Quickest Sequential Multiband Spectrum Sensing with Mixed Observations

Area of Science:

• Electrical Engineering
• Computer Science
• Signal Processing

Background:

• Spectrum sensing is crucial for cognitive radio systems to identify available frequency channels.
• Sequential sensing with a narrow-band sensor presents challenges in speed and accuracy when dealing with numerous potential channels.

Purpose of the Study:

• To develop a novel two-stage mixed-observation sensing strategy for cognitive radio systems.
• To address the challenge of quickly and accurately identifying unoccupied channels in a sequential sensing scenario.

Main Methods:

• A two-stage strategy involving a scanning stage (observing linear combinations of signals from channel pairs) and a refinement stage (observing signals from a single identified channel).
• Utilizing multiple stopping time theory to solve the ordered two concatenated Markov stopping time problem.
• Proposing a low-complexity algorithm employing cumulative sum (CUSUM) test in the scanning stage and sequential probability ratio test (SPRT) in the refinement stage.

Main Results:

• The proposed strategy effectively identifies potential unoccupied channels in the scanning stage.
• The low-complexity algorithm demonstrates significant reductions in sensing time, particularly when unoccupied channels are rare.
• Performance analysis confirms the strategy's effectiveness even when the majority of potential channels are occupied.

Conclusions:

• The novel two-stage mixed-observation sensing strategy offers a significant improvement in spectrum sensing efficiency for cognitive radio.
• The proposed low-complexity algorithm provides a practical and effective solution for implementing advanced sensing strategies.
• This approach enhances cognitive radio performance by reducing the time required to find available spectrum.