A new class of sparse channel estimation methods based on support vector machines.

摘要

In this dissertation sparse channel estimation is reformulated as support vector regression (SVR) in which the channel coeffcients are the Lagrange multipliers of the dual problem. By employing the Vapnik's epsilon-insensitivity loss function, the solution can be expanded in terms of a reduced number of Lagrange multipliers (i.e., the nonzero filter coeffcients) and then a sparse solution is found. Furthermore, methods to extend the SVR technique are investigated to derive an iterative algorithm for blind estimation of sparse single-input multiple-output (SIMO) channels. This method can be also used for non-sparse channels, in particular when the channel order has been highly overestimated. In this situation, the structural risk minimization (SRM) principle pushes the small leading and trailing terms of the impulse response to zero. Results show that the SVR approach outperforms other conventional techniques of channel estimation. The main drawback of this approach is the high computational cost of the resulting quadratic programming (QP) solution. To reduce the complexity, we propose a simple and fast iterative algorithm called the Adatron to solve the SVR problem iteratively. Simulation results demonstrate the performance of the method.