Performance Comparison of SAGE and MUSIC for Channel Estimation in Direction-Scan Measurements

In this paper, the performances of a space-alternating generalized expectation-maximization (SAGE) algorithm and a MUltiple Signal Identification Classification (MUSIC) technique are compared when they are used to estimate the parameters of multipath components in a propagation channel from measurement data acquired by stepping a horn antenna in directions. The impacts of the horn antenna's half-power beamwidth (HPBW) and the stepping strategy on root mean squared estimation error and the intrinsic resolution of the estimators are investigated through simulations. The results demonstrate that the performances of both methods rely on the settings of the direction-scan strategy and the antenna HPBW. In general, the MUSIC algorithm, which owns a superior resolution, is less sensitive to the scan configuration and more suitable for scenarios of high signal-to-noise power ratio compared with the SAGE algorithm. Measurement data collected at 13-17 GHz is processed by using both algorithms. The results show that the MUSIC technique can be used to provide similar results with those of the SAGE algorithm at a time consumption significantly reduced.