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Next Generation Wireless Communication Systems : Cognitive Radio Adnan Quadri & Dr. Naima Kaabouch, Department of Electrical Engineering Goal and Objectives Results This research aims to develop techniques that will enhance cognitive radio systems’ channel selection ability. o With the utility based frequency selection technique, a cognitive radio system is able to select signals of better quality and most availability, as shown below. Introduction o Wireless networks and traffic have grown exponentially over the last decade, which led to an exponential increase in demand for frequency channels. o Current fixed frequency allocation results in inefficient utilization of the radio spectrum. • Licensed frequency channels are not used all the time • Unlicensed bands, such as Wi-Fi, are heavily used Goal Objectives • Develop accurate spectrum sensing methods. • Investigate the use of utility models to evaluate the quality of sensed frequency channels. Methodology o Existing spectrum sensing techniques measure the energy of sensed signals to determine the availability of channels [3]. o In this work, we have added an important parameter, Signal-toNoise Ratio (SNR) to determine the quality of available frequency channels Fig 1. Inefficient utilization of radio spectrum [1] o Developed a technique to calculate the ‘usefulness’, utility, of each frequency channel Fig 5. Results for the technique using utility based frequency selection o This utility will allow cognitive radio systems to rank the quality of channels in terms of availability, noise, and quality of service. Background o Utility Function is defined as – o Cognitive Radio technology is a promising solution to address the inefficient utilization of spectrum. o Cognitive Radio systems will be smart, able to sense and configure their transmission parameters according to the environment [2]. 𝑈𝑥𝑦 = [𝑤𝑥 𝑈𝑥 𝑟 𝑟 1/𝑟 + 𝑤𝑦 𝑈𝑦 ] Where, 𝑈𝑥𝑦 is the combined utility of the channel’s SNR and availability, 𝑤𝑥 and 𝑤𝑦 are weights, 𝑈𝑥 and 𝑈𝑦 are the individual utility values for SNR and signal availability, and 𝑟= 𝑠−1 , 𝑠 Fig 6. Results for the technique without utility based frequency selection where 𝑠 is the elasticity of substitution. Conclusions & Future Work o An improved technique to enhance cognitive radio’s frequency selection capability was developed. o Results show that the utility based technique enhances cognitive radio’s frequency selection criteria. Brain Empowered Wireless communications o Real experiments will be performed to evaluate the performance of the proposed technique. - Professor Simon Haykin Fig 2. Cognitive Radio: Intelligent Radio Systems References [1] Pictures taken from Nokia Research on Cognitive Radio http://mynokiablog.com/2010/06/17/video-nokia-research-centerpresents-cognitive-radio/ [2] A. Quadri, M. Riahi Manesh, & N. Kaabouch, “Denoising Signals in Cognitive Radio Systems Using An Evolutionary Algorithm Based Adaptive Filter”, IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference, pp. 1–7, 2016. Fig 3. Efficient utilization of scarce radio spectrum using Cognitive Radio [1] Fig 4. Steps involved in selecting best signal using the developed technique [3] M. Riahi Manesh, A. Quadri,, S. Subramaniam. & N. Kaabouch, “An Optimized SNR Estimation Technique Using Particle Swarm Optimization Algorithm”, IEEE Annual Computing and Communication Workshop and Conference, pp. 1–7, 2017. Acknowledgement: The project ‘Radio Spectrum Decision Enhancement’ is funded by the National Science Foundation (NSF) For details, please visit the website for Signal & Image Processing Lab - http://engineering.und.edu/radio-spectrum-decision-enhancement/index