MIMO Signals Classification with Cauchy Noise in Space-air-ground Integrated Networks
Mingqian Liu, Yae Chai · 2023
An average likelihood ratio test (ALRT)-based methodology is proposed for automatic modulation classification (AMC) of orthogonal space-time block code (STBC) based multiple-input multiple-output (MIMO) systems. Most existing works for modulation classification of MIMO systems assume the addictive noise is Gaussian. However, the actual noise in practical systems often exhibits non-Gaussian characteristics. To address this, we propose a practical MIMO system model that assumes the additive noise is Cauchy. The proposed approach analyzes the ALRT functions corresponding to different modulation types and employs zero-forcing (ZF) equalization algorithm to reduce the computational complexity of the likelihood functions. Performance analysis is carried out for two scenarios: Alamouti-STBC systems (Al-STBC) with a 2 × 2 transmit and receive antenna configuration and space-time transmit diversity (STTD) with a 4×4. Simulation results indicate that the correct recognition rate can exceed 90% when the generalized signal-to-noise ratio (GSNR) is 10dB under the STTD model.