A Survey of Wideband Spectrum Sensing Algorithms for Cognitive Radio Networks and Sub-Nyquist Approaches
Bashar I Ahmad

TL;DR
This survey reviews wideband spectrum sensing algorithms for cognitive radio networks, emphasizing sub-Nyquist sampling techniques, their advantages, limitations, and performance considerations for efficient spectrum utilization.
Contribution
It provides a comprehensive comparison of various wideband sensing methods, highlighting recent advances and challenges in sub-Nyquist approaches for cognitive radio.
Findings
Sub-Nyquist sampling reduces data rates and hardware complexity.
Wideband sensing techniques vary in accuracy and resource requirements.
Cooperative sensing enhances spectrum detection performance.
Abstract
Cognitive Radio (CR) networks presents a paradigm shift aiming to alleviate the spectrum scarcity problem exasperated by the increasing demand on this limited resource. It promotes dynamic spectrum access, cooperation among heterogeneous devices, and spectrum sharing. Spectrum sensing is a key cognitive radio functionality, which entails scanning the RF spectrum to unveil underutilised spectral bands for opportunistic use. To achieve higher data rates while maintaining high quality of service QoS, effective wideband spectrum sensing routines are crucial due to their capability of achieving spectral awareness over wide frequency range(s)\ and efficiently harnessing the available opportunities. However, implementing wideband sensing under stringent size, weight, power and cost requirements (e.g., for portable devices) brings formidable design challenges such as addressing potential…
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Taxonomy
TopicsCognitive Radio Networks and Spectrum Sensing · Sparse and Compressive Sensing Techniques · Advanced Adaptive Filtering Techniques
