1. 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: /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.