A Study On Compressed Sensing Based Estimation Of Multipath Channels

Wireless signal propagated in the wireless channel will be affected by multi-path time delay spread, Doppler shift, angular spread and other characteristics of multi-path fading, leading to the received signal distortion in amplitude, phase and frequency. So it needs to estimate the state of the channel to compensate the received signal. And the inherent feature of sparsity in physical wireless channel makes it possible to apply compressed sensing (CS) theory on the estimation of sparse multipath. As a new sampling theory, CS theory breaks the framework of the Nyquist sampling theory, transforming the signal sampling into the information sampling. So to ensure no loss of information, it can get sample signal at a rate much less than Nyquist sampling and reconstruct the signal with high probability under certain conditions. Simulation results show that compressed sensing-based channel estimation methods need much less training sequences under the same estimation performance, improving utilization of spectrum resources, reducing greatly the reconstruction error.In Chapter 4, it analyses the sparsity of the multi-path channel in angle-delay-Doppler spread domain, establishes the model of the single-antenna frequency-selective CDMA channel and applies CS theory on the channel estimation. Furthermore the concept of optimized measurement matrix is used, by reducing the correlation between column vectors of the measurement matrix to form an optimized measurement matrix, it can lead a further improved performance in sparse channel estimation.In Chapter 5, it considers the estimation of time-selective channels in amplify-and-forward two-way relay network (AF-TWRN), analyses the sparsity of the channel in Doppler domain and establishes the corresponding sparse channel model. In the simulations, it analyses the influences of pilot number,SNR,maximum Doppler frequency shift on the channel estimation performance respectively, and draws a conclusion that different pilot pattern will affect the correlation of measurement matrix, which can furtherly affect the channel estimation.In Chapter 6, it uses the adaptive Bayesian compressed sensing (A-BCS) based on maximum a posteriori estimation (MAP) principle to do the compressed channel estimation, which combines the signal reconstruction with the measurement matrix design, making these two steps no longer independent. At the meantime, a new scheme of joint optimized measurement matrix and A-BCS is proposed, by reducing the correlation and the adaptive design of measurement matrix to get a better reconstruction performance both on anti-noise ability and recovery accuracy.