P. T. Sivagurunathan ¹, P. Nithya ¹, N. Mahendran ¹, V. Kavitha ¹

Affiliations
1 Department of Electronics and communication engineering, M. Kumarasamy College of Engineering, Karur, Thalavapalayam – 639113, Tamil Nadu, IN

Abstract

Objectives: To reduce the PAPR in OFDM signal. Methods/Statistical Analysis: The foremost problem found in OFDM is the high PAPR (Peak to Average Power Ratio). The variety of processes worned to diminish the high PAPR is: Partial Transmit Sequence (PTS) Coding, Clipping, m-Law, Selective Mapping (SLM), Exponential Companding (EC) etc. Findings: However, these PAPR reduction techniques have some disadvantages, such as Higher Modulation Order, Degradation of the BER and SNR performance, increasing of Average Power etc. A new Nonlinear Companding technique is proposed which aims at compressing the large amplitude signals and at the same time sustaining the normal power as stable. The Simulation results show that the proposed Companding method possibly provides an improved scheme recital in stipulations of PAPR lessening, and BER recital. Application/Improvements: Amplifier will not get damage.

Keywords

Exponential Companding (EC), PAPR (Peak to Average Power Ratio), Partial Transmit Sequence (PTS), Selective Mapping (SLM)

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N. Mahendran ¹, T. Mekala ²

Affiliations
1 Department of Electronics and Communication Engineering, M. Kumarasamy College of Engineering, Thalavapalayam − 639113, Karur, Tamil Nadu, IN
2 Department of Computer Science and Engineering, M. Kumarasamy College of Engineering, Thalavapalayam − 639113, Karur, Tamil Nadu, IN

Abstract

Objective: To minimize the energy frenzied around cloud datacenters. The proposed system provides a dynamic and continuous load prediction along with existing heuristic load prediction to improve the adaptation performance for green computing through Markov chaining of resources. Methodology: The trouble of energy competence through resource management for a large-scale cloud environment to facilitate hosts sites is resolute here. In apply there will be number of underutilized servers in the datacenter which guzzle bulky part of established energy. To minimize the energy frenzied around datacenters the existing system outlines a distributed middleware architecture and presenting one of its key elements, a gossip protocol, Skewness algorithm and load balancing that meets our design goals: justice of resource allocation with respect to hosted sites through overload escaping and efficient edition to load changes and scalability in terms of together the numeral of machines and sites thereby turning off the unused servers for green computing through Markov Chaining Model. This model is scalable enough to signify systems composed of thousands of resources and it makes potential to represent both physical and virtual wealth exploiting cloud explicit concepts such as the infrastructure elasticity. Results: To certify the model, simulation is conducted within the Network Simulator (NS) 2.28 have platform with GCC 4.3 and Fedora 13. The load prediction algorithm and Markov chain model achieves both overload avoidance and green computing for systems with multiresource limitations. Application: Green Wireless Network: To improve the energy efficient, data retrieval and resource service based on coaching, computing and networking in the area of Green Wireless. Green Big Data: To reduce the power consumption, big data merge the concept with green computing. Green job Scheduling: Each Server has different jobs, to save the energy server merged with green computing. Green Cloud data center: To use the green computing in cloud data center to increases the energy efficiency.

Keywords

Green Computing, Load Prediction, Markov Chaining, Virtualization

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