Mohamed Hamid

On Spectrum Sharing and Dynamic Spectrum Allocation: MAC Layer Spectrum Sensing in Cognitive Radio Networks

One of the most critical issues regarding wireless networks regulation agencies is how to manage the available electromagnetic radio spectrum in a way that satisfies the needs of the huge growing in wireless systems both economically and technically, especially with the recent crowding in the available spectrum. Hence, building cognitive radio systems supporting dynamic access to the available spectrum has appeared recently as a novel solution for the wireless system huge expansion. In this paper we investigate the MAC layer sensing schemes in cognitive radio networks, where both reactive and proactive sensing are considered. In proactive sensing the adapted and non-adapted sensing periods schemes are also assessed. The assessment of these sensing schemes has been held via two performance metrics: available spectrum utilization and idle channel search delay. Simulation results show that with proactive sensing adapted periods we achieve the best performance but with an observable overhead computational tasks to be done by the network nodes.

Blind Spectrum Sensing for Cognitive Radios Using Discriminant Analysis: A Novel Approach

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.

Energy and Eigenvalue Based Combined Fully Blind Self Adapted Spectrum Sensing Algorithm

In this paper, a comparison between energy and maximum-minimum eigenvalue (MME) detectors is performed. The comparison has been made concerning the sensing complexity and the sensing accuracy in terms of the receiver operating characteristic (ROC) curves. The impact of the signal bandwidth compared with the observation bandwidth is studied for each detector. For the energy detector, the probability of detection increases monotonically with the increase in the signal bandwidth. For the MME detector, an optimal value of the ratio between the signal bandwidth and the observation bandwidth is found to be 0.5 when reasonable values of the system dimensionality are used. Based on the comparison findings, a combined two-stage detector is proposed. The combined detector performance is evaluated based on simulations and measurements. The combined detector achieves better sensing accuracy than the two individual detectors with complexity that lies in between the two individual complexities. The combined detector is fully blind and self-adapted as the MME detector estimates the noise and feeds it back to the energy detector. The performance of the noise estimation process is evaluated in terms of the normalized mean square error (NMSE).

Spectrum sensing through spectrum discriminator and maximum minimum eigenvalue detector: A comparative study

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.

Maximum minimum eigenvalues based spectrum scanner for cognitive radios

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.

Sample covariance matrix eigenvalues based blind SNR estimation

In this paper, a newly developed SNR estimation algorithm is presented. The new algorithm is based on the eigenvalues of the sample covariance matrix of the recieved signal. The presented algorithm is blind in the sense that both the noise and the signal power are unknown and estimated from the received samples. The Minimum Descriptive Length (MDL) criterion is used to split the signal and noise corresponding eigenvalues. The experimental results are judged using the Normalized Mean Square Error (NMSE) between the estimated and the actual SNRs. The results show that, depending on the value of the received vectors size and the number of received vectors, the NMSE is changed and down to -55 dB NMSE can be achieved for the highest used values of the system dimensionality.

Saving lives by integrating cognitive radios into ambulances

A brain stroke is defined as a disturbance in the blood supply of the brain. This can be due to either an obstruction in the blood vessels of the brain or a rupture in the blood vessels which causes a leakage of blood in the brain. In many cases, a stroke results in the death of the patient within 24 hours. Hence, it is crucial that the neurologist has immediately contact with the patient in the first 30 minutes after the stroke. This means that a direct broadband communication link between the ambulance and the hospital is needed in order to transmit all necessary physiological parameters, such as blood pressure and glucose level as well as video images. In this paper, we present a new architecture of a wireless communication link between the ambulance and the hospital based on the concept of cognitive radios. The sender/receiver module in the ambulance will allow measuring the wideband spectrum and search for a suitable empty frequency band to send the data.

A novel approach for energy detector sensing time and periodic sensing interval optimization in cognitive radios

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 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.

Signal Bandwidth Impact on Maximum-Minimum Eigenvalue Detection

The impact of the signal bandwidth and observation bandwidth on the detection performance of the maximum-minimum eigenvalue detector is studied in this letter. The considered signals are the Gaussian signals. The optimum ratio between the signal and the observation bandwidth is analytically proven to be 0.5 when reasonable values of the system dimensionality are used. The analytical proof is verified by simulations.

Non-parametric spectrum cartography using adaptive radial basis functions

This paper presents a framework for spectrum cartography based on the use of adaptive Gaussian radial basis functions (RBF) centered around a specific number of centroid locations, which are determined, jointly with the other RBF parameters, by the available measurement values at given sensor locations in a specific geographical area. The spectrum map is constructed non-parametrically as no prior knowledge about the transmitters is assumed. The received signal power at each location (over a given bandwidth and time period) is estimated as a weighted contribution from different RBF, in such a way that the both RBF parameters and the weights are jointly optimized using an alternating minimization method with a least squares loss function and a quadratic regularization term. Our method is evaluated through simulations, showing a performance (in terms of normalized MSE) that is comparable to semi-parametric methods, and even superior as the number of sensors or RBF increases.