Research Of OFDM Pivotal Technology Based On Wavelet Theory

With the development and mature of the wireless communication technology , The Orthogonal Frequency Division Multiplexing technology has received people's widespread attention. In the OFDM system, high-speed data stream through bunch and merge transform, makes data symbol duration increase relatively in each sub-carrier wave, reduces inter-symbol interference from wireless channel time dispersion effectively, consequently it is possibility for sustaining future high speed wireless data operation. But tradition OFDM system toward to frequency excursion and phase yawp sensitivity, auto-adapted modulating technology increased complexity of system ,and OFDM system based on wavelet theory reduced the complexity of system and enhance the ability of anti- jamming.This paper studied several aspects from here in after:Firstly, through to wavelet filter bank and two channel filter bank's research, and proposes a scheme of orthogonal frequency division multiplexing (OFDM) system based on wavelet packet. And has discussed system's realizability. And analysed the performance of the different wavelet bases in WPT-OFDM system through the Matlab simulation, and made the compare with each other for four kinds of wavelets, through simulation we proved that the error bit rate for WPT-OFDM is less than that of traditional OFDM system.Secondly, through to wavelet lifting algorithm research , and proposes a scheme of orthogonal frequency division multiplexing (OFDM) system based on lifting wavelet. Focused on Jakes fading channel, we've studied the performance of the different wavelet bases in LWT-OFDM system. At last a principle to select the optimal wavelet bases in LWT-OFDM systems is proposed, and through simulation we proved that the error bit rate for LWT-OFDM is less than that of traditional OFDM system. Finally, for orthogonal frequency division multiplexing (OFDM) system, exploiting the sparsity of channel impulse response, and proposes a channel estimation algorithm based on approximately l₀ norm. The algorithm obtains the minimum l₀ norm with the approximate function optimal solution, thus obtains the sparsest channel response. Simulation results show: Under the same conditions, with the channel estimation algorithm based on l₀ norm to compare, this algorithm's convergence speed is fast, signal-to-noise ratio is high, and the mean error is small.