Limits on Sparse Data Acquisition: RIC Analysis of Finite Gaussian Matrices

One of the key issues in the acquisition of sparse data by means of compressed sensing (CS) is the design of the measurement matrix. Gaussian matrices have been proven to be information-theoretically optimal in terms of minimizing the required number of measurements for sparse recovery. In this paper we provide a new approach for the analysis of the restricted isometry constant (RIC) of finite dimensional Gaussian measurement matrices. The proposed method relies on the exact distributions of the extreme eigenvalues for Wishart matrices. First, we derive the probability that the restricted isometry property is satisfied for a given sufficient recovery condition on the RIC, and propose a probabilistic framework to study both the symmetric and asymmetric RICs. Then, we analyze the recovery of compressible signals in noise through the statistical characterization of stability and robustness. The presented framework determines limits on various sparse recovery algorithms for finite size problems. In particular, it provides a tight lower bound on the maximum sparsity order of the acquired data allowing signal recovery with a given target probability. Also, we derive simple approximations for the RICs based on the Tracy-Widom distribution.Comment: 11 pages, 6 figures, accepted for publication in IEEE transactions on information theor

Single-Snapshot Localization for Near-Field Ris Model using Atomic Norm Minimization

Reconfigurable intelligent surfaces (RISs) are expected to play a significant role in the next generation of wireless cellular technology. This paper proposes an uplink localization scheme using a single-snapshot solution for user equipment (UE) that is located in the near-field of the RIS. We propose utilizing the atomic norm minimization method to achieve super-resolution localization accuracy. We formulate an optimization problem to estimate the UE location parameters (i.e., angles and distances) by minimizing the atomic norm. Then, we propose to exploit strong duality to solve the atomic norm problem using the dual problem and semidefinite programming (SDP). The RIS is controlled and designed using estimated parameters to enhance the beamforming capabilities. Finally, we compare the localization performance of the proposed atomic norm minimization with compressed sensing (CS) in terms of the localization error. The numerical results show a superior performance of the proposed atomic norm method over the CS where a sub-cm level of accuracy can be achieved under some of the system configuration conditions using the proposed atomic norm method

Syndrome-Based Encoding of Compressible Sources for M2M Communication

Data originating from many devices and sensors can be modeled as sparse signals. Hence, efficient compression techniques of such data are essential to reduce bandwidth and transmission power, especially for energy constrained devices within machine to machine communication scenarios. This paper provides accurate analysis of the operational distortion-rate function (ODR) for syndrome-based source encoders of noisy sparse sources. We derive the probability density function of error due to both quantization and pre- quantization noise for a type of mixed distributed source comprising Bernoulli and an arbitrary continuous distribution, e.g., Bernoulli- uniform sources. Then, we derive the ODR for two encoding schemes based on the syndromes of Reed-Solomon (RS) and Bose, Chaudhuri, and Hocquenghem (BCH) codes. The presented analysis allows designing a quantizer such that a target average distortion is achieved. As confirmed by numerical results, the closed-form expression for ODR perfectly coincides with the simulation. Also, the performance loss compared to an entropy based encoder is tolerable

Enhancing Near-Field Wireless Localization with LiDAR-Assisted RIS in Multipath Environments

In Next-Generation Wireless Networks that Adopt Millimeter-Waves and Large RIS, the User is Expected to Be in the Near-Field Region, Where the Widely Adopted Far-Field Algorithms based on Far-Field Can Yield Low Positioning Accuracy. Also, the Localization of UE Becomes More Challenging in Multipath Environments. in This Paper, We Propose a Localization Algorithm for a UE in the Near-Field of a RIS in Multipath Environments. the Proposed Scheme Utilizes a LiDAR to Assist the UE Positioning by Providing Geometric Information About Some of the Scatterers in the Environment. This Information is Fed to a Sparse Recovery Algorithm to Improve the Localization Accuracy of the UE by Reducing the Number of Variables (I.e., Angle of Arrivals and Distances) to Be Estimated. the Numerical Results Show that the Proposed Scheme Can Improve the Localization Accuracy by 65% Compared to the Standard CS Scheme

