Shamsher Malik ¹, Suresh Kumar ²

Affiliations
1 M. D. University Rohtak, Rohtak – 124001, Haryana, IN
2 ECE Department, M. D. University Rohtak, Rohtak – 124001, Haryana, IN

Abstract

Objectives: The paper designs a technique to optimize the phase noise compensation in the multiple inputs multiple output orthogonal frequency division multiplexing by using the neural network. Methods/Statistical Analysis: The phase noise compensation in the orthogonal frequency division multiplexing consists of two parts, one is the inter-carrier interference and other is the common phase error. This paper represents a neural network and Jaya algorithm based technique for the estimation of phase noise. The neural network has been used to remove the common phase error while inter-carrier interference has been removed by the Jaya algorithm. Findings: The proposed technique has been compared with three states of art techniques on different block size by using bit error rate. The reduced bit error rate can be identified for each block size. The effectiveness of the technique can be recognized by analyzing the results. Application/Improvements: The proposed technique can be used to facilitate the high data rate with better QoS during transmission.

Keywords

Artificial Neural Network, Bit Error Rate, Back Propagation, OFDM, Phase Noise Compensation.

Full Text

   

Amita Yadav ¹, Suresh Kumar ¹

Affiliations
1 Department of Computer Science and Engineering, Maharaja Surajmal Institute of Technology (MSIT), C-4, Lal Sain Mandir Marg, Janak Puri – 110058, New Delhi, IN

Abstract

Objective: To achieve the maximum possible lifetime of a network by efficient clustering and reducing the energy expended by sensor nodes. Methods/Analysis: Wireless Sensor Networks are an important part in today’s network infrastructure. With a great deal of importance given to minimized sensors due to the boom in Internet of Things combined with the traditional uses in military, pollution monitoring and gathering other geospatial data, WSNs are playing an ever increasing role in the technology sphere. The confinements of WSN incorporate the constrained battery life of the sensor hubs and the trouble in supplanting them on the field. Hence the most important criteria to be expand the lifetime of the system by minimising battery utilisation through efficient clustering algorithms. Findings: In our research, we have focused on using the Particle Swarm Optimization (PSO) algorithm for efficient clustering. The proposed model simulated through MATLAB.

Keywords

Clustering, Energy Efficiency, Particle Swarm Optimization, Wireless Sensor Networks

Full Text