  Channel estimation problem is one of the key technical issues in time-variant
multiple-input single-output (MSIO) communication systems. To estimate the MISO
channel, least mean square (LMS) algorithm is applied to adaptive channel
estimation (ACE). Since the MISO channel is often described by sparse channel
model, such sparsity can be exploited and then estimation performance can be
improved by adaptive sparse channel estimation (ASCE) methods using sparse LMS
algorithms. However, conventional ASCE methods have two main drawbacks: 1)
sensitive to random scale of training signal and 2) unstable in low
signal-to-noise ratio (SNR) regime. To overcome these two harmful factors, in
this paper, we propose a novel ASCE method using normalized LMS (NLMS)
algorithm (ASCE-NLMS). In addition, we also proposed an improved ASCE method
using normalized least mean fourth (NLMF) algorithm (ASCE-NLMF). Two proposed
methods can exploit the channel sparsity effectively. Also, stability of the
proposed methods is confirmed by mathematical derivation. Computer simulation
results show that the proposed sparse channel estimation methods can achieve
better estimation performance than conventional methods.