LoRa Backscatter Communications: Temporal, Spectral, and Error Performance Analysis

LoRa backscatter (LB) communication systems can be considered as a potential candidate for ultra low power wide area networks (LPWAN) because of their low cost and low power consumption. In this paper, we comprehensively analyze LB modulation from various aspects, i.e., temporal, spectral, and error performance characteristics. First, we propose a signal model for LB signals that accounts for the limited number of loads in the tag. Then, we investigate the spectral properties of LB signals, obtaining a closed-form expression for the power spectrum. Finally, we derived the symbol error rate (SER) of LB with two decoders, i.e., the maximum likelihood (ML) and fast Fourier transform (FFT) decoders, in both additive white Gaussian noise (AWGN) and double Nakagami-m fading channels. The spectral analysis shows that out-of-band emissions for LB satisfy the European Telecommunications Standards Institute (ETSI) regulation only when considering a relatively large number of loads. For the error performance, unlike conventional LoRa, the FFT decoder is not optimal. Nevertheless, the ML decoder can achieve a performance similar to conventional LoRa with a moderate number of loads.Comment: Early access in IEEE Journal of Internet of Things. Codes are provided in Github: https://github.com/SlinGovie/LoRa-Backscatter-Performance-Analysi

Sparse Signal Processing and Statistical Inference for Internet of Things

Data originating from many devices within the Internet of Things (IoT) framework can be modeled as sparse signals. Efficient compression techniques of such data are essential to reduce the memory storage, bandwidth, and transmission power. In this thesis, I develop some theory and propose practical schemes for IoT applications to exploit the signal sparsity for efficient data acquisition and compression under the frameworks of compressed sensing (CS) and transform coding. In the context of CS, the restricted isometry constant of finite Gaussian measurement matrices is investigated, based on the exact distributions of the extreme eigenvalues of Wishart matrices. The analysis determines how aggressively the signal can be sub-sampled and recovered from a small number of linear measurements. The signal reconstruction is guaranteed, with a predefined probability, via various recovery algorithms. Moreover, the measurement matrix design for simultaneously acquiring multiple signals is considered. This problem is important for IoT networks, where a huge number of nodes are involved. In this scenario, the presented analytical methods provide limits on the compression of joint sparse sources by analyzing the weak restricted isometry constant of Gaussian measurement matrices. Regarding transform coding, two efficient source encoders for noisy sparse sources are proposed, based on channel coding theory. The analytical performance is derived in terms of the operational rate-distortion and energy-distortion. Furthermore, a case study for the compression of real signals from a wireless sensor network using the proposed encoders is considered. These techniques can reduce the power consumption and increase the lifetime of IoT networks. Finally, a frame synchronization mechanism has been designed to achieve reliable radio links for IoT devices, where optimal and suboptimal metrics for noncoherent frame synchronization are derived. The proposed tests outperform the commonly used correlation detector, leading to accurate data extraction and reduced power consumption

Dominance of Smartphone Exposure in 5G Mobile Networks

The deployment of 5G networks is sometimes questioned due to the impact of ElectroMagnetic Field (EMF) generated by Radio Base Station (RBS) on users. The goal of this work is to analyze such issue from a novel perspective, by comparing RBS EMF against exposure generated by 5G smartphones in commercial deployments. The measurement of exposure from 5G is hampered by several implementation aspects, such as dual connectivity between 4G and 5G, spectrum fragmentation, and carrier aggregation. To face such issues, we deploy a novel framework, called 5G-EA, tailored to the assessment of smartphone and RBS exposure through an innovative measurement algorithm, able to remotely control a programmable spectrum analyzer. Results, obtained in both outdoor and indoor locations, reveal that smartphone exposure (upon generation of uplink traffic) dominates over the RBS one. Moreover, Line-of-Sight locations experience a reduction of around one order of magnitude on the overall exposure compared to Non-Line-of-Sight ones. In addition, 5G exposure always represents a small share (up to 28%) compared to 4G EMF