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Performance Comparison of RLS and LMS Algorithm based Cognitive Radio for Maximum Energy Detection in Smart Grid Network

Prakash Thapa, Hui Il Chang, Gye Choon Park, Jin Lee


Background/Objectives: Smart grid network has capability to communicate and control the different part of the electrical energy system such as generation, transmission, and distribution. With the advanced in computer and telecommunication netwrok, it is very easy to managed supply and demand of electricity by using smart grid networks.
Methods/Statistical analysis: Frequency allocation is one of the important factor for the proper control and monitoring of the smart grid system. Higher the frequency band larger access to the users. But rapidly growing online application and wireless user, the scarcity of allocated frequency is being increased due to which frequency shortage problem is occurred. To solve this frequency shortage problem in smart grid network we should use smart RLS and LMS adaptive beam forming algorithm based cognitive radio network.
Findings: By using MATLAB simulation, we are trying to simulate LMS and RLS algorithms in cognitive radio environments. When the number of users increases from 20 to 90 and SNR values from -15dB to -5dB, then unused frequencies of primary users are used by the secondary users. So, these algorithms based cognitive network increased the number of secondary user by using the concept of probability of energy detection. As a result large number of unlicensed (opportunistic) user can have access to the smart energy network system. Due to the faster convergence rate, computational complexity and maximum rate of probability of signal detection, RLS algorithm based cognitive radio is more applicable in smart grid communication and control systems as compared to the LMS based cognitive radio network systems.
Improvements/Applications: For the better improvements in monitoring and controlling part of smart grid system we must increase the number of node, gateway and the base station for the secondary users. So we can achieve highly efficient smart grid systems with large area coverage and greater access to the users. Which improves the reliability, efficiency and sustainability of the grid systems.


Smart Grid Networks (SGN), Cognitive Radio Networks, Cognitive Radio Cycle, LMS and RLS Algorithms

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