pwc-archive/papers-with-abstracts huggingface dataset:
Intelligent Reflecting Surface Aided Multi-Cell NOMA Networks
Authors: Wanli Ni, Xiao Liu, Yuanwei Liu, Hui Tian, Yue Chen
Date: 2020-12-07 00:00:00

This paper proposes a novel framework of resource allocation in intelligent reflecting surface (IRS) aided multi-cell non-orthogonal multiple access (NOMA) networks, where a sum-rate maximization problem is formulated. To address this challenging mixed-integer non-linear problem, we decompose it into an optimization problem (P1) with continuous variables and a matching problem (P2) with integer variables. For the non-convex optimization problem (P1), iterative algorithms are proposed for allocating transmit power, designing reflection matrix, and determining decoding order by invoking relaxation methods such as convex upper bound substitution, successive convex approximation and semidefinite relaxation. For the combinational problem (P2), swap matching-based algorithms are proposed to achieve a two-sided exchange-stable state among users, BSs and subchannels. Numerical results are provided for demonstrating that the sum-rate of the NOMA networks is capable of being enhanced with the aid of the IRS.
Physics-Aware Gaussian Processes in Remote Sensing
Authors: Gustau Camps-Valls, Luca Martino, Daniel H. Svendsen, Manuel Campos-Taberner, Jordi Muñoz-Marí, Valero Laparra, David Luengo, Francisco Javier García-Haro
Date: 2020-12-07 00:00:00
Tasks: Bayesian Optimization, Earth Observation, Gaussian Processes, parameter estimation, regression, Retrieval, Time Series Analysis

Earth observation from satellite sensory data poses challenging problems, where machine learning is currently a key player. In recent years, Gaussian Process (GP) regression has excelled in biophysical parameter estimation tasks from airborne and satellite observations. GP regression is based on solid Bayesian statistics and generally yields efficient and accurate parameter estimates. However, GPs are typically used for inverse modeling based on concurrent observations and in situ measurements only. Very often a forward model encoding the well-understood physical relations between the state vector and the radiance observations is available though and could be useful to improve predictions and understanding. In this work, we review three GP models that respect and learn the physics of the underlying processes in the context of both forward and inverse modeling. After reviewing the traditional application of GPs for parameter retrieval, we introduce a Joint GP (JGP) model that combines in situ measurements and simulated data in a single GP model. Then, we present a latent force model (LFM) for GP modeling that encodes ordinary differential equations to blend data-driven modeling and physical constraints of the system governing equations. The LFM performs multi-output regression, adapts to the signal characteristics, is able to cope with missing data in the time series, and provides explicit latent functions that allow system analysis and evaluation. Finally, we present an Automatic Gaussian Process Emulator (AGAPE) that approximates the forward physical model using concepts from Bayesian optimization and at the same time builds an optimally compact look-up-table for inversion. We give empirical evidence of the performance of these models through illustrative examples of vegetation monitoring and atmospheric modeling.
Mapping Leaf Area Index with a Smartphone and Gaussian Processes
Authors: Manuel Campos-Taberner, Franciso Javier García-Haro, Álvaro Moreno, María Amparo Gilabert, Sergio Sánchez-Ruiz, Beatriz Martínez, Gustau Camps-Valls
Date: 2020-12-07 00:00:00
Tasks: Gaussian Processes, GPR

Leaf area index (LAI) is a key biophysical parameter used to determine foliage cover and crop growth in environmental studies. Smartphones are nowadays ubiquitous sensor devices with high computational power, moderate cost, and high-quality sensors. A smartphone app, called PocketLAI, was recently presented and tested for acquiring ground LAI estimates. In this letter, we explore the use of state-of-the-art nonlinear Gaussian process regression (GPR) to derive spatially explicit LAI estimates over rice using ground data from PocketLAI and Landsat 8 imagery. GPR has gained popularity in recent years because of their solid Bayesian foundations that offers not only high accuracy but also confidence intervals for the retrievals. We show the first LAI maps obtained with ground data from a smartphone combined with advanced machine learning. This work compares LAI predictions and confidence intervals of the retrievals obtained with PocketLAI to those obtained with classical instruments, such as digital hemispheric photography (DHP) and LI-COR LAI-2000. This letter shows that all three instruments got comparable result but the PocketLAI is far cheaper. The proposed methodology hence opens a wide range of possible applications at moderate cost.
Gradient-based Automatic Look-Up Table Generator for Atmospheric Radiative Transfer Models
Authors: Jorge Vicent, Luis Alonso, Luca Martino, Neus Sabater, Jochem Verrelst, Gustau Camps-Valls
Date: 2020-12-07 00:00:00
Tasks: Earth Observation

