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Super-Resolution mmWave Channel Estimation for Generalized Spatial Modulation Systems

We propose super-resolution multiple-input multiple-output channel estimators for generalized spatial modulation-based millimeter-wave systems. Utilizing the inherent spatial sparsity of millimeter-wave channels, channel estimation problem is formulated using atomic norm minimization that enhances sparsity in the continuous angles of arrival and departure. Both pilot-assisted and data-aided channel estimators are developed, with the former one formulated as a convex problem and the latter one as a nonconvex problem. To efficiently solve these formulated channel estimation problems, we develop nonconvex factorization-based conjugate gradient descent methods to restrict search space into low-rank matrices. Superior channel estimation performance of the proposed algorithms compared to the state-of-the-art compressed-sensing-based estimators is demonstrated by simulation results.