Hybrid GOMP–ROMP Algorithm for Sparse Channel Estimation in mmWave MIMO: Enhancing Convergence and Reducing Computational Complexity

Anjana Babu Sujatha and Vinoth Babu Kumaravelu ()
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Anjana Babu Sujatha: Department of Communication Engineering, School of Electronics Engineering, Vellore Institute of Technology, Vellore 632014, India
Vinoth Babu Kumaravelu: Department of Communication Engineering, School of Electronics Engineering, Vellore Institute of Technology, Vellore 632014, India

Future Internet, 2025, vol. 17, issue 11, 1-20

Abstract: This paper proposes an efficient sparse channel estimation method for millimeter wave (mmWave) hybrid multiple-input multiple-output (MIMO) systems. The performance of orthogonal matching pursuit (OMP) and its advanced variants—generalized OMP (GOMP), simultaneous OMP (SOMP), and regularized OMP (ROMP)—is evaluated based on normalized mean square error (NMSE) and computational complexity. A new hybrid GOMP–ROMP algorithm is proposed to achieve faster convergence and lower computational costs while maintaining the desired estimation accuracy. Simulation results reveal that the proposed algorithm reduces NMSE by 0.040823 compared to OMP and attains ROMP’s accuracy with significantly less complexity. For a MIMO system with 32 ×32 configuration, the method offers up to a fourfold reduction in computational complexity compared to OMP, ROMP, and SOMP. These findings highlight the potential of the hybrid algorithm for real-time mmWave massive MIMO applications in fifth-generation (5G) and sixth-generation (6G) systems, where high bandwidth and low latency are essential.

Keywords: channel estimation; millimeter wave (mmWave); orthogonal matching pursuit (OMP); generalized OMP (GOMP); hybrid GOMP-ROMP; regularized OMP (ROMP); simultaneous OMP (SOMP); sparse Bayesian learning (SBL) (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2025
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