Cyclostationarity-based blind classification of STBCs for cognitive radio systems

Abstract

Signal classification is a major task of a cognitive radio. This paper proposes a novel cyclostationarity-based algorithm for the blind classification of space time block codes (STBCs) and derives analytical expressions for the second-order cyclic statistics used as the basis of the algorithm. Monte Carlo simulation results demonstrate a good classification performance with low sensitivity to phase noise and the channel Doppler shift. The proposed approach avoids the need for a priori knowledge of the channel coefficients, carrier phase, and timing offsets. Moreover, it does not need accurate information about the transmission data rate and carrier frequency offset.Index Terms-Signal intelligence and cognitive radio, signal cyclostationarity, signal classification, multiple antenna systems. I. INTRODUCTIONCognitive radio (CR), as a promising technology for improving the effective utilization of the radio spectrum, has recently attracted much attention [1], [2]. Spectrum awareness is currently one of the most challenging problems in CR design; it is a prerequisite for adaptively responding to changes in the signal environment. Consequently, the detection and classification of signals with relaxed a priori information about the signal parameters is critical for proper CR functionality. The problem of signal classification for multi-antenna (MA) systems remains relatively unexplored. Previously reported investigations [3], [4], [5], [6], [7], [8] all relied on the assumption that the data rate, carrier phase, and frequency offsets, phase noise and Doppler shift were precisely known. Since these conditions are unrealistic in practice, further work is needed to address the important practical problem of signal classification for MA systems affected by transmission impairments. In this paper, we develop and analyze an STBC classification algorithm based on the second-order cyclostationarity observed with two receive antennas, in the presence of transmission impairments. This algorithm does not require information about channel, carrier phase and timing offsets. Moreover, it does not need accurate information about the transmission data rate and carrier frequency offset, and is robust with respect to channel Doppler shift and phase noise.The rest of the paper is organized as follows. Section II presents the signal model and problem formulation. Second-order cyclostationarity of STBCs and the proposed algorithm are introduced in Section III. Simulation results are provided in Section IV. Finally, conclusions are drawn

SFBC Recognition over Orthogonal Frequency Division Multiplexing Schemes in the Presence of Inphase and Quadrature Phase Discrepancies for Cognitive Radio Applications

“…Many aspects of WR have been the subject of extensive research, including the recognition of modulators [19][20][21][22], channel control encoders [23,24], multi-carrier broadcasts [1,2,12], and space coding emissions [25][26][27][28][29]. The following discussion delivers a background on latest developments in the subject of space coding recognition, which is the principal focus of this investigation.…”

“…These methods relied on the fourth-order moment as an identifying feature and required a recipient that was only outfitted with a single antenna. By examining the cyclostationarity of the waveforms coming from two antennas, it was possible to distinguish between multiple space time block code (STBC) waveforms even when experiencing a wide range of broadcast problems [26,27]. The authors of [28] distinguished between SM and AL waveforms by analyzing the dispersion properties of multiple tapped radio channels.…”

Modulation Awareness Method for Dual-Hop Cooperative Transmissions over Frequency-Selective Channels

“…We employ the widely held premise in the literature that data symbols are unrelated to each other [ 30 , 31 ]. We write where is a random variable’s statistical expectation, is the function of Kronecker delta, and BPSK refers to a binary phase shift keying modulation.…”

Higher order statistics for modulation and STBC recognition in MIMO systems

“…Both likelihood-based (LB) and feature-based (FB) approaches were considered in the literature. For SC systems, the LB method was investigated in [21,25], while the FB approach was considered in [22,[24][25][26][27][28][29][30][31]. The FB approach includes the second-and fourth-order statistics [22,23,26,29,[32][33][34][35] and cyclostationarity-based methods [23,24,36,37].…”