Research On Ultra-wideband Channel Estimation Based On Compressive Sensing

Compared with the conventional communication system, the Ultra-wideband system has lower power consumption, higher transmission speed and higher security characteristics. Due to these distinguishing features, the Ultra-wideband system becomes a hot topic in the field of short distance indoor communication. But in the UWB system, the original signal will have serious distortion when transmitted through the uncertain wireless channel environment. So in order to reconstruct the original signal as more accurately as possible, the channel estimation technology becomes an important research hotspot. At the receiving end of the system, ultra-wideband signal requires a very high sampling rate to complete the analog to digital conversion, which brings challenge to the current hardware craft and channel estimation technology. Different from the traditional Nyquist sampling theorem, the emerging compressed sensing theory put forward a new opportunity to overcome the sampling bottleneck in UWB system. Since UWB signals and UWB channel are sparse, it is meaningful and feasible to discuss the UWB system based on CS theory.This thesis aims at solving the problem of UWB channel estimation based on CS theory. On the one hand, in the conventional method, orthogonal matching pursuit reconstruction algorithm estimates the channel parameters which include magnitude and time delay by matching the strongest atom from the observed signal in the iterative process. However, in the presence of noise pollution, the reconstruction algorithm will erroneously select the false atom which has weak power, and this will lead to lower the overall system performance of the reconstruction. On the other hand, in the traditional ultra-wideband analog channel estimation scheme, when observing the received signal, the process amplifies the adverse effects of channel noise, beyond that, the complexity of the receiver is also really high. Based on the above issues, in order to suppress the adverse effects of noise on the channel estimation result and reduce the complexity of UWB systems, the improvements on the design of dictionary and measurement matrix are discussed in this thesis and the details can be seen as following:(1)In the Chapter three, in order to avoid using false atom which is polluted by noise to reconstruct the original signal, the thesis here put an weighted factor on the sparse dictionary. What is known from the average power delay profile of the channel impulse response is that the received power of different multipath decay exponentially with time increases. The thesis put the profile curve as the weighting factor in multipath dictionary, which means that the earlier arrived atom correspond to bigger weighting factor and the pulse energy varies in accordance with the exponential decay law of power delay profile. Small delay has high energy, and a more accurate channel estimation performance can be seen in the improved scheme.(2)In the Chapter four, in order to prevent the channel noise polluting the UWB signal, the thesis introduces a random digital pre-processing module. The thesis uses a pre-processing matrix which satisfies Gaussian distribution at the transmitter to code the non-zero elements into a linear combination one. The combination of the UWB channel and the pre-processing filter can be viewed as an important part of the measurement process, and then the signal can be directly sampled with a low rate analog-to-digital converter. At the end of the chapter, the thesis combines weighted dictionary which is described in Chapter three and pre-processing scheme to deal with the channel estimation problem. The simulation results show us that while considering the adverse noise effect, the proposed model can obtain a significant improvement with lower root mean square error and better reconstruction performance.