Non-Gaussian Signal Detection: How much can massive MIMO help?

The radio frequency spectrum is occupied with authorized and unauthorized user activities which might include noise and interference. Detection of signals-of-interest (SOI) and differentiation from non-signals-of-interest (NSOI) are therefore crucial for frequency use management. There is a wide variety of signals in a desired radio spectrum band, which leads to the application of Signal Intelligence (SIGINT) to detect and identify signals in real-time. In this paper, we study the problem of non-Gaussian signal detection when the receivers are configured with a large number of antennas (or the massive antenna regime). First, we investigate the performance of signal detection with massive MIMO when the transmitted signals are generated from a Gaussian distribution. For the detection of Gaussian signals, we consider the Neyman-Pearson (NP) detector. Then, we focus on the performance of non-Gaussian signal detection with massive MIMO, which is one of the main objectives of this paper. We show that the NP detector gives poor performance for non-Gaussian signals in low signal-to-noise-ratio (SNR). Therefore, we propose to use a bispectrum detector, which contains the Gaussian noise and reveals the non-Gaussian information that exists in the signal. We present the theoretical analysis for asymptotic behavior of Probability of False Alarm (P-FA) and Probability of Detection (P-D) when the transmitter sends Gaussian and non-Gaussian signals. We show the performance of signal detection (for both Gaussian and non-Gaussian signals) as a function of the number of antennas and sampling rate. We also obtain the scaling behavior of the performance in the massive antenna regime.