Study On Ultra-wideband Channel Estimation Algorithm Based On Compressed Sensing

Ultra-wideband(UWB) is a new wireless communication technology which attracts more attention in recent years. UWB signal carries information using nanosecond even subnanosecond narrow pulse instead of carrier and it has very high transmission rate. According to the strict limits of its power spectral density required by Federal Communications Commission, UWB can coexist with the existing wireless communication technology. UWB wireless communication starts its boom in the field of military and commercial with its unique advantages and UWB channel estimation is one of the keys. However analog-to-digital conversion(ADC) unit requires very high sampling rate, high computational complexity due to the very wide bandwidth of UWB signal. The design and hardware implementation of ADC unit are very difficult.The emergence of compressed sensing theory provides an opportunity to change current situation. Compressed sensing theory is a new sampling method, which breaks through the bottleneck of the Nyquist sampling theorem. It proves that if the signal is sparse or compressible in a transform domain, the original signal can be reconstructed by a small amount of sampling points after observation, which provides a solution for high resolution signal acquisition. Because the UWB channel is sparse, compressed sensing theory can be applied in channel estimation. Considering the sparseness of the UWB channel in time domain, the problem of channel estimation can be transformed into reconstruction of sparse vector in the theory of compressive sensing. The UWB channel estimation algorithm based on compressed sensing theory is researched, aiming at reducing sampling rate of the receiver ADC, improving performance of the channel estimation, and enhancing the reliability of communication.Compressed sensing theory is introduced firstly, including signal sparse representation, observation matrix, reconstruction algorithm. It focuses on researching and contrasting the different reconstruction algorithms. The basic principle of ultra-wideband wireless communication, IEEE802.15.3a model and channel estimation technology are also introduced, which provides the system model for the subsequent UWB channel estimation based on compressed sensing.Then, according to the characteristic that UWB channel is sparse, the model of channel estimation based on compressed sensing is constructed. And UWB channel estimation system based on compressed sensing is designed from the three core aspects of compressed sensing theory. Three kinds of UWB channel estimation algorithms based on MP, OMP and Co Sa MP are simulated respectively. The reconstructed channel impulse response is compared with the original channel response and the channel estimation performance of diffident reconstruction algorithms is also compared.On this basis, sparsity adaptive UWB channel estimation algorithm is proposed, aiming at solving the problem that channel estimation based on the theory of compressive sensing needs to predict sparsity of the channel. The ideas of adaptive and regularization are introduced based on compressive sampling matching pursuit(Co Sa MP) algorithm. The number of the selected atoms is controlled automatically in order to approach channel sparsity K gradually. The UWB channel is estimated accurately although the sparsity of the channel is not available. The proposed channel estimation algorithm is simulated, and the performance is compared with SAMP and Co Sa MP algorithm.