Tarek Elderini & Dr. Naima Kaabouch Channel Quality Estimation Techniques for Cognitive Radio Networks Tarek Elderini & Dr. Naima Kaabouch Introduction Cognitive radio (CR) technology aims to address the problems due to the scarcity and underutilization of the radio spectrum. CR systems need to evaluate the quality of channels and re-adjust their operating parameters. Stages of the cognitive radio cycle are affected by uncertainty. Existing techniques do not handle uncertainty. Goal and Objectives This research aims to develop efficient channel quality estimation techniques in order to enhance the efficiency of the next generation communication systems. This goal will be achieved through the following objectives: Develop efficient channel quality estimation techniques. Implement these techniques using Software Defined Radio platforms. Test extensively the developed techniques. Compare the performances of the developed techniques with those of the existing ones. Bayesian Models References Examples of Results The following results show the probabilistic distribution of BER while using 3 modulation schemes under channel conditions. Bayesian model is a probabilistic graphical model that represents a set of random variables and their conditional dependencies. P(C=F) P(C=T) 0.5 Wet Grass Rain Sprinkler Example Cloudy C P(S=F) P(S=T) F 0.5 T 0.9 0.1 C P(R=F) P(R=T) F 0.8 0.2 T S R P(W=F) P(W=T) F F 1.0 0.0 T F 0.1 0.9 F T T T 0.01 0.99 BER Doppler Shift Modulation Schemes C/I Eb/N0 Channel Coding Data Rate Bayesian Network for BER Bayesian Network for SINR SINR Noise Power Interference Power Power of Transmitted Signal Propagation Loss` Proposed Methodology To estimate the quality of the channel, we will use: Bit Error Rate (BER). Signal to Interference plus Noise Ratio (SINR). Outage Probability. To deal with uncertainty, we will develop a Bayesian Network to estimate the metrics above along with the parameters affecting these metrics. Based on these parameters’ values, CR will be able to analyze the surrounding environment and make a better decision. The decrease in uncertainty is directly proportional to the increase of the probability distribution of the BER. This leads the CR to be more efficient through re-adjusting its parameters. Hence, a better channel quality can be achieved. Outage Probability Noise Power Interference Power Propagation Losses Threshold Power of Transmitted Signal Bayesian Network for Outage Probability [1]H. Reyes, S. Subramaniam and N. Kaabouch, "A Bayesian network model of the bit error rate for cognitive radio networks", 2015 IEEE 16th Annual Wireless and Microwave Technology Conference (WAMICON), 2015. [2]S. Subramaniam, H. Reyes and N. Kaabouch, "Spectrum occupancy measurement: An autocorrelation based scanning technique using USRP", 2015 IEEE 16th Annual Wireless and Microwave Technology Conference (WAMICON), 2015.