| 研究実績の概要 |
Adaptive sparse channel estimation techniques have been studied in different wireless communication systems. To reduce computational complexity and to achieve the robustness in different noise environment, in this project, our research results are summarized as follows:
1) Based on the D-scale channel model, we proposed two algorithms: sparse normalized least mean fourth (NLMF) algorithm and mixed least square/fourth (LMS/F) algorithm. The two proposed algorithms can take advantage of the channel sparsity in the D-scale channel. Computer simulation results have been provided to confirm the effectiveness of the proposed algorithms.
2) Based on the non-Gaussian noise model, we proposed a kinds of stable sparse sign least mean square (SLMS) algorithms to mitigate impulsive noise and to exploit channel sparsity. Representative simulations have been given to validate the proposed algorithms. In addition, regularization parameter selection for the proposed algorithm has been investigated in this project. The selected parameter can improve the estimation performance while accelerate the convergence speed for the proposed algorithms.
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