  This work presents a new variation of the commonly used Least Mean Squares
Algorithm (LMS) for the identification of sparse signals with an a-priori known
sparsity using a hard threshold operator in every iteration. It examines some
useful properties of the algorithm and compares it with the traditional LMS and
other sparsity aware variations of the same algorithm. It goes on to examine
the application of the algorithm in the problem of spectrum estimation for
cognitive radio devices.