Atmospheric correction of Earth Observation data is one of the most critical steps in the data processing chain of a satellite mission for successful remote sensing applications. Atmospheric Radiative Transfer Models (RTM) inversion methods are typically preferred due to their high accuracy. However, the execution of RTMs on a pixel-per-pixel basis is impractical due to their high computation time, thus large multi-dimensional look-up tables (LUTs) are precomputed for their later interpolation. To further reduce the RTM computation burden and the error in LUT interpolation, we have developed a method to automatically select the minimum and optimal set of nodes to be included in a LUT. We present the gradient-based automatic LUT generator algorithm (GALGA) which relies on the notion of an acquisition function that incorporates (a) the Jacobian evaluation of an RTM, and (b) information about the multivariate distribution of the current nodes. We illustrate the capabilities of GALGA in the automatic construction and optimization of MODerate resolution atmospheric TRANsmission (MODTRAN) LUTs for several input dimensions. Our results indicate that, when compared to a pseudo-random homogeneous distribution of the LUT nodes, GALGA reduces (1) the LUT size by $\sim$75\% and (2) the maximum interpolation relative errors by 0.5\% It is concluded that automatic LUT design might benefit from the methodology proposed in GALGA to reduce computation time and interpolation errors.
Quality-of-Transmission Estimation in Physical Impairment Aware Flexible Optical Networks
Authors: Joschua Dilly
Date: 2020-12-06 00:00:00

In this thesis the creation of nonlinear interference noise (NLIN) in the context of impairment aware flexible optical networks is investigated to estimate transmission quality. In particular, the nonlinear interference of neighboring channels (interferer) during transmission on a channel under test is studied. The modulation format of the interferer, the accumulated chromatic dispersion of the interferer, the span length and channel spacing are identified as the parameter influencing the generation of NLIN in the context of flexible optical networks. Estimation of the NLIN is done based on the evaluation of numerical simulation results and compared to recent analytical transmission models, namely the Gaussian noise model and the enhanced Gaussian noise model. In addition to nonlinear noise power, the simulations also yield information about the phase- and circular-noise contributions as well as the correlation time of the phase noise. The results are evaluated in the context of the pulse-collision picture and the influence on transmission quality is discussed.
Coverage Probability Analysis of IRS-Aided Communication Systems
Authors: Zhuangzhuang Cui, Ke Guan, Jiayi Zhang, Zhangdui Zhong
Date: 2020-12-06 00:00:00

The intelligent reflective surface (IRS) technology has received many interests in recent years, thanks to its potential uses in future wireless communications, in which one of the promising use cases is to widen coverage, especially in the line-of-sight-blocked scenarios. Therefore, it is critical to analyze the corresponding coverage probability of IRS-aided communication systems. To our best knowledge, however, previous works focusing on this issue are very limited. In this paper, we analyze the coverage probability under the Rayleigh fading channel, taking the number and size of the array elements into consideration. We first derive the exact closed-form of coverage probability for the unit element. Afterward, with the method of moment matching, the approximation of the coverage probability can be formulated as the ratio of upper incomplete Gamma function and Gamma function, allowing an arbitrary number of elements. Finally, we comprehensively evaluate the impacts of essential factors on the coverage probability, such as the coefficient of fading channel, the number and size of the element, and the angle of incidence. Overall, the paper provides a succinct and general expression of coverage probability, which can be helpful in the performance evaluation and practical implementation of the IRS.
Magnetless Circulators Based on Synthetic Angular-Momentum Bias: Recent Advances and Applications
Authors: Ahmed Kord, Andrea Alu
Date: 2020-12-06 00:00:00

In this paper, we discuss recent progress in magnet-free non-reciprocal structures based on a synthetic form of angular momentum bias imparted via spatiotemporal modulation. We discuss how such components can support metrics of performance comparable with traditional magnetic-biased ferrite devices, while at the same time offering distinct advantages in terms of reduced size, weight, and cost due to the elimination of magnetic bias. We further provide an outlook on potential applications and future directions based on these components, ranging from wireless full-duplex communications to metasurfaces and topological insulators.
SPIRAP Wireless Uplink Random Access Protocol Using Spinal Code
Authors: Dan Raphaeli, Snir Nisim
Date: 2020-12-05 00:00:00

In this paper we present SPIRAP, SPinal Random Access Protocol, a new method for multiuser detection over wireless fading channel. SPIRAP combines sequential decoding with rateless Spinal code. SPIRAP appears to be an efficient protocol for transmitting small packets in a minimally controlled network and can be attractive for the Internet of Things (IOT) applications. The algorithm applies equivalently to any other rateless code, we chose Spinal for its good performance. We show that SPIRAP may achieve higher rate than TDMA and ALOHA in some cases and without the need for users synchronization.
Hybrid Beamforming for mm-Wave Massive MIMO Systems with Partially Connected RF Architecture
Authors: Mohammad Majidzadeh, Jarkko Kaleva, Nuutti Tervo, Harri Pennanen, Antti Tolli, Matti Latva-aho
Date: 2020-12-04 00:00:00

