Detection of Spectrum Sensors for Maximizing Eigenvalue and Hardware Efficiency in Cognitive Radio Networks Using Machine Learning
Keywords:
Eigenvalue Detection, Cognitive Radio Network, Triangular Systolic Array (TSA), Maximum Eigenvalue Detection (MED), Fusion Centre (FC), Empirical Mode DecompositionAbstract
Cognitive radio networks (CRNs) make it possible for opportunistic spectrum access by dynamically identifying and utilising underutilised frequency bands. In this research, we provide a spectrum sensor architecture for CRNs that is hardware-efficient and based on simulated Maximum Eigenvalue Detection (MED). The suggested architecture makes use of MED, a dependable technique for locating signals in noisy situations. We address the resource limitations of actual CRN devices as we propose a hardware-efficient version of this detection algorithm. A combination of analogue and digital processing steps is used in the architecture. The signal that has been previously established undergoes initial filtration and amplification via the analogue front-end. Subsequently, it undergoes a conversion process from analogue to digital format. The next step in the digital processing stage is pre-processing, which includes feature extraction and noise removal. The covariance matrix (CM) is built using the retrieved characteristics, and the greatest eigenvalue is determined from this matrix. Offer a Simulated Maximum Eigenvalue Detection (SMED) method to increase hardware effectiveness. Employ a portion of the conventional signal illustrations to estimate the greatest eigenvalue rather than computing the whole covariance matrix. This maintains detection performance while substantially reducing computing complexity. Using effective parallel processing units and improved memory management, create a hardware architecture especially suited for the suggested approach. The architecture guarantees energy economy and real-time processing, which are essential for CRN devices with limited resources. Numerous simulations and comparisons with current spectrum sensing methods show how effective and efficient the suggested architecture is. Software from Xilinx was used to develop a hardware-efficient architecture with a fast spectrum sensor based on MED. The findings demonstrate that SMED-based spectrum sensors, which are hardware-efficient, can identify signals with high reliability while using a fraction of the resources. Spectrum sensing in CRNs is made simple and hardware-friendly by the suggested architecture. Because of its effective implementation, cognitive radio devices can use the spectrum more effectively, improving overall network performance
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