proposes two types of mixed orthogonal frequency division multiplexing (X-OFDM) waveform. The Hermitian symmetry (HS) character of the sub-carriers in the frequency domain, guarantees the signal in the time domain to be real, which reduces the spectral efficiency to $1/2$. For the odd sub-carriers in the frequency domain, the signal in the time domain after the inverse fast fourier transform (IFFT) is antisymmetric. For the even sub-carriers in the frequency domain, the signal in the time domain after the IFFT is symmetric. Based on the antisymmetric and symmetric characters, the two types of X-OFDM waveform are designed to guarantee the signal in the time domain to be non-negative, where the direct current (DC) bias is not needed. With $N$ sub-carriers in the frequency domain, the generated signal in the time domain has $3N/2$ points, which further reduces the spectral efficiency to $1/3 = 1/2 \times 2/3$. The numerical simulations show that, the two types of X-OFDM waveform greatly enhance the power efficiency considering the OWC channel with the signal-dependent noise and/or the signal-independent noise.
Predicting of shear wave velocity using Artificial Neural Networks
Authors: Franklin Adjei, Priscilla Afare, Teddy Osae
Date: 2020-11-07 00:00:00

Shear wave velocity is an important parameter for determining lithology, porosity and the dynamic properties in geo-mechanical studies. However, due to time and cost limitations, shear wave velocity is not available at all intervals and in all wells. In this paper, well logs with strong correlation to shear wave velocity were determined and used to predict the shear wave velocity for the Tano North Field. Four different methods were used to estimate the shear wave velocity under three different conditions. Then, based on obtained coefficient of determination and average absolute percent relative error between real and predicted values of shear wave velocity, the final results were compared. The results of this work demonstrated that the neural network based on multiple variables can estimate the shear wave velocity better than other methods used.
On the Mutual Interference between Spaceborne SARs: Modeling, Characterization, and Mitigation
Authors: Huizhang Yang, Mingliang Tao, Shengyao Chen, Feng Xi, Zhong Liu
Date: 2020-10-14 00:00:00

Since by the International Telecommunications Union (ITU) regulatory the radio spectrum available to spaceborne synthetic aperture radar (SAR) is restricted to certain limited frequency intervals, there are many different spaceborne SAR systems sharing common frequency bands. Due to this reason, it is reported that two spaceborne SARs at orbit cross positions can potentially cause severe mutual interference. Specifically, the transmitting signal of a SAR, typically linear frequency modulated (LFM), can be directly received by the side or back lobes of another SAR's antenna, causing radiometric artefacts in the focused image. This paper tries to model and characterize the artefacts, and study efficient methods for mitigating them. To this end, we formulate an analytical model for describing the artefact, which reveals that the mutual interference can introduce a two-dimensional LFM radiometric artefact in image domain with a limited spatial extent. We show that the artefact is low-rank based on range-azimuth decoupling analysis and two-dimensional high-order Taylor expansion. Based on the low rank model, we show that two methods, i.e., principle component analysis and its robust variant, can be adopted to efficiently mitigate the artefact via processing in image domain. The former method has the advantage of fast processing speed, for example, a sub-swath of Sentinel-1 interferometric wide swath image can be processed within 70 seconds via block-wise operation, whereas the latter provides improved accuracy for sparse point-like scatterers. Experiment results demonstrate that the radiometric artefacts caused by mutual interference in Sentinel-1 level-1 images can be efficiently mitigated via the proposed methods.
Spatial Signal Strength Prediction using 3D Maps and Deep Learning
Authors: Enes Krijestorac, Samer Hanna, Danijela Cabric
Date: 2020-11-06 00:00:00
Tasks: Deep Learning, Prediction

