An improved proportionate normalized least-mean-square algorithm for broadband multipath channel estimation.
To make use of the sparsity property of broadband multipath wireless communication channels, we mathematically propose an l p -norm-constrained proportionate normalized least-mean-square (LP-PNLMS) sparse channel estimation algorithm. A general l p -norm is weighted by the gain matrix and is incorporated into the cost function of the proportionate normalized least-mean-square (PNLMS) algorithm. This integration is equivalent to adding a zero attractor to the iterations, by which the convergence speed and steady-state performance of the inactive taps are significantly improved. Our simulation results demonstrate that the proposed algorithm can effectively improve the estimation performance of the PNLMS-based algorithm for sparse channel estimation applications.
Graduate School of Engineering, Kochi University of Technology, Kami-shi 782-8502, Japan.
Yingsong
0000-0002-2450-6028
Y
Li
Graduate School of Engineering, Kochi University of Technology, Kami-shi 782-8502, Japan.
Masanori
0000-0001-6193-2037
M
Hamamura
D000465:Algorithms; D008962:Models, Theoretical; D059015:Wireless Technology