  Accurate channel state information (CSI) is required for coherent detection
in time-variant multiple-input multipleoutput (MIMO) communication systems
using orthogonal frequency division multiplexing (OFDM) modulation. One of
low-complexity and stable adaptive channel estimation (ACE) approaches is the
normalized least mean square (NLMS)-based ACE. However, it cannot exploit the
inherent sparsity of MIMO channel which is characterized by a few dominant
channel taps. In this paper, we propose two adaptive sparse channel estimation
(ASCE) methods to take advantage of such sparse structure information for
time-variant MIMO-OFDM systems. Unlike traditional NLMS-based method, two
proposed methods are implemented by introducing sparse penalties to the cost
function of NLMS algorithm. Computer simulations confirm obvious performance
advantages of the proposed ASCEs over the traditional ACE.
