Covariance Absolute Value Research Articles

A Covariance Matrix-Based Cooperative Spectrum Sensing Algorithm in Electric Wireless Private Network

Cognitive radios can significantly improve the spectral efficiency of wireless communications. Spectrum sensing is a key function of cognitive radios to prevent the harmful interference with licensed users. This paper considers the cooperative spectrum sensing in 230 MHz electric wireless private networks. First, the statistical distribution of the sample covariance matrix of the received signals is investigated. Then, taking the ratio of the sum of absolute values of the off-diagonal elements of the sample covariance matrix to that of the diagonal elements as the decision metric, an improved covariance absolute value (ICAV) cooperative sensing algorithm is proposed. After that, a performance analysis concerning the probabilities of false alarm and detection is carried out. Theoretical analysis and simulation results show that the ICAV algorithm possesses an accurate decision threshold and is robust to the noise uncertainty.

Threshold optimisation with Bayesian approach in covariance absolute value detection method

ABSTRACT This study investigate statistical properties of Bayes cost, it proposes new group-based detection models for Cognitive Radios (CR) systems using this statistic. In commonly used spectrum sensing applications, multi-user or multi-antenna detection scenario with different optimisation methods are recommended to increase the detection under high SNRs. In this study, the optimum threshold for Covariance Absolute Value detection methods is determined using the Bayesian Cost. Thus, the detection scenario is given for the proposed method and the results are compared with the traditional Covariance Absolute Value method. Simulations based on random communication signals are presented to verify the methods.

Multistage spectrum sensing scheme with SNR estimation

Multistage detection has inspired a heated debate due to its capacity to take full advantage of each detector. Motivated by this, an investigation into multistage spectrum sensing is conducted and a two-stage spectrum detector is proposed based on energy detector and covariance absolute value (CAV) detector here. The two-stage spectrum detector periodically determines which detector is appropriate for current radio environment according to the comparison between estimated SNR and SNR threshold provided here. The total blindness of CAV detector and available noise variance by SNR estimation scheme results in the blindness of the proposed detector to the characteristics of observed signal and noise. Simultaneously, the proposed scheme balances the sensing complexity and detection accuracy and provides higher practicability in consequence. Simulations validate the performance of the proposed method.

Blind Spectrum Sensing Approaches for Interweaved Cognitive Radio System: A Tutorial and Short Course

Spectrum sensing is one of the essential tasks to have a cognitive radio system, which will allow an unlicensed user, called secondary user, to utilize the spectrum while the licensed user, called primary user, is not occupying it. The spectrum sensing approaches can be classified as blind and knowledge aided approaches. This tutorial summarizes blind spectrum sensing (BSS) approaches that require no prior knowledge of the licensed user’s signal characteristics, specifically for an interweave cognitive radio network model. The tutorial provides a thorough background, major implementations, and limitations of the BSS approaches, which are energy detector approach, maximum to minimum eigenvalue approach, maximum eigenvalue approach, covariance absolute value approach, and covariance Frobenius norm approach. Moreover, the tutorial compares these approaches based on performance metrics and complexity requirements. Furthermore, for a higher interference protection, the combination of two different spectrum sensing approaches, namely two-stage detection technique is presented and discussed. Besides, the tutorial discusses the challenges and possible future research directions. The fundamental objective of this tutorial is to provide insightful views and design aspects of BSS approach to researchers. For this purpose, the tutorial includes pseudo codes and simulation examples to illustrate more about the practical aspects of the above-mentioned approaches.

Performance of Hybrid Sensing Method in Environment with Noise Uncertainty

The paper presents a novel hybrid spectrum sensing method used in cognitive radio and presents a hybrid detector (HD) which improves the sensing performance. The proposed HD takes advantage of the energy detection (ED) principle and a method based on Covariance Absolute Value (CAV), as well as on Cyclic Autocorrelation Function (CAF). The paper shows the limitations of using ED, resulting from the uncertainty of spectral density of noise power estimation, known as the SNR wall. The paper describes a system model and presents simulation results for the OFDM signal of a WiMAX-based communications system. The simulation results refer to an ideal environment with well-known parameters, and to an environment with uncertain spectral density of noise power estimation.

The Influence of Noise Uncertainty and SNR Wall on the Performance of Hybrid Sensing Method

The paper discusses the hybrid sensing method and presents the hybrid detector (HD) which improves the sensing performance. The proposed HD takes advantage of the energy detection (ED) and a method based on the Covariance Absolute Value (CAV) or Cyclic Autocorrelation Function (CAF). The paper chara

Blind Signal Detection Using a Linear Antenna Array: An Experimental Approach

An experimental investigation is reported on the use of a linear antenna array to perform blind signal detection. A software-defined-radio (SDR)-based receiver system is used to capture signals received by an antenna array while ensuring time and frequency synchronization across the radio-frequency (RF) front ends. The antenna array is moved in circular and linear paths to cause variation in received signal strength and to cause variation in the incident angles of the incoming waves. Covariance-based detection (CBD) algorithms are applied to the received signals to perform blind signal detection. Multiple antennas serve to enhance the received signal correlation, thereby causing an improvement in the detection performance of the CBD algorithms. In the presence of noise calibration error (which is inevitable in a practical system), the maximum eigenvalue of the correlation matrix (MEC), Hadamard ratio test (HRT), and covariance absolute value (CAV) algorithms exhibit a significant improvement in detection performance as the number of antennas is increased, whereas the eigenvalue-based detection (EBD) algorithms show no improvement. This paper also investigates the effect of varying the antenna spacing on the received signal correlation and its subsequent effect on the detection performance. It is observed that the detection performance of the linear array is directly related to the mean signal cross-correlation achieved by the array. A significant improvement in detection performance was observed for an antenna spacing of 0.1λ; however, to avoid degradation of antenna efficiency at such close spacing, proper impedance matching must be performed.

Sensing Algorithm for Cognitive Radio Networks based on Random Data Matrix

Signal detection is a fundamental problem in Cognitive radio. In this paper a new statistical test is proposed based on random data matrix (RDM) for detecting the signals in noise, as opposed to the eigenvalue based tests. Among the many spectrum sensing methods, the RDM method detects the primary users without any prior information. The performance of the test is compared with energy detection (ED), covariance absolute value (CAV) and eigenvalue based algorithms through simulation analysis. This sensing algorithm can be used for very low SNR signal detection without requiring the knowledge of signal, channel and noise. Simulations are based on wireless microphone and identically and independently distributed (iid) signals.

Blind Detection for Primary User Based on the Sample Covariance Matrix in Cognitive Radio

In this letter, a new blind sensing method based on the Cholesky factorization of the sample covariance matrix is presented for cognitive radios (CR). The proposed method overcomes the noise uncertainty problem of the energy detector (ED) and can also perform well without information about the channel, the primary signal and the noise power. Most importantly, unlike other detectors based on the sample covariance matrix (including the covariance absolute value (CAV), the maximum-minimum eigenvalue (MME), and the maximum eigenvalue trace (MET) detectors), the decision threshold for the proposed detector can be determined using an exact non-asymptotic expression without any asymptotic assumptions on the sample size and the sample dimension. Numerical simulations show the superiority of the new detector to the CAV, MME and MET detectors.