To satisfy the capacity requirements of future mobile systems, under-utilized millimeter wave frequencies can be efficiently exploited by employing massive MIMO technology with highly directive beamforming. Hybrid analog-digital beamforming has been recognised as a promising approach for large-scale MIMO implementations with a reduced number of costly and power-hungry RF chains. In comparison to fully connected architecture, hybrid beamforming (HBF) with partially connected RF architecture is particularly appealing for the practical implementation due to less complex RF power division and combining networks. In this paper, we first formulate single- and multi-user rate maximization problems as weighted minimum mean square error (WMMSE) and derive solutions for hybrid beamformers using alternating optimization. The algorithms are designed for the full-array- and sub-array-based processing strategies of partially connected HBF architecture. In addition to the rate maximizing WMMSE solutions, we propose lower complexity sub-array-based zero-forcing algorithms. The performance of the proposed algorithms is evaluated in two different channel models, i.e., a simple geometric model and a realistic statistical millimeter wave model known as NYUSIM. The performance results of the WMMSE HBF algorithms are meant to reveal the potential of partially connected HBF and serve as upper bounds for lower complexity methods. Numerical results imply that properly designed partially connected HBF has the potential to provide an good compromise between hardware complexity and system performance in comparison to fully digital beamforming.
Accurate Frequency Domain Identification of ODEs with Arbitrary Signals
Authors: Eduardo Martini, André V. G. Cavalieri, Peter Jordan, Lutz Lesshafft
Date: 2019-07-09 00:00:00

The difficulty in frequency domain identification is that frequency components of arbitrary inputs and outputs are not related by the system's transfer function if signals are windowed. When rectangular windows are used, it is well known that this difference is related to transient effects that can be estimated alongside the systems' parameters windows. In this work, we generalize the approach for arbitrary windows, showing that signal windowing introduces additional terms in the system's equations. The formalism is useful for frequency-domain input-output analysis of a system, and also for system identification. For the latter application, the approach considerably reduces aliasing effects and allows the computation of the associated correction terms, reducing the number of parameters that need to be estimated. The system identification approach has features of the modulating-function technique, filtering out the effects of initial conditions while retaining the spectral interpretation of frequency-domain methods.
Granger-faithfulness and link orientation in network reconstruction
Authors: Mihaela Dimovska, Donatello Materassi
Date: 2020-12-03 00:00:00

Networked dynamic systems are often abstracted as directed graphs, where the observed system processes form the vertex set and directed edges are used to represent non-zero transfer functions. Recovering the exact underlying graph structure of such a networked dynamic system, given only observational data, is a challenging task. Under relatively mild well-posedness assumptions on the network dynamics, there are state-of-the-art methods which can guarantee the absence of false positives. However, in this article we prove that under the same well-posedness assumptions, there are instances of networks for which any method is susceptible to inferring false negative edges or false positive edges. Borrowing a terminology from the theory of graphical models, we say those systems are unfaithful to their networks. We formalize a variant of faithfulness for dynamic systems, called Granger-faithfulness, and for a large class of dynamic networks, we show that Granger-unfaithful systems constitute a Lebesgue zero-measure set. For the same class of networks, under the Granger-faithfulness assumption, we provide an algorithm that reconstructs the network topology with guarantees for no false positive and no false negative edges in its output. We augment the topology reconstruction algorithm with orientation rules for some of the inferred edges, and we prove the rules are consistent under the Granger-faithfulness assumption.
A Practical Proposal for State Estimation at Balanced, Radial Distribution Systems
Authors: Panayiotis Moutis, Omid Alizadeh-Mousavi
Date: 2020-12-03 00:00:00
Tasks: State Estimation

The ever-increasing deployment of distributed resources and the opportunities offered to loads for more active roles has changed the previously unidirectional and relatively straight-forward operating profile of distribution systems (DS). DS will be required to be monitored closely for robustness and sufficient power quality. State estimation of transmission systems has consistently served as a monitoring tool, which drives system-wide control actions and, thus, ensures the operational integrity of the electric grid. An update to the classic state estimation for the case of DS is offered in this work, based on a power flow formulation for radial networks that does not require measurements or estimate of the voltage angles.
Ultra-Wideband Radar and Wireless Human Sensing
Authors: Takuya Sakamoto
Date: 2020-12-03 00:00:00

