Spectrum sensing algorithms and software-defined radio implementation for cognitive radio system

The scarcity of spectral resources in wireless communications, due to a fixed frequency allocation policy, is a strong limitation to the increasing demand for higher data rates. However, measurements showed that a large part of frequency channels are underutilized or almost unoccupied. The cognitive radio paradigm arises as a tempting solution to the spectral congestion problem. A cognitive radio must be able to identify transmission opportunities in unused channels and to avoid generating harmful interference with the licensed primary users. Its key enabling technology is the spectrum sensing unit, whose ultimate goal consists in providing an indication whether a primary transmission is taking place in the observed channel. Such indication is determined as the result of a binary hypothesis testing experiment wherein null hypothesis (alternate hypothesis) corresponds to the absence (presence) of the primary signal. The first parts of this thesis describes the spectrum sensing problem and presents some of the best performing detection techniques. Energy Detection and multi-antenna Eigenvalue-Based Detection algorithms are considered. Important aspects are taken into account, like the impact of noise estimation or the effect of primary user traffic. The performance of each detector is assessed in terms of false alarm probability and detection probability. In most experimental research, cognitive radio techniques are deployed in software-defined radio systems, radio transceivers that allow operating parameters (like modulation type, bandwidth, output power, etc.) to be set or altered by software.In the second part of the thesis, we introduce the software-defined radio concept. Then, we focus on the implementation of Energy Detection and Eigenvalue-Based Detection algorithms: first, the used software platform, GNU Radio, is described, secondly, the implementation of a parallel energy detector and a multi-antenna eigenbased detector is illustrated and details on the used methodologies are given. Finally, we present the deployed experimental cognitive testbeds and the used radio peripherals. The obtained algorithmic results along with the software-defined radio implementation may offer a set of tools able to create a realistic cognitive radio system with real-time spectrum sensing capabilities

Hybrid approach analysis of energy detection and eigenvalue based spectrum sensing algorithms with noise power estimation

Two particular semi-blind spectrum sensing algorithms are taken into account in this paper: Energy Detection (ED) and Roy's Largest Root Test (RLRT). Both algorithms require the knowledge of the noise power in order to achieve optimal performance. Since by its nature the noise power is unpredictable, noise variance estimation is needed in order to cope with the absence of prior knowledge of the noise power: this leads to a new hybrid approach for both considered detectors. Probability of detection and false alarm with this new approach are derived in closed-form expressions. The impact of noise estimation accuracy for ED and RLRT is evaluated in terms of Receiver Operating Characteristic (ROC) curves and performance curves, i.e., detection/misdetection probability as a function of the Signal to Noise Ratio (SNR). Analytical results have been confirmed by numerical simulations under a flat-fading channel scenario. It is concluded that both hybrid approaches tend to their ideal cases when a large number of slots is used for noise variance estimation and that the impairment due to noise uncertainty is reduced on RLRT w.r.t. E

A Random Matrix Model for mmWave MIMO Systems

Random matrices are nowadays classical tools for modeling multiantenna wireless channels. Scattering phenomena typical of cellular frequencies and channel reciprocity features led to the adoption of matrices sampled either from the Gaussian Unitary Ensemble (GUE) or from more general Polynomial Ensembles (PE). Such matrices can be used to model the random impairments of the radio channel on the transmitted signal over a wireless link whose transmitter and receiver are both equipped with antenna arrays. The exploitation of the millimeter-wave (mmWave) frequency band, planned for 5G and beyond mobile networks, prevents the use of GUE and PE elements as candidate models for channel matrices. This is mainly due to the lack of scattering richness compared to microwave-based transmissions. In this work, we propose to model mmWave Multi-Input–MultiOutput (MIMO) systems via products of random Vandermonde matrices. We illustrate the physical motivation of our model selection, discuss the meaning of the parameters and their impact on the spectral properties of the random matrix at hand, and provide both a list of results of immediate use for performance analysis of mmWave MIMO systems, as well as a list of open problems in the field

Sensing of DVB-T signals for white space cognitive radio systems

In cognitive radio networks, systems operating in digital television white spaces are particularly interesting for practical applications. In this paper, we consider single- antenna and multi-antenna spectrum sensing of real DVB-T signals under different channel conditions. Some of the most important algorithms are considered and compared, including energy detection, eigenvalue based techniques and methods exploiting OFDM signal knowledge. The obtained results show the algorithm performance and hierarchy in terms of ROC and detection probability under fixed false alarm rate, for different channel profiles in case of true DVB-T signal

SNR Wall Analysis of Multi-Sensor Energy Detection with Noise Variance Estimation

In this paper, we extend the concept of SNR Wall phenomenon for Energy Detection (ED) to the multi-sensor ED by using a hybrid approach with auxiliary noise estimation under a typical flat fading channel scenario. SNR Wall expression is derived for multi-sensor ED and proved to be independent of the number of sensors. The distribution of the uncertainty of the noise variance estimate is derived for auxiliary Gaussian noise samples. The analytical expression of the uncertainty bound is derived. It is concluded that the noise uncertainty can be reduced by increasing the number of samples used for noise variance estimation, but the number of samples/slots used for noise estimation exponentially increases as the SNR Wall condition becomes more stringen

