Wavelet Analysis-Based OFDM with BPSK Modulation: Spectral Classification and Prediction Approaches

Application of Wavelet Analysis for Spectral Classification and Prediction in BPSK-OFDM Systems

BPSK (Binary Phase Shift Keying) and OFDM (Orthogonal Frequency Division Multiplexing) technologies are fundamental components in modern communication systems. Introducing wavelet analysis into this domain provides novel perspectives for analyzing spectral characteristics and predicting signal behavior.

Advantages of Wavelet Analysis The multi-resolution characteristic of wavelet transform makes it particularly suitable for analyzing non-stationary signals. Compared to traditional Fourier transforms, wavelets simultaneously provide localized information in both time and frequency domains, which proves especially valuable for analyzing OFDM signals composed of multiple subcarriers. Implementation typically involves using wavelet functions (e.g., via MATLAB's wavelet toolbox) with adjustable scaling parameters to capture signal features at different resolutions.

Decomposition of BPSK-OFDM Signals By selecting appropriate wavelet basis functions (such as Daubechies wavelets), received BPSK-OFDM signals can be decomposed into different scales. Each scale's coefficients reflect the energy distribution across various frequency bands, establishing the foundation for subsequent spectral classification. Code implementation would involve using wavelet decomposition functions (e.g., wavedec in MATLAB) with proper level selection to separate signal components effectively.

Spectral Feature Extraction and Classification Based on wavelet decomposition coefficients from different layers, statistical features (such as energy and entropy values) can be extracted as classification criteria. Combined with machine learning algorithms, this approach enables: Identification of different channel conditions Detection of signal anomalies Distinction between various modulation schemes A typical implementation might calculate energy features using squared coefficient sums and employ classifiers like SVM or neural networks for pattern recognition.

Signal Quality Prediction The time-varying characteristics of wavelet coefficients can be utilized to construct prediction models. By analyzing patterns in historical wavelet feature variations, future signal attenuation levels or interference conditions can be predicted, providing decision support for adaptive modulation schemes. This could involve time-series analysis of wavelet coefficients using ARIMA models or recurrent neural networks for temporal pattern recognition.

This method proves particularly suitable for wireless communication systems in multipath environments, where its multi-scale analysis capability helps overcome limitations of traditional methods in joint time-frequency analysis. Practical applications require careful selection of wavelet bases and optimization of decomposition levels to balance computational complexity with analytical accuracy, often achieved through parameter sweeping and cross-validation techniques.