Automatic Modulation Classification And Parameter Estimation For Multi-carrier Signals

The goal of the new generation wireless communication systems is capable to support higher information transmission speed, which should be able to provide more flexible function for immense applications, and have an ability to adapt the complex electromagnetic environment simultaneously. Due to the advantages of the high spectrum efficiency and capability to mitigate the side-effect of multipath propagation, multi-carrier modulation (MCM) techniques, especially the orthogonal frequency division multiplexing (OFDM), have been widely applied for the broad-band and high-data-rate communication systems.In the field of non-cooperation communication, such as electromagnetic spectrum monitoring, signal reconnaissance and electronic countermeasure, it is significant to take blind analysis tasks on the received signals, which need to recognize signal modulation scheme and to estimate demodulation parameters automatically, with the purpose of providing essential preconditions for demodulation and information recovery. Delves into the particularity of MCM system structure, this dissertation mainly focuses on the key techniques in automatic modulation classification and parameter estimation for MCM signals. The relevant theories and algorithms are also realized on a general signal processing platform. The main work and innovative achievements can be summarized as follows:1. Based on the analysis of signal characteristic and modulation principle, the paper establishes blind analysis mathematical models for OFDM and offset quadrature amplitude modulation (OQAM) signals, and points out the particularities of blind analysis for MCM signals: the variety of modulation schemes, the multiplicity of parameter contents, and the variety of operations to induce inherent cyclostationarity.2. Deduces the expressions of second-order cyclic cumulants (CC) for zero-padding OFDM (ZP-OFDM) and OQAM signals. Based on the analysis of different CC profiles, a second-order/zero-conjugate CC feature is proposed to achieve the identification of OQAM signals, and the cyclic autocorrelation function (CAF) feature is exploited to classify among single carrier linearly digitally modulated (SCLD) signals, cyclic prefixing OFDM (CP-OFDM) and ZP-OFDM signals. Finally, a classification scheme is carried out using support vector machine (SVM) structure approach, and the effectiveness of this method is illustrated with experimental results.3. According to the equivalent IFFT implementation models of MCM signals, the second-order cyclostationarity for OQAM and OFDM signals is proved, and the explicit expression of spectral correlation function (SCF) is derived. Based on this, the existing SCF expression of CP-OFDM is corrected, meanwhile, a conclusion is reached that cyclostationarity for the generic MCM signals is induced by the pulse shaping operation and the periodicity of modulated sequence. Simulation results sustain the theoretical analysis and indicate that our features are insensitive to the influence of noise and multipath fading channel.4. Aiming at the colored-background noise problem, caused by the nonlinear transform in parameter estimation, a novel algorithm is proposed based on the discrete gray-scale morphologic filtering, which can suppress colored-background noise effectively and improve the lines detection capability in the case of low SNR, small pulse shape coefficient and signal symbols. Then, after taking full advantages of CAF and SCF profile features for OFDM and OQAM signals, a joint algorithm of blind parameter estimation is investigated, which improves the estimation performance of existing method for MCM signals in the fading channels under blind reception conditions.5. A comprehensive MCM signal analysis system is preliminary implemented and tested by real MCM signals. Experimental testing results validate that the system is pragmatic and above algorithms lead to an efficient performance in practical signal analysis scenarios.