Off-Grid Compressive Channel Estimation for mm-Wave Massive MIMO With Hybrid Precoding

To reduce the pilot overhead and improve the channel estimation accuracy in the massive MIMO system, various channel estimation algorithms employing the sparse signal reconstruction (SSR) scheme have been proposed. However, the spatial grid division leads to the tradeoff between the estimation accuracy and the computational complexity. In addition, when the true angle is not on the discretized grid point which is referred as off-grid problem, the performance of SSR-based algorithms will degrade heavily. In this letter, a novel channel estimation algorithm which achieves superior performance under the off-grid scenario is proposed. At first, the conventional joint angle of arrivals/departures (AoAs/AoDs) estimation is transformed into two 1-D sub-problems. Then, the SSR-based framework is presented to obtain the initial sparse-support set. By minimizing the constructed objective function, the off-grid errors regarded as adjustable parameters are iteratively refined. In addition, scatter gains are acquired by LSE. Numerical simulations are provided to illustrate the superiority of the proposed algorithm in terms of estimation accuracy and computational complexity.