Aggregate Interference Power Modeling For Cognitive Radio Networks Using Bayesian Model Mohsen Riahi Manesh and Dr. Naima Kaabouch Electrical Engineering Department, University of North Dakota INTRODUCTION METHODOLOGY RESULTS Fig. 5 Probability distribution of the interference power for different frequency states Fig. 7 Probability distribution of the interference power for different frequency states while distance at state 2 Fig. 6 Probability distribution of the interference power for different frequency states while distance at state 2 and power at state 4 Fig. 8 Probability distribution of the interference power for different frequency states while distance at state 2, power at state 4 and shadowing at state 1 Cognitive radio (CR) technology aims to address the spectrum scarcity problem by intelligently managing the spectrum usage. In cognitive radio systems, unlicensed users are allowed to use the same channel as the licensed user without causing any interference to it. Interference is due to signals generated by other sources. Existing techniques to estimate the interference power do not consider all variables that affect interference power. In addition, they also do not deal with uncertainty. Moreover, they cannot dynamically add or remove parameters to the system. To address the previously mentioned problems, a Bayesian model is developed as shown in Fig. 1 and Fig. 2. Bayesian model expresses the beliefs about random variables along with their dependence relationships. Aggregate Interference Power Path loss Power of interfering nodes Distance Location Frequency Shadowing Fig. 2 Bayesian model for interference Doppler Spread Path Loss Shadowing/Fading AWGN SINR Received Signal Power Aggregate Interference Power Location Transmit Power of Interferers Transmit Power of Signal Frequency Distance Fig. 1 Bayesian model for signal to noise plus interference ratio (SINR) Start n = 0 Randomly select values for each combination of parent variables according to the intervals given Calculate and save the child variable Check the values of the child variable and match it with corresponding interval name n = n + 1 n ≤ 1000 Calculate the CPD by checking how many times an interval is happened (out of 1000 times) End No Yes CONCLUSIONS & FUTURE WORK Bayesian network is able to dynamically update itself as it gets more evidence. This enables the Bayesian network to handle uncertainty of the system. Future work includes developing a real-time model so that the impact of the past behavior of the network is considered in its present operation. Fig. 4 Process of obtaining a conditional probability distribution table GOAL & OBJECTIVES Bayesian Inference Engine CPD of All Variables of the Model Query Evidence Response To The Query Based On The Evidence Fig. 3 Inference engine The goal of this research project is to investigate the impact of the interference power and signal to interference plus ratio (SINR) on the detection performance of CR. To achieve this goal, the following objectives will be pursued: To analyze and identify all the parameters that affect the interference and SINR. To relate the parameters qualitatively and quantitatively using a probabilistic graphical model (PGM). To investigating the statistical behavior of interference and SINR. REFERENCES M. Riahi Manesh, A. Quadri & N. Kaabouch, “An Optimized SNR Estimation Technique Using Particle Swarm Optimization Algorithm”, IEEE Computing and Communication Workshop and Conference, pp. 1–7, 2017. M. Riahi Manesh, N. Kaabouch, H. Reyes, W-C. Hu, “A Bayesian approach to estimate and model SINR in wireless networks”, International Journal of Communication Systems, 2016, doi: 10.1002/dac.3187. M. Riahi Manesh, Md. S. Apu, N. Kaabouch, W-C. Hu, “Performance evaluation of spectrum sensing techniques for cognitive radio systems”, IEEE Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON), pp. 1-7, 2016. M. Riahi Manesh, N. Kaabouch, H. Reyes, W-C. Hu, “A Bayesian model of the aggregate interference power in cognitive radio networks”, IEEE Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON), pp. 1-7, 2016. A. Quadri, M. Riahi Manesh, N. Kaabouch, “Denoising signals in cognitive radio systems using an evolutionary algorithm based adaptive filter”, IEEE Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON), pp. 1-7, 2016.