On the use of eigenvectors in multi-antenna spectrum sensing with noise variance estimation

In this paper, a thorough comparison of multi-antenna spectrum sensing techniques is performed. We considered well known algorithms, such as Energy Detector (ED), eigenvalue based detectors, and an algorithm that uses the eigenvector associated to the largest eigenvalue of the covariance matrix. With the idea of auxiliary noise variance estimation, a hybrid approach for the eigenvector-based method is presented and compared against the hybrid Roy's Largest Root Test and hybrid ED. Performance results are evaluated in terms of Receiver Operating Characteristic (ROC) curves and performance curves, i.e., detection probability as a function of the Signal to Noise Ratio (SNR). It is shown that the the eigenvector-based algorithm and its hybrid variant are able approach the optimal Neyman-Pearson performance

Performance Analysis of Multi-User MIMO Schemes under Realistic 3GPP 3-D Channel Model for 5G mmWave Cellular Networks

Novel techniques such as mmWave transmission and massive MIMO have proven to present many attractive features able to support high data demand for 5G NR technologies. Towards the standardization of 5G networks, channel modeling has become an important step in order to test the reliability of theoretical studies. In this paper, we study the performance of a 5G network at mmWave range for the downlink. We consider a single trisectorized base station equipped with planar arrays, and we model users as a spatial Poisson process in a hexagonal grid. We adopt the latest 3GPP channel model described in TR 38.901 and we provide a thorough description and step-by-step tutorial of it along with our customizations and MATLAB scripts for channel generation in the presented scenario. Moreover, we evaluate the performance of Multi-User Multi-Layer MIMO techniques, such as Signal-to-Leakage-plus-Noise Ratio (SLNR) precoding and MMSE combined with different system configurations by means of achievable per-user rate

Impact of noise estimation on eigenvalue based spectrum sensing in cognitive radio systems

In order to gain awareness of available transmission opportunities, a cognitive radio [1] system requires the implementation of a spectrum sensing unit, which must indicate whether a transmission is taking place in the considered channel. In order to provide such indication, a binary hypothesis testing experiment must be periodically performed [2]. Many algorithms have been proposed for the computation of such test statistics [3]. In this paper we focus on the performance of Hybrid Approach of semi-blind eigenvalue-based detection (EBD) algorithms, especially RLRT [4], in which the noise variance is estimated in a given number of auxiliary slot

Impact of noise estimation on energy detection and eigenvalue based spectrum sensing algorithms

In this paper, semi-blind class of spectrum sensing algorithms, Energy Detection (ED) and Roy's Largest Root Test (RLRT), are considered under a typical flat fading channel scenario. The knowledge of the noise variance is imperative for the optimum performance of ED and RLRT. Unfortunately, the variation and unpredictability of noise variance is unavoidable. An idea of auxiliary noise variance estimation is introduced in order to cope with the absence of prior knowledge of the noise variance, thus a hybrid approach of signal detection is set forth for each considered method. The detection performance of the methods are derived and expressed by closed form analytical expressions. The impact of noise estimation accuracy on the the performance of ED and RLRT is compared in terms of Receiver Operating Characteristic (ROC) curves and performance curves (Probability of Detection/Miss-detection as a function of SNR by fixing the false alarm probability). It is concluded that optimum performance of ED and RLRT can be achieved even with the use of estimated noise variance by using a large number of slots for variance estimation. Finally, it is also found out that the impairment due to noise uncertainty is reduced on RLRT w. r. t. ED

Evaluation of MU-MIMO Digital Beamforming Algorithms in B5G/6G LEO Satellite Systems

Satellite Communication (SatCom) systems will be a key component of 5G and 6G networks to achieve the goal of providing unlimited and ubiquitous communications and deploying smart and sustainable networks. To meet the ever-increasing demand for higher throughput in 5G and beyond, aggressive frequency reuse schemes (i.e., full frequency reuse), combined with digital beamforming techniques to cope with the massive co-channel interference, are recognized as a key solution. Aimed at (i) eliminating the joint optimization problem among the beamforming vectors of all users, (ii) splitting it into distinct ones, and (iii) finding a closed-form solution, we propose a beamforming algorithm based on maximizing the users’ Signal- to-Leakage-and-Noise Ratio (SLNR) served by a Low Earth Orbit (LEO) satellite. We investigate and assess the performance of several beamforming algorithms, including both those based on Channel State Information (CSI) at the transmitter, i.e., Minimum Mean Square Error (MMSE) and Zero-Forcing (ZF), and those only requiring the users’ locations, i.e., Switchable Multi-Beam (MB). Through a detailed numerical analysis, we provide a thorough comparison of the performance in terms of per-user achievable spectral efficiency of the aforementioned beamforming schemes, and we show that the proposed SLNR beamforming technique is able to outperform both MMSE and ZF schemes in the presented SatCom scenario