Identification of SM-OFDM and AL-OFDM Signals Based on Their Second-Order Cyclostationarity

Automatic signal identification (ASI) has important applications to both commercial and military communications, such as software defined radio, cognitive radio, spectrum surveillance and monitoring, and electronic warfare. While ASI has been intensively studied for single-input single-output systems, only a few investigations have been recently presented for multiple-input multiple-output systems. This paper introduces a novel algorithm for the identification of spatial multiplexing (SM) and Alamouti coded (AL) orthogonal frequency division multiplexing (OFDM) signals, which relies on the second-order signal cyclostationarity. Analytical expressions for the second-order cyclic statistics of SM-OFDM and AL-OFDM signals are derived and further exploited for the algorithm development. The proposed algorithm provides a good identification performance with low sensitivity to impairments in the received signal, such as phase noise, timing offset, and channel conditions.Comment: 36 pages, 14 figures, TVT201

Data-Driven Modulation and Antenna Classification of Wireless Digital Communication Signals

In this paper we are interested to learn from a wireless digitally modulated signal the number of antennas that the transmitter (Tx) of this signal uses, as well as its specific modulation scheme (from phase-shift keying (PSK) or quadrature amplitude modulation (QAM)). Formally, these are modulation and antenna classification problems. We examine the problems with data-driven machine learning (ML)-based techniques. The two sub-problems of modulation and number of transmitter antenna classification are initially examined independently for a variety for system parameters, namely the SNR, number of receiver (Rx) antennas, and classification algorithms. Then we consider the joint problem where we follow two approaches. One, where the sub-problems are solved independently and in parallel, and one where the antenna classifier waits on the result of the modulation classifier. The two proposed schemes do not require any knowledge/details of the used modulation schemes and the way the Tx antennas are used (spatial multiplexing, space-time codes,etc.) as it is fully data-driven and not decision-theoretic based. The results of our approach are characterized by high classification accuracy and they pave the way for more ML-based data-driven techniques that reveal more characteristics of the Tx