Today wireless transmissions are one of the most important information sharing methods due to their cableless, anywhere radio propagation nature. Today, so many cell phone companies cannot find a low-overhead, ultra-fast, high-accuracy spectrum sensing scheme for cognitive radio communications. It is always difficult to achieve both sensing accuracy and low-complexity at the same time. Some spectrum sensing schemes are fast, but they are not accurate in terms of describing the spectrum map; or they may be accurate in seeking free spectrum, but they take a complex computation and long sensing time.
Researchers at The University of Alabama have developed a spectrum sensing technology that can be used for any CRN applications. First, to use Compressive signal processing (CSP) for ultra-fast (<10ns) spectrum sensing without spectrum reconstruction. We directly use CSP to avoid the time-consuming signal reconstruction step while meanwhile obtaining the accurate spectrum sensing results (such as which channels are idle and what is the modulation type of PU signals). Secondly, the SVM (support vector machine, an artificial intelligence method) to classify cyclostationary features in CSP to better recognize the spectrum patterns. Thirdly, to adaptively adjust the system parameters (i.e. bandwidth, measurement size, etc.) of our CSP method and find the good performance balance point between the performance and the complexity. This is important to spectrum sensing since it is not a static process anymore. It can self-adjust its operations based on the complex radio environment.
- Ultra-fast sensing.
- Accurate sensing notwithstanding noise.
- Increases pattern recognition performance.
- Simpler algorithm.
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