| Summary: | 博士 === 國立交通大學 === 電信工程研究所 === 103 === Sparse Bayesian learning (SBL) is a widely used compressive sensing (CS) method that finds the solution by Bayesian inference. In this approach, a basis function is specified to form the transform matrix. For a particular application, it may exist a proper basis, with known model function and unknown parameters, which can convert the signal to a sparse domain. In conventional SBL, the parameters of the basis are assumed to be known as priori. This assumption may not be valid in real-world applications, and the efficacy of conventional SBL
approaches can be greatly affected. In this dissertation, we propose a basis-adaptive-sparse-Bayesian-learning (BA-SBL) framework, which can estimate the basis and system parameters, alternatively and iteratively, to solve the problem. Possible applications are also explored.
We start the work with the cooperative spectrum sensing problem in cognitive radio (CR) systems. It is known that in addition to spectrum sparsity, spatial sparsity can also be used to further enhance spectral utilization. To achieve that, secondary users (SUs) must know the locations and signal-strength distributions of primary-users’ base-stations (PUBSs), which is referred to as radio source positioning and power-propagation-map (PPM) reconstruction. Conventional approaches approximate PUBSs’ power decay with a path-loss model (PLM) and
assume PUBSs’ locations on some grid points. However, the parameters of the PLM have to be known in advance and the estimation accuracy is bounded by the resolution of the grid points.
We first employ a Laplacian function to model the PUBS power decay profile and propose a BA-SBL scheme to estimate corresponding parameters. With the proposed method, little priori information is required. To further enhance the performance, we incorporate source number
detection methods such that the number of the PUBSs can be precisely detected. Simulations show that the proposed algorithm has satisfactory performance even when the spatial measurement rate is low. While the proposed BA-SBL scheme can effectively reconstruct the PPM in
CR systems, it can only be applied in one frequency band at a time, and the frequency-band dependence is not considered. To fill the gap, we then extend the Laplacian function to the multiple-band scenario. For a multi-band Laplacian function, its correlation between different
bands is taken into consideration by a block SBL (BSBL) method. The BA-SBL is then modified and extended to a basis-adaptive BSBL (BA-BSBL) scheme, simultaneously reconstructing the PPMs of multiple frequency bands. Simulations show that BA-BSBL outperforms BA-SBL
applied to each band, independently.
Finally, we apply the proposed BA-BSBL procedure to the positioning problem in the 3rdgeneration-partnership-project (3GPP) long-term-evolution (LTE) systems. The observed-timedifference-of-arrival (OTDOA) method is used to estimate the location of user-element (UE).
It uses the estimated time-of-arrivals (TOAs) from three different base stations (BSs) as the observations. The TOA corresponding to a BS can be obtained by the first-tap delay of the time-domain channel response. The main problem of conventional OTDOA methods is that
the precision of TOA estimation, obtained by a channel estimation method, is limited by the quantization effect of the receiver’s sampler. Since wireless channels are generally spare, we can then formulate the time-domain channel estimation as a CS problem. Using the pulseshaping-filter response as the basis, we apply the proposed BA-BSBL procedure to conduct the
channel estimation, and the TOA can be estimated without quantization. Simulations show that the proposed BA-BSBL algorithm can significantly enhance the precision of TOA estimation and then improve the positioning performance.
|
|---|
