In this paper we introduce a technique for spectrum scanning with the maximum minimum eigenvalue detection based spectrum sensing. The fundamental problem we address in this paper is the inability of using maximum minimum eigenvalue detection with filtering in time domain where the white noise becomes coloured. The solution we propose here is based on frequency domain rectangular filtering. By frequency domain rectangular filtering we take the spectral lines inside each sub-band and throw out the rest. After doing the frequency domain rectangular filtering, we generate the corresponding time domain signal and inject it to the maximum minimum eigenvalue detector. An experimental verification has been performed and the obtained results show that the technique is implementable with a performance better than the energy detector as a reference technique in terms of the probability of detection when both technique have the same probability of false alarm.

In this paper we present a new spectrum sensing technique for cognitive radios based on discriminant analysis called spectrum discriminator and compare it with the maximum minimum eigenvalue detector. The common feature between those two techniques is that neither prior knowledge about the system noise level nor the primary user signal, that might occupy the band under sensing, is required. Instead the system noise level will be derived from the received signal. The main difference between both techniques is that the spectrum discriminator is a non-parametric technique while the maximum minimum eigenvalue detector is a parametric technique. The comparative study between both has been done based on two performance metrics: the probability of false alarm and the probability of detection. For the spectrum discriminator an accuracy factor called noise uncertainty is defined as the level over which the noise energy may vary. Simulations are performed for different values of noise uncertainty for the spectrum discriminator and different values for the number of received samples and smoothing factor for the maximum minimum eigenvalue detector.

In this paper a new approach of optimizing the sensing time and periodic sensing interval for energy detectors has been explored. This new approach is built upon maximizing the probability of right detection, captured opportunities and transmission efficiency. The probability of right detection is defined as the probability of having no false alarm and correct detection. Optimization of the sensing time relies on maximizing the summation of the probability of right detection and the transmission efficiency while optimization of periodic sensing interval subjects to maximizing the summation of transmission efficiency and the captured opportunities. The optimum sensing time and periodic sensing interval are dependent on each other, hence, iterative approach to optimize them is applied and convergence criterion is defined. The simulations show that both converged sensing time and periodic sensing interval increase with the increase of the channel utilization factor, moreover, the probability of false alarm, the probability of detection, the probability of right detection, the transmission efficiency and the captured opportunities have been taken as the detector performance metrics and evaluated for different values of channel utilization factor and signal-to-noise ratio.

One of the most critical issues regarding wireless networksregulation agencies is how to manage the available electromagneticradio spectrum in a way that satisfies the needs of the huge growingin wireless systems both economically and technically, especiallywith the recent crowding in the available spectrum. Hence, buildingcognitive radio systems supporting dynamic access to the availablespectrum has appeared recently as a novel solution for the wirelesssystem huge expansion. In this paper we investigate the MAC layersensing schemes in cognitive radio networks, where both reactiveand proactive sensing are considered. In proactive sensing theadapted and non-adapted sensing periods schemes are also assessed.The assessment of these sensing schemes has been held via twoperformance metrics: available spectrum utilization and idlechannel search delay. Simulation results show that with proactivesensing adapted periods we achieve the best performance but withan observable overhead computational tasks to be done by thenetwork nodes

In this paper a model to investigate the spectrum opportunities for cognitive radio networks in three radar frequency bands L, S and C at a specific location is introduced. We consider underlay unaware spectrum sharing model. The Secondary System we assume is an OFDM based system. The followed strategy is built upon defining a specific co or adjacent channel as a spectrum opportunity if -and only if- the interference generated by the secondary system occupying that channel  into the radar system is less than the permissible interference defined by the value of Interference to Noise ratio (INR) and the radar receiver inherited noise level. The simulation results show that for the same transmission parameters C band offer more spectrum opportunities than S band which is itself offers more spectrum opportunities than L band.

In this paper, we present a new spectrum sensing technique for cognitive radios based on discriminant analysis called spectrum discriminator. The presented technique uses the knowledge of the noise uncertainty and a probabilistic validation to overcome the limitations of the discriminant analysis. A comparative study between the proposed technique and the maximum-minimum eigenvalue detection has been performed based on two performance metrics: the probability of false alarm and the probability of detection. The spectrum discriminator has been further developed to a peel-off technique where all primary users can be detected. The performance of the spectrum discriminator and the peel-off technique has been tested on simulations and experimentally verified. The comparative study is based on simulations as well as measurements.