Cooperative Multiband Spectrum Sensing Using Radio Environment Maps and Neural Networks

Area of Science:

• Wireless Communication Engineering
• Signal Processing
• Artificial Intelligence

Background:

• Cognitive radio networks (CRNs) are essential for efficient spectrum utilization.
• Accurate detection of primary users (PUs) and spectrum holes is critical for secondary users (SUs).
• Existing methods require robust real-time spectrum monitoring capabilities.

Purpose of the Study:

• To propose and implement a centralized CRN for real-time multiband spectrum monitoring.
• To develop radioelectric environment maps (REMs) for spectrum gap identification.
• To compare digital signal processing (DSP) and neural network (NN) approaches for PU detection.

Main Methods:

• Utilized software-defined radios (SDRs) in a real wireless environment.
• Employed sample entropy for local spectrum occupancy monitoring by SUs.
• Centralized data processing using DSP and NN techniques for PU feature extraction (power, bandwidth, central frequency).

Main Results:

• Both DSP and NN-based CRNs successfully located PUs and identified spectral gaps.
• The NN-based CRN demonstrated superior accuracy in detecting PU carrier frequency and bandwidth.
• The system effectively avoided the hidden terminal problem by providing accurate spectrum availability information.

Conclusions:

• Neural networks provide a highly accurate method for PU detection in CRNs.
• Centralized spectrum monitoring with NNs enhances CRN performance and spectrum efficiency.
• The proposed system effectively maps the radioelectric environment for dynamic spectrum access.