Channel estimation and performance analysis for amplify and forward wireless relay networks

Abstract

The significant requirements of wireless networks are high spectral efficiency link reliability and extended coverage Multiple Input Multiple Output technology meets all these requirements as it combats fading and interference As the size constraint of the mobile terminals imposes a restriction on the number of antenna to be placed on them the concept of relays is introduced in wireless networks for extended coverage and link newlinereliability The principle of Wireless Relay Network comprises of two phases namely phase I and phase II In phase I the source node transmits an information signal to relay node through a broadcast channel The relay node receives the signal and applies Amplify and Forward relaying strategy which uses limited power and spectral resources to improve the coverage In phase II the processed signal is forwarded towards the destination node This thesis mathematically portrays performance of WRN employing AF relaying strategy by considering the channel estimation as a significant issue and newlineergodic capacity and Bit Error Rate as prime performance metrics In AFWRN Channel State Information is estimated at the destination node Many algorithms have been reported for channel estimation in AFWRN But most of the methods are based on Least Squares and Minimum Mean Square Error which reduce the mean square error to a significant extent but does not result in minimal variance Further the complexity of MMSE estimator increases exponentially as the number of samples grow In this thesis a Best Linear Unbiased Estimator based channel estimation algorithm is proposed for AFWRN Analytical expression for the MSE of the proposed algorithm and Cramer Rao Lower Bound are derived The simulation and theoretical results show better MSE newlineperformance than LS and MMSE estimators Further the proposed algorithm is also extended to AFWRN which employs multiple antennas at source relay and destination nodes It is proven by theoretical MSE derivation and simulation that the proposed algorithm shows better MSE performance than the traditional LS and MMSE estimators newline newline