TRAINING SIGNAL DESIGN FOR CHANNEL ESTIMATION IN MASSIVE MIMO SYSTEMS

In this paper, the design of training signals for channel estimation in massive multiple-input multiple-output (MIMO) systems is considered. Under a stationary, block Gauss-Markov channel model, a method for optimal pilot beam pattern design for enhanced channel estimation is proposed, exploiting both the properties of Kalman filtering and the spatio-temporal channel correlation. First, pilot beam pattern design is considered under the assumption of orthogonal beam patterns within a block. The orthogonality assumption is subsequently relaxed and the design problem is solved via a greedy approach. Numerical results show the efficacy of the proposed algorithm.