Design and Hardware Realization of Adaptive Absolute Score Algorithm on FPGA for Intelligent Cognitive Radio Networks
Keywords:
Cognitive Radio, Spectrum Sensing, Adaptive Absolute SCORE Algorithm, FPGA Implementation, Blind Signal Extraction, Non-Stationary Signals, Hardware Acceleration, SINR Improvement, Real-Time Processing, Intelligent Wireless Networks.Abstract
The rapid growth of wireless communication systems has intensified spectrum scarcity, making efficient spectrum sensing a critical requirement in Intelligent Cognitive Radio (CR) networks. Traditional spectrum sensing techniques often struggle with blind signal extraction in dynamic and non-stationary environments, where interference, noise uncertainty, and time-varying channel conditions degrade detection performance. This creates the need for robust, adaptive, and hardware-efficient sensing mechanisms capable of operating in real time. This work presents the design and hardware realization of an Adaptive Absolute SCORE (Statistically Consistent and Optimal Recovery Estimator) algorithm implemented on a Field-Programmable Gate Array (FPGA) for intelligent cognitive radio networks. The proposed adaptive Absolute SCORE approach enhances blind source separation and signal detection performance by dynamically adjusting algorithmic parameters to accommodate non-stationary signal characteristics and varying interference levels. The integration of adaptive processing improves robustness against noise uncertainty and enhances signal detection accuracy in low Signal-to-Noise Ratio (SNR) conditions. To achieve real-time performance, the algorithm is mapped onto an FPGA platform, leveraging inherent hardware parallelism, pipelined architecture, and low-latency computation. FPGA implementation ensures high throughput, deterministic timing, and energy-efficient processing compared to conventional processor-based solutions. The hardware architecture is optimized to reduce resource utilization while maintaining high detection reliability. Experimental results demonstrate improved Signal-to-Interference-plus-Noise Ratio (SINR), enhanced detection probability, and