A new framework for utilizing side information in sparse representation

An underdetermined system of linear equation has infinitely number answers. To find a specific solution, regularization method is used. For this propose, we define cost function based on desired features the solution and that answer with best matches to these selected as solution. In case sparse zero-norm function. many engineering cases, there side information which are omitted because Finding way conquer limitation, will help improve estimation parameter. regard, utilize maximum posterior (MAP) modify prior such both sparsity utilized. As consequence, framework into representation algorithms proposed. We also test our proposed in orthogonal frequency division multiplexing (OFDM) channel problem indicates, by utilizing system, performance improves fewer resources required for estimating channel.