Channel Estimation for Millimeter Wave Massive MIMO System: Proposed Hybrid Optimization with Heuristic-Enabled Precoding and Combining

Abstract

Abstract In multiple-input multiple-output (MIMO), millimeter wave (mmWave) is considered as a promising technology for advanced communication over wireless networks due to its rich frequency spectral resources. However, recognizing the mmWave in MIMO remains a complex task that faces the issues like increased propagation loss. Therefore, this paper proposes a new optimization-assisted estimation algorithm to estimate the mmWave channel parameters. The channel estimation and hybrid precoding performance on mmWave massive MIMO system are proposed by adopting optimization process in the codebook design principles. In fact, the existing works have performed uniform distribution of azimuth angles in the codebook design, whereas the proposed work evaluates it as a single objective optimization problem without excluding the angle characteristics. In order to solve the mentioned optimization problem, dragonfly-evaluated gray wolf optimization (DA-GWO) model is introduced that hybridizes the concepts of dragonfly algorithm and GWO, respectively. Finally, the performance of proposed work is compared and validated over other state-of-the-art models with respect to channel state information and error measures. Accordingly, from the analysis, the proposed DA-GWO model concerning (64, 64) combination for 400th channel bandwidth is 80% and 95.53% superior to adaptive channel estimation and projected gradient factorization algorithms.