Joint space time block code and modulation classification for MIMO systems

Non-cooperative identification of unknown communication signals is a popular research area with widespread civilian and military applications. Multiple input multiple output (MIMO) systems employing multi-antenna transmission pose new challenges to signal identification systems, such as the classification of the employed space time block code (STBC) and modulation in the presence of the self-interference inherent to the multi-antenna transmission. In the existing literature, these two classification problems have been handled separately, despite the fact that they are interrelated. This letter presents a novel approach to MIMO signal identification by considering the modulation type and the STBC classification tasks as a joint classification problem

Space time block code classification for MIMO signals exploiting cyclostationarity

Blind and noncooperative identification of the transmission parameters of unknown communication signals has been employed both in military and civilian applications. Multiple-Input-Multiple-Output (MIMO) transmission systems emerging in the last decade pose new challenges to the signal identification systems, one of which is the identification of the Space-Time Block Code (STBC) used in the transmission. In this work, we present a novel STBC classification algorithm that exploits the joint wide sense cyclostationary characteristics of the coded transmit signals as discriminating features. Compared to existing algorithms, the proposed method can discriminate between a large number of different STBCs

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

MIMO Systems

In recent years, it was realized that the MIMO communication systems seems to be inevitable in accelerated evolution of high data rates applications due to their potential to dramatically increase the spectral efficiency and simultaneously sending individual information to the corresponding users in wireless systems. This book, intends to provide highlights of the current research topics in the field of MIMO system, to offer a snapshot of the recent advances and major issues faced today by the researchers in the MIMO related areas. The book is written by specialists working in universities and research centers all over the world to cover the fundamental principles and main advanced topics on high data rates wireless communications systems over MIMO channels. Moreover, the book has the advantage of providing a collection of applications that are completely independent and self-contained; thus, the interested reader can choose any chapter and skip to another without losing continuity