Implementation of Various Spectrum Sensing Techniques with Code Examples

Spectrum sensing is a crucial technology in cognitive radio systems, used to detect the presence of primary user signals. Common spectrum sensing techniques include the following:

Energy Detection: The simplest sensing method that determines spectrum occupancy by measuring the received signal power. Its main advantage is low computational complexity, but it's susceptible to noise interference. Implementation typically involves calculating signal power using FFT or time-domain squaring operations, with a threshold comparison for decision making.

Matched Filtering: A correlation-based detection method that utilizes known signal characteristics, offering optimal signal-to-noise ratio performance. However, it requires precise prior knowledge of primary user signal parameters. Code implementation involves designing a filter matched to the known signal waveform and performing convolution operations.

Cyclostationary Feature Detection: This technique leverages periodic statistical properties of signals for detection, providing strong noise resistance at the cost of higher computational complexity. Implementation requires cyclic autocorrelation function calculations and spectral correlation density analysis to identify unique cyclic frequencies.

Cooperative Sensing: Multiple sensing nodes share detection results to improve reliability, though it depends on communication coordination mechanisms. Algorithm implementation involves data fusion techniques like weighted combination or voting schemes across distributed nodes, often requiring synchronization protocols.

Each method has distinct advantages and limitations. In practical applications, hybrid approaches combining multiple techniques are frequently employed to enhance detection accuracy and robustness.