  This paper presents a data-aided channel estimator that reduces the channel
estimation error of the conventional linear minimum-mean-squared-error (LMMSE)
method for multiple-input multiple-output communication systems. The basic idea
is to selectively exploit detected symbol vectors obtained from data detection
as additional pilot signals. To optimize the selection of the detected symbol
vectors, a Markov decision process (MDP) is defined which finds the best
selection to minimize the mean-squared-error (MSE) of the channel estimate.
Then a reinforcement learning algorithm is developed to solve this MDP in a
computationally efficient manner. Simulation results demonstrate that the
presented channel estimator significantly reduces the MSE of the channel
estimate and therefore improves the block error rate of the system, compared to
the conventional LMMSE method.
