Johanna Vartiainen

On the Selection of the Best Detection Performance Sensors for Cognitive Radio Networks

In cooperative spectrum sensing, information from several cognitive radios (CRs) is used for detecting the primary user. To reduce sensing overhead and total energy consumption, it is recommended to cooperate only with the CRs that have the best detection performance. However, the problem is that it is not known a priori which of the CRs have the best detection performance. In this letter, we are proposing three methods for selecting the CRs with the best detection performance based only on hard (binary) local decisions from the CRs. Simulations are used to evaluate and compare the methods. The results indicate that the proposed CR selection methods are able to offer significant gains in terms of system performance.

Spectrum Sensingwith Forward Methods

New technologies will require effective spectrum use. Opportunistic spectrum usage that is one application of so called cognitive radio techniques enables the use of unused frequencies. One possible way to locate these free frequency bands is to use so called spectrum sensing. In this paper, energy detection based spectrum sensing methods called the forward consecutive mean excision (FCME) and forward cell averaging (CA) methods are studied in the situations where the noise power is unknown. The detection and false alarm probabilities of the studied methods are also of interest. Numerical results show that the investigated approaches have good performance

Double-threshold based narrowband signal extraction

A localization algorithm based on double-thresholding (LAD) is a computationally simple method for localizing narrowband signals in the frequency domain. The method does not need any a priori information about the narrowband signal. The localization is based on two thresholds. The lower threshold is used to compose adjacent signal samples into clusters whereas the upper threshold is used to detect signals. The LAD can be applied in narrowband signal detection as well as in interference suppression. The simulation results show that the LAD gives quite good localization accuracy and the LAD is able to determine correct number of narrowband signals even over 95% of the cases.

Spectrum Sensing with LAD-Based Methods

Opportunistic spectrum usage would enable enhancing the efficiency of existing and emerging wireless communication systems. One of the key issues related to those systems is spectrum opportunity estimation. In this paper, we are using a technique utilized earlier in narrowband signal detection, namely the localization algorithm based on double-thresholding (LAD), for sensing the existence of primary user signals in a cognitive radio systems. The LAD method requires no a priori information on the primary user statistics and it has a low computational complexity that will enable a low-cost real-time implementation. The LAD method is able to estimate the number of narrowband signals and their characteristics, including bandwidth and power. A simplified version of the LAD method which uses normalized thresholds (NT) as well as an enhancement of the scheme that uses adjacent cluster combining (ACC) are proposed. Simulation results show that the simplified version of the LAD method is useful in the considered situations, and the enhanced version of the LAD method improves the performance of the LAD and LAD NT methods significantly.

CFAR Outlier Detection With Forward Methods

Separation or classification of signal-present samples from noise-only samples is studied. The false-alarm probability implies how many noise-only samples are wrongly classified as outliers, and typically it should be smaller than some upper limit. The noise distribution parameters are not known a priori and have to be estimated. Multiple outliers have a strong influence to that estimation and may lead to uncontrollable false-alarm probability. The false-alarm probability control can be improved by robust estimators and/or by forward-detection methods. In this article, the false-alarm probability of the forward methods is analyzed. The forward consecutive mean excision (FCME) algorithm is enhanced to allow better false-alarm control. It is proposed that the forward method using the cell-averaging (CA) constant false-alarm rate (CFAR) technique can be applied for locating the outliers. The results show that its false-alarm probability stays close to the required value even in the presence of multiple outliers.

A Blind Signal Localization and SNR Estimation Method

Congested radio frequencies call for efficient and flexible spectrum use and, hence, spectrum sensing. As cognitive radios need channel information for achieving better use of radio frequencies, military systems require information about unknown signals. In this paper, a novel method for computing signal-to-noise ratios (SNR) of several unknown narrowband signals without a priori information is proposed. The presented frequency domain method is an extension of the localization algorithm based on double-thresholding (LAD). The main point is to produce fast and cost-efficient estimators with adequate accuracy. The proposed method is verified via computer simulations and tested also with real-life radio channel measurement data. The results verify that the proposed method gives sufficient approximations of SNR values with low overall computational complexity

Analysis of the LAD Methods

A localization algorithm based on double-thresholding (LAD) has been recently proposed for localizing concentrated signal(s) in the frequency or time domain. The LAD method blindly detects and separates concentrated signals and estimates their characteristics. This letter presents a theoretical performance analysis of the LAD method and an enhancement of the LAD method: the LAD with adjacent cluster combining (ACC). The good detection performance of the LAD ACC method is confirmed both theoretically and numerically.

Combination of short term and long term database for cognitive radio resource management

We propose a method that uses long term information on the use of primary channels to select the most auspicious ones to be sensed and exploited by cognitive radios at the requesting time. These channels are investigated in more detail over the short term. Sensing results are stored in the short term database and used to predict which channels are best for data transmission. The method makes the operation of cognitive radios more reliable and efficient in terms of delay and throughput, and decreases collisions with primary users.

Adaptive FCME-based threshold setting for energy detectors

The detection threshold setting and noise uncertainty are known to be critical aspects for energy detectors. Adaptive methods for threshold setting outperform non-adaptive methods due to flexibility and robustness. There exists several adaptive threshold setting methods of which the forward consecutive mean excision (FCME) algorithm is among the most attractive ones since it is blind, computationally simple and efficient. However, in some situations, it may give a too large threshold. We propose to apply median filtering with the FCME. Real-life real-time measurement results show that proposal enables more stable thresholds even in the situations when there are no signal-free reference samples for the initial threshold computing.

Measurement studies of a spectrum sensing algorithm based on double thresholding

Welch spectrum estimator may be used for spectrum monitoring and sensing. Conventionally, the detection threshold is set assuming that the noise power is estimated from noise-only samples. The forward consecutive mean excision (FCME) algorithm is a method for automatically making decisions based on the decision variables, without estimation of the noise power a priori. In this paper, we propose the utilization of the FCME algorithm with the Welch spectrum estimator. Additionally, theoretical analysis about asymptotic threshold setting for the FCME algorithm with the Welch spectrum estimator is presented. The localization algorithm based on double-thresholding (LAD) with adjacent cluster combining (ACC) utilizes the FCME algorithm and is very suitable for signal detection. Laboratory and field measurement results with the LAD ACC method are included to study the detectability of different signal types, including a real wireless microphone signal.