This study presents the progress of our recent research regarding wireless measurements of the human body. First, we explain radar imaging algorithms for screening passengers at security checkpoints that can process data faster than existing algorithms to generate accurate and high-resolution images of human bodies. Second, we introduce signal processing techniques for accurately measuring radar micro-Doppler signals associated with human motions and activities. Finally, we present radar signal processing algorithms for measuring human respiration, heartbeat, and other types of motion. Such algorithms can be applied in various fields including healthcare, security, biometric identification, and man-machine interfaces. These fields have one thing in common: prior knowledge about the human shape, motion, and physiology are effectively exploited to improve performance by optimizing the radar systems and signal processing algorithms.
A 3D Non-Stationary Channel Model for 6G Wireless Systems Employing Intelligent Reflecting Surface
Authors: Yingzhuo Sun, Cheng-Xiang Wang, Jie Huang, Jun Wang
Date: 2020-12-03 00:00:00
Tasks: 3D geometry

As one of the key technologies for the sixth generation (6G) mobile communications, intelligent reflecting surface IRS) has the advantages of low power consumption, low cost, and simple design methods. But channel modeling is still an open issue in this field currently. In this paper, we propose a three-dimensional (3D) geometry based stochastic model (GBSM) for a massive multiple-input multiple-output (MIMO) communication system employing IRS. The model supports the movements of the transmitter, the receiver, and clusters. The evolution of clusters on the linear array and planar array is also considered in the proposed model. In addition, the generation of reflecting coefficient is incorporated into the model and the path loss of the sub-channel assisted by IRS is also proposed. The steering vector is set up at the base station for the cooperation with IRS. Through studying statistical properties such as the temporal autocorrelation function and space correlation function, the nonstationary properties are verified. The good agreement between the simulation results and the analytical results illustrates the correctness of the proposed channel model.
Robust Transmission Design for Intelligent Reflecting Surface Aided Secure Communication Systems with Imperfect Cascaded CSI
Authors: Sheng Hong, Cunhua Pan, Hong Ren, Kezhi Wang, Kok Keong Chai, Arumugam Nallanathan
Date: 2020-04-24 00:00:00

In this paper, we investigate the design of robust and secure transmission in intelligent reflecting surface (IRS) aided wireless communication systems. In particular, a multi-antenna access point (AP) communicates with a single-antenna legitimate receiver in the presence of multiple single-antenna eavesdroppers, where the artificial noise (AN) is transmitted to enhance the security performance. Besides, we assume that the cascaded AP-IRS-user channels are imperfect due to the channel estimation error. To minimize the transmit power, the beamforming vector at the transmitter, the AN covariance matrix, and the IRS phase shifts are jointly optimized subject to the outage rate probability constraints under the statistical cascaded channel state information (CSI) error model that usually models the channel estimation error. To handle the resulting non-convex optimization problem, we first approximate the outage rate probability constraints by using the Bernstein-type inequality. Then, we develop a suboptimal algorithm based on alternating optimization, the penalty-based and semidefinite relaxation methods. Simulation results reveal that the proposed scheme significantly reduces the transmit power compared to other benchmark schemes.
Millimeter-Wave Massive MIMO Testbed with Hybrid Beamforming
Authors: MinKeun Chung, Liang Liu, Andreas Johansson, Martin Nilsson, Olof Zander, Zhinong Ying, Fredrik Tufvesson, Ove Edfors
Date: 2020-12-02 00:00:00
Tasks: Scheduling

Massive multiple-input multiple-out (MIMO) technology is vital in millimeter-wave (mmWave) bands to obtain large array gains. However, there are practical challenges, such as high hardware cost and power consumption in such systems. A promising solution to these problems is to adopt a hybrid beamforming architecture. This architecture has a much lower number of transceiver (TRx) chains than the total antenna number, resulting in cost- and energy-efficient systems. In this paper, we present a real-time mmWave (28 GHz) massive MIMO testbed with hybrid beamforming. This testbed has a 64-antenna/16-TRx unit for beam-selection, which can be expanded to larger array sizes in a modular way. For testing everything from baseband processing algorithms to scheduling and beam-selection in real propagation environments, we extend the capability of an existing 100-antenna/100-TRx massive MIMO testbed (below 6 GHz), built upon software-defined radio technology, to a flexible mmWave massive MIMO system.
Distributed User Association in B5G Networks Using Early Acceptance Matching Games
Authors: Alireza Alizadeh, Mai Vu
Date: 2020-12-02 00:00:00

We study distributed user association in 5G and beyond millimeter-wave enabled heterogeneous networks using matching theory. We propose a novel and efficient distributed matching game, called early acceptance (EA), which allows users to apply for association with their ranked-preference base station in a distributed fashion and get accepted as soon as they are in the base station's preference list with available quota. Several variants of the EA matching game with preference list updating and reapplying are compared with the original and stability-optimal deferred acceptance (DA) matching game, which implements a waiting list at each base station and delays user association until the game finishes. We show that matching stability needs not lead to optimal performance in other metrics such as throughput. Analysis and simulations show that compared to DA, the proposed EA matching games achieve higher network throughput while exhibiting a significantly faster association process. Furthermore, the EA games either playing once or multiple times can reach closely the network utility of a centralized user association while having much lower complexity.