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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