A Spatial-Diversity MIMO Dataset for RF Signal Processing Research

Abstract

The procedure of classifying a detected signal’s modulation scheme with no a priori information is known as automatic modulation classification (AMC). AMC has presented numerous contributions toward civilian and military applications. However, in the scientific literature, designing automatic modulation classifiers has been limited to a single particular simulated environment in most cases of study. Hence, the performance of such classifiers lacks: 1) reliability in real-world environments and 2) multivariate environment analysis for AMC operation in dissimilar surroundings. These two reasons represent a significant obstacle to the real-world implementation of such classifiers. Therefore, in this research, we present our contribution to remove these obstacles by generating an emulated signal reference dataset named MIMOSigRef-SD. It includes a wide variety of signal streams modulated by different digital modulation schemes, such as M-QAM, MIL-STD-188-110 B/C standard-specific QAM, M-PSK, M-APSK, DVB-S2/S2X/SH standard-specific APSK, and M-PAM with different modulation orders, each with different multiple-input multiple-output (MIMO) system configurations in order to provide an extensive signal reference. Each signal is also exposed to the realistic impact of different channel models at 2450 MHz, specifically Vehicular-A/B, Pedestrian-A/B, and the Butler, through a channel emulator. This signal reference includes the randomly generated transmit signals, characteristics of the emulated environments, and the recorded received symbols. This enables the dataset to be highly applicable to applications beyond AMC as well. In general, this signal reference can help analyze, evaluate, and design any other radio frequency transceiver tasks under realistic environmental effects. We also validate the elements of the signal reference by comparing the emulation and simulation bit error rate of the designed communication system.