The issue of sparsity adaptivechannel reconstruction in time-varying cooperative communication networks through the amplify-and-forward transmission scheme is studied. A new sparsity adaptive system identification me...
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The issue of sparsity adaptivechannel reconstruction in time-varying cooperative communication networks through the amplify-and-forward transmission scheme is studied. A new sparsity adaptive system identification method is proposed, namely reweighted lp norm (0<p<1) penalised least mean square (lms) algorithm. The main idea of the algorithm is to add a lp norm penalty of sparsity into the cost function of the lms algorithm. By doing so, the weight factor becomes a balance parameter of the associated lp norm adaptivesparse system identification. Subsequently, the steady state of the coefficient misalignment vector is derived theoretically, with a performance upper bounds provided which serve as a sufficient condition for the lmschannelestimation of the precise reweighted lp norm. With the upper bounds, the authors prove that the lp (0<p<1) norm sparsity inducing cost function is superior to the reweighted l1 norm. An optimal selection of p for the lp norm problem is studied to recover various d sparsechannel vectors. Several experiments verify that the simulation results agree well with the theoretical analysis, and thus demonstrate that the proposed algorithm has a better convergence speed and better steady-state behaviour than other lms algorithms.
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