Enhancement of Channel Estimation in OFDM Systems Using Functional Link Neural Fuzzy Network

• Main Authors: Yao-Hung Huang, 黄燿宏
• Other Authors: Chia-Hsin Cheng
• Format: Others
• Language: zh-TW
• Published: 2014
• Online Access: http://ndltd.ncl.edu.tw/handle/n4bug5

碩士 === 國立虎尾科技大學 === 電機工程研究所 === 102 === In this thesis, a new application of Functional Link Neural Fuzzy Network (FLNFN) to enhance performance channel estimation in OFDM systems is investigated. In wireless communications, it is necessary to estimate the channel to overcome the impairments caused by fading channels, including delay spread, multipath effect and Doppler shift. To eliminate these, the receiver needs to get the channel impulse response (CIR) of radio channel. In this thesis, we exploit traditional channel estimations, such as Least Square (LS), Minimum Mean Square Error (MMSE). Back Propagation Neural Network (BPNN) and Genetic Algorithm Based Back Propagation Neural Network (GABPNN) algorithms . Finally, FLNFN is also proposed for channel estimation in OFDM systems. Compared to LS, MMSE, BPNN and GABPNN algorithms, simulation results indicate that the proposed schemes can improve the system performance and approach the performance of MMSE algorithm.