Automatic Identification of Space-Time Block Coding for MIMO-OFDM Systems in the Presence of Impulsive Interference

Signal identification, a vital task of intelligent communication radios, finds its applications in various military and civil communication systems. Previous works on identification for space-time block codes (STBC) of multiple-input multiple-output (MIMO) system employing orthogonal frequency division multiplexing (OFDM) are limited to additive white Gaussian noise. In this paper, we develop a novel automatic identification algorithm to exploit the generalized cross-correntropy function of the received signals to classify STBC-OFDM signals in the presence of Gaussian noise and impulsive interference. This algorithm first introduces the generalized cross-correntropy function to fully utilize the space-time redundancy of STBC-OFDM signals. The strongly-distinguishable discriminating matrix is then constructed by using the generalized cross-correntropy for multiple receive antennas. Finally, a decision tree identification algorithm is employed to identify the STBC-OFDM signals which is extended by the binary hypothesis test. The proposed algorithm avoids the traditionally required pre-processing tasks, such as channel coefficient estimation, noise and interference statistics prediction and modulation type recognition. Numerical results are presented to show that the proposed scheme provides good identification performance by exploiting the generalized cross-correntropy function of STBC-OFDM signals under impulsive interference circumstances

Spatial-Frequency Block Coding Automatic Recognition with Non-Gaussian Interference for Cognitive MIMO-OFDM Systems

Space-time/frequency block coding (STBCs/SFBCs) scheme is a crucial technique for enhancing the effectiveness and reliability of multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) systems with cognitive radio (CR) capability. Automatic recognition of STBCs/SFBCs is a prerequisite for achieving dynamic spectrum sharing in cognitive MIMO-OFDM systems. In contrast to existing works, this paper proposes a weighted cross-correlation function-based algorithm to recognize SFBCs for cognitive MIMO-OFDM systems with Gaussian noise and non-Gaussian impulsive interference. The proposed algorithm extracts the space-frequency redundancy information of different OFDM subcarriers on different receiver antenna pairs by using weighted cross-correlation functions. Then, the weighted cross-correlation feature vectors are constructed by exploiting the multi-antenna system so as to design the detection statistics and thresholds based on the central limit theorem. Finally, a decision tree method is adopted to discriminate between several SFBCs. The proposed algorithm does not require prior information such as channel coefficients, modulation schemes, noise power, or interference power. Simulation results show that the proposed algorithm is robust against non-Gaussian impulsive interference and achieves high recognition performance in the case of a small number of samples and a low signal-to-noise ratio. Index Terms-Cognitive radio, multiple-input multiple-output, non-Gaussian impulsive interference, orthogonal frequency division multiplexing, parameter recognition, space-frequency block coding

A Survey of Blind Modulation Classification Techniques for OFDM Signals

Blind modulation classification (MC) is an integral part of designing an adaptive or intelligent transceiver for future wireless communications. Blind MC has several applications in the adaptive and automated systems of sixth generation (6G) communications to improve spectral efficiency and power efficiency, and reduce latency. It will become a integral part of intelligent software-defined radios (SDR) for future communication. In this paper, we provide various MC techniques for orthogonal frequency division multiplexing (OFDM) signals in a systematic way. We focus on the most widely used statistical and machine learning (ML) models and emphasize their advantages and limitations. The statistical-based blind MC includes likelihood-based (LB), maximum a posteriori (MAP) and feature-based methods (FB). The ML-based automated MC includes k-nearest neighbors (KNN), support vector machine (SVM), decision trees (DTs), convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long short-term memory (LSTM) based MC methods. This survey will help the reader to understand the main characteristics of each technique, their advantages and disadvantages. We have also simulated some primary methods, i.e., statistical- and ML-based algorithms, under various constraints, which allows a fair comparison among different methodologies. The overall system performance in terms bit error rate (BER) in the presence of MC is also provided. We also provide a survey of some practical experiment works carried out through National Instrument hardware over an indoor propagation environment. In the end, open problems and possible directions for blind MC research are briefly discussed