Machine learning (ML) and artificial neural networks (ANNs) have been successfully applied to simulating complex physics by learning physics models thanks to large data. Inspired by the successes of ANNs in physics modeling, we use deep neural networks (DNNs) to predict the radio signal strength field in an urban environment. Our algorithm relies on samples of signal strength collected across the prediction space and a 3D map of the environment, which enables it to predict the scattering of radio waves through the environment. While already extensive body of research exists in spatial signal strength prediction, our approach differs from most existing approaches in that it does not require the knowledge of the transmitter location, it does not require side channel information such as attenuation and shadowing parameters, and it is the first work, to the best of our knowledge, to use 3D maps to accomplish the task of signal strength prediction. This algorithm is developed for the purpose of placement optimization of a UAV or mobile robot to maximize the signal strength to or from a stationary transceiver but it also holds relevance to dynamic spectrum access networks, cellular coverage design, power control algorithms, etc.
Real-time noise cancellation with Deep Learning
Authors: Sama Daryanavard, Lucía Muñoz Bohollo, Henry Cowan, Bernd Porr, Ravinder Dahiya
Date: 2020-11-06 00:00:00
Tasks: Deep Learning, EEG, Electroencephalogram (EEG)

Biological measurements are often contaminated with large amounts of non-stationary noise which require effective noise reduction techniques. We present a new real-time deep learning algorithm which produces adaptively a signal opposing the noise so that destructive interference occurs. As a proof of concept, we demonstrate the algorithm's performance by reducing electromyogram noise in electroencephalograms with the usage of a custom, flexible, 3D-printed, compound electrode. With this setup, an average of 4dB and a maximum of 10dB improvement of the signal-to-noise ratio of the EEG was achieved by removing wide band muscle noise. This concept has the potential to not only adaptively improve the signal-to-noise ratio of EEG but can be applied to a wide range of biological, industrial and consumer applications such as industrial sensing or noise cancelling headphones.
Low-Power Wireless Wearable ECG Monitoring Chestbelt Based on Ferroelectric Microprocessor
Authors: Zhendong Ai, Zihan Wang, Wei Cui
Date: 2020-11-06 00:00:00

Since cadiovascular disease (CVD) posts a heavy threat to people's health, long-term electrocardiogram (ECG) monitoring is of great value for the improvement of treatment. To realize remote long-term ECG monitoring, a low-power wireless wearable ECG monitoring device is proposed in this paper. The ECG monitoring device, abbreviated as ECGM, is designed based on ferroelectric microprocessor which provides ultra-low power consumption and contains four parts-MCU, BLE, Sensors and Power. The MCU part means circuit of MSP430FR2433, the core of ECGM. The BLE part is the CC2640R2F module applied for wireless transmission of the collected bio-signal data. And the sensors part includes several sensors like BMD101 used for monitoring bio-signals and motion of the wearer, while the Power part consists of battery circuit, charging circuit and 3.3V/1.8V/4.4V power supply circuit. The ECGM first collects ECG signals from the fabric electrodes adhered to wearers' chest, preprocesses the signals to eliminate the injected noise, and then transmit the output data to wearers' hand-held mobile phones through Bluetooth low energy (BLE). The wearers are enabled to acquire ECGs and other physiological parameters on their phones as well as some corresponding suggestions. The novelty of the system lies in the combination of low-power ECG sensor chip with ferroelectric microprocessor, thus achieving ultra-low power consumption and high signal quality.
Physical Layer Security of Intelligent Reflective Surface Aided NOMA Networks
Authors: Zhiqing Tang, Tianwei Hou, Yuanwei Liu, Jiankang Zhang, Lajos Hanzo
Date: 2020-11-06 00:00:00
Tasks: Diversity, Unity

Intelligent reflective surface (IRS) technology is emerging as a promising performance enhancement technique for next-generation wireless networks. Hence, we investigate the physical layer security of the downlink in IRS-aided non-orthogonal multiple access networks in the presence of an eavesdropper, where an IRS is deployed for enhancing the quality by assisting the cell-edge user to communicate with the base station. To characterize the network's performance, the expected value of the new channel statistics is derived for the reflected links in the case of Nakagami-m fading. Furthermore, the performance of the proposed network is evaluated both in terms of the secrecy outage probability (SOP) and the average secrecy capacity (ASC). The closed-form expressions of the SOP and the ASC are derived. We also study the impact of various network parameters on the overall performance of the network considered. To obtain further insights, the secrecy diversity orders and the high signal-to-noise ratio slopes are obtained. We finally show that: 1) the expectation of the channel gain in the reflected links is determined both by the number of IRSs and by the Nakagami-m fading parameters; 2) The SOP of both receiver 1 and receiver 2 becomes unity, when the number of IRSs is sufficiently high; 3) The secrecy diversity orders are affected both by the number of IRSs and by the Nakagami-m fading parameters, whereas the high-SNR slopes are not affected by these parameters. Our Monte-Carlo simulations perfectly demonstrate the analytical results.
Reconfigurable Intelligent Surfaces for Doppler Effect and Multipath Fading Mitigation
Authors: Ertugrul Basar
Date: 2019-12-09 00:00:00

