Multidimensional signal analysis

This chapter discusses signal characteristics in multiple domains, leading to the concept of multidimensional signal analysis. Multidimensional signal analysis is referred for analyzing the received radio signal from multiple dimensions jointly or separately. Energy detection, autoregressive modeling of a signal, and correlation techniques can also be performed in the time domain. In frequency domain analysis, the received time discrete samples are transformed to frequency by using discrete Fourier transform which is implemented efficiently with fast Fourier transform. To reveal the temporal and spectral components of a nonstationary signal, joint time-frequency analysis (TFA) could be performed. TFA combines time and frequency domain analysis which is especially useful to understand the characteristics of nonstationary, impulsive, and multicomponent signals. Code domain power analysis provides the signal power projected on a code-space normalized to the total signal power. Autocorrelation and cross-correlation are two popular correlation measurements. Correlation analysis provides the degree of similarity between two signals.