Research Of Wireless Channel Estimation Based On Compressed Sensing Theory

In wireless communication system,the wireless channel is a fading channel.At the receiving end,the received signal is demodulated and channel decoded,and the state information of the channel is needed at the receiving end.Therefore,the channel estimation technique is an important content of wireless communication research.The compressed sensing theory is a supplement to the Nyquist theorem,which can simultaneously implement the process of sampling and compression,and can restore the original signal with a small amount of sampled data.In the compressed sensing theory,if the signal is sparse,some algorithms can be used to reconstruct the original signal with high probability by a small amount of sampled data.Through the compressed sensing theory and the sparse characteristics of wireless channels,this thesis studies the channel estimation problem in Ultra-wide Band(UWB)system and MIMO-OFDM system based on compressed sensing theory.The content of this thesis is as follows:(1)The compressed sensing theory is studied.The research is carried out with three aspects: the sparse representation of the signal,the construction of the observation matrix and the reconstruction algorithm.Firstly,the sparse decomposition of the signal is studied.Secondly,the satisfying conditions and construction methods of the observation matrix are studied.Finally,the greedy iterative and convex optimization reconstruction algorithm are studied,and the reconstruction algorithm is simulated.(2)The UWB channel estimation technique based on the compressed sensing theory is studied.The UWB system is introduced and the sparsity of UWB multipath channel is analyzed.The compressed sensing theory is applied to UWB channel estimation.The IEEE 802.15.3a channel model of the UWB system is mainly studied.In the simulation experiment,the UWB system was simulated according to the IEEE802.15.3a channel model parameters.At the receiving end,the received signal is compressed and sampled,and the observed signal is obtained.The OMP,GOMP andCoSaMP reconstruction algorithms are used and simulation experiment is performed by the channel environment,the normalized mean square error and the number of observation data.The results show that the compressed sensing reconstruction algorithm can estimate UWB channel parameters well,by comparing with the LS algorithm.(3)The MIMO-OFDM channel estimation technique based on compressed sensing theory is studied.Firstly,the MIMO and OFDM techniques are introduced,and secondly,the channel model of the MIMO-OFDM system is constructed by MIMO and OFDM techniques.Finally,the compressed sensing theory is applied to the MIMO-OFDM channel estimation according to the sparse characteristics of the multipath channel of the MIMO-OFDM system.The MIMO-OFDM channel estimation based on training sequence and pilot signal is studied.In the simulation experiment,the channel model of the two transmit antennas and two receive antennas system is adopted,and the channel parameters are restructured by the OMP,GOMP and ROMP algorithms,and compared with the original channel parameters of MIMO-OFDM system.The results show that the compressed sensing reconstruction algorithm can restructure the channel parameters of MIMO-OFDM system well.