Extensive research has already started on 6G and beyond wireless technologies due to the envisioned new use-cases and potential new requirements for future wireless networks. Although a plethora of modern physical layer solutions have been introduced in the last few decades, it is undeniable that a level of saturation has been reached in terms of the available spectrum, adapted modulation/coding solutions and accordingly the maximum capacity. Within this context, communications through reconfigurable intelligent surfaces (RISs), which enable novel and effective functionalities including wave absorption, tuneable anomalous reflection, and reflection phase modification, appear as a potential candidate to overcome the inherent drawbacks of legacy wireless systems. The core idea of RISs is the transformation of the uncontrollable and random wireless propagation environment into a reconfigurable communication system entity that plays an active role in forwarding information. In this paper, the well-known multipath fading phenomenon is revisited in mobile wireless communication systems, and novel and unique solutions are introduced from the perspective of RISs. The feasibility of eliminating or mitigating the multipath fading effect stemming from the movement of mobile receivers is also investigated by utilizing the RISs. It is shown that rapid fluctuations in the received signal strength due to the Doppler effect can be effectively reduced by using the real-time tuneable RISs. It is also proven that the multipath fading effect can be totally eliminated when all reflectors in a propagation environment are coated with RISs, while even a few RISs can significantly reduce the Doppler spread as well as the deep fades in the received signal for general propagation environments with several interacting objects.
Intelligent Reflecting Surface Aided Vehicular Communications
Authors: Dilin Dampahalage, K. B. Shashika Manosha, Nandana Rajatheva, MattiLatva-aho
Date: 2020-11-05 00:00:00

We investigate the use of an intelligent reflecting surface (IRS) in a millimeter-wave (mmWave) vehicular communication network. An intelligent reflecting surface consists of passive elements, which can reflect the incoming signals with adjustable phase shifts. By properly tuning the phase shifts we can improve link performance. This is known as phase optimization or passive beamforming. We consider the problem of rate maximization in the uplink, which utilizes an IRS. However, using an IRS brings more challenges in terms of channel estimation. We propose two schemes to reduce the channel estimation overhead associated with utilizing an IRS. One method uses the grouping of reflecting elements and the other one performs passive beamforming based on the position of the device. Numerical results show IRS can bring significant improvements to existing communication. Furthermore, to get a practical insight into vehicular communications aided by an IRS, we use a commercial ray-tracing tool to evaluate the performance.
Riemannian information gradient methods for the parameter estimation of ECD: Some applications in image processing
Authors: Jialun Zhou, Salem Said, Yannick Berthoumieu
Date: 2020-11-05 00:00:00
Tasks: Colorization, parameter estimation, Texture Classification

Elliptically-contoured distributions (ECD) play a significant role, in computer vision, image processing, radar, and biomedical signal processing. Maximum likelihood. estimation (MLE) of ECD leads to a system of non-linear equations, most-often addressed using fixed-point (FP) methods. Unfortunately, the computation time required for these methods is unacceptably long, for large-scale or high-dimensional datasets. To overcome this difficulty, the present work introduces a Riemannian optimisation method, the information stochastic gradient (ISG). The ISG is an online (recursive) method, which achieves the same performance as MLE, for large-scale datasets, while requiring modest memory and time resources. To develop the ISG method, the Riemannian information gradient is derived taking into account the product manifold associated to the underlying parameter space of the ECD. From this information gradient definition, we define also, the information deterministic gradient (IDG), an offline (batch) method, which is an alternative, for moderate-sized datasets. The present work formulates these two methods, and demonstrates their performance through numerical simulations. Two applications, to image re-colorization, and to texture classification, are also worked out.