An energy efficient hybrid code combining technique for cluster based co operative wireless network

Abstract

Objective of this research work is to provide and energy efficient newlinecoding for cluster based cooperative wireless networks The modified newlinetechnique is used based on selective repeat ARQ and low density parity newlinecheck LDPC A technique of combining noisy packets to achieve errorfree newlineresults for all channels with low bit errors below 50 percent are known as newlinecode combining Code combining optimizes the code rate and minimizes the newlinedelay required to decode a given packet by allowing a receiver to combine the newlineminimum number of packets Code combining applications include spreadspectrum newlinesystems packet communication systems twoway links and newlinemultiple hop networks Diversity combining provides a large variety of newlineschemes for improved biterrorrate BER in packet communications Data newlineclustering plays an important role in many disciplines including data mining newlinemachine learning bioinformatics pattern recognition and other fields where newlinethere is a need to learn the inherent grouping structure of data in an newlineunsupervised manner There are many clustering approaches proposed in the newlineliterature with different quality and complexity tradeoffs The thesis work newlinecomprises a snapshot of trelliscoded modulation which is shown to be newlinepowerefficient and bandwidthefficient modulation technique for fading newlinechannels A large amount of coding gain on the order of 10 dB can easily be newlineobtained in this case newline newlineThe clustering algorithm is fully distributed each node transmits newlineonly one message during clustering operation and the algorithm terminates in newlineappropriate time without iterations The thesis evaluates Energy Efficient newlineHybrid Code Combining EEHCC technique through NS2 simulation and newlineuses a bounded region of 1000 x 1000 sqm in which the nodes are placed newlineusing a uniform distribution The two data structures are designed to find the newlinematching sub clusters between different clustering and to obtain the final set newlineof cooperative clusters through a merging process The Cooperative newlineClustering CC model is introduced that involves multiple clustering newlinetechniques newline newline newline