OFDM Simulation And Channel Estimation

Today, communication technology is undergoing rapid changes, and has madetremendous contributions to the development of human society. However, therequirements of communication quality are also increasing. Therefore, tocontinuously improve the performance of communication systems has been a problemfaced by many researchers. In this thesis, the OFDM communication system has beenselected, and the radio channels, the basic principle and channel estimation algorithmtechnology of the OFDM has been In-depth study.MIMO-OFDM is a key technology for next-generation cellular communications(3GPP-LTE, Mobile WiMAX, IMT-Advanced) as well as wireless LAN (IEEE802.11a, IEEE802.11n), wireless PAN (MB-OFDM), and broadcasting (DAB, DVB,DMB). This book provides a comprehensive introduction to the basic theory andpractice of wireless channel modeling, OFDM, and MIMO, with MATLAB_programsto simulate the underlying techniques on MIMO-OFDM systems. This book isprimarily designed for engineers and researchers who are interested in learningvarious MIMO-OFDM techniques and applying them to wireless communications. Itcan also be used as a textbook for graduate courses or senior-level undergraduatecourses on advanced digital communications. The readers are assumed to have a basicknowledge on digital communications, digital signal processing, communicationtheory, signals and systems, as well as probability and random processes.This paper presents the fundamental concepts and MATLAB programs for simulationof wireless channel modeling techniques, including large-scale fading, small-scalefading, indoor and outdoor channel modeling, SISO channel modeling, and MIMOchannel modeling. And then presents the fundamental concepts and MATLABprograms for simulation of OFDM transmission techniques including OFDM basics.Finally, the article according to the different arrangement of the pilot, the pilot structure introduced classification. And describes the training symbols can beobtained when the channel estimation is widely used two techniques: LS and MMSEtechniques. Also studied to improve LS and MMSE channel estimation performanceof DFT-based channel estimation techniques. And DFT techniques with and withoutchannel estimation performance simulation comparison. Verified DFT-based channelestimation techniques superiority. The traditional channel estimator is fairly improvedand proposed. Only by introducing a decision threshold into the length of CP, theinfluence of the noise could be further removed by threshold processing. Simulationresults show that the improved algorithm is superior to the original algorithm, andthus is of practical application value.