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

A Techno-Economic study for heating poultry houses using renewable energy

In any broilers poultry house, fuel-based heating systems are commonly used to maintain the targeted temperatures for successful breeding of chicken. A considerable amount of fuel is consumed for this application, which leads to high running cost and contributes to the increase of air pollutant emissions. Given the current energy crisis and the urge to use renewable energy, this research studies the application of a solar heating system (SHS) for a poultry house. It includes the technical and economic study for the SHS and the integration of biogas produced from chicken manure as an auxiliary source of heat. The heating demand of a broilers poultry house of capacity 24000 birds located in Al Menia governorate in Egypt is calculated hourly over a complete year using TRNSYS simulation tool.Accordingly, a SHS is designed to cover part of this demand besides a fuel based auxiliary source. The system consists of: evacuated tubes, water storage tanks and fan coil units. The two main design variables of the SHS are the area of the solar collector (ASC) and the volume of the storage tanks (Vtank).An economical study of the SHS is carried out, where the net present value (NPV) is calculated. A solution space consisting of 65 different designs is explored, where the NPV is calculated at each solution to select the best economical design within the solution space. The calculation is performed twice, once using the Egyptian local fuel price, where the SHS is found to be economically feasible using certain designs only.The other calculation is performed using the international minimum benchmark price of diesel fuel, where the NPV of all designs is found to be significantly higher and thus, the use of SHS is more appealing at this fuel price.Other parameters such as the infiltration rate of the building and the usage of latent heat storage technique are examined to explore their effect on the performance of the SHS. Finally,an all-green heating solution is introduced, where bio-digesters are used to produce biogas from the waste of the poultry house in order to complement the SHS by covering the auxiliary energy needed. The economics of the all-green solution is examined and found to be bettered

On the LoRa Modulation for IoT: Waveform Properties and Spectral Analysis

An important modulation technique for Internet of Things (IoT) is the one proposed by the low power long range (LoRa) alliance. In this paper, we analyze the M -ary LoRa modulation in the time and frequency domains. First, we provide the signal description in the time domain, and show that LoRa is a memoryless continuous phase modulation. The cross-correlation between the transmitted waveforms is determined, proving that LoRa can be considered approximately an orthogonal modulation only for large M. Then, we investigate the spectral characteristics of the signal modulated by random data, obtaining a closed-form expression of the spectrum in terms of Fresnel functions. Quite surprisingly, we found that LoRa has both continuous and discrete spectra, with the discrete spectrum containing exactly a fraction 1/M of the total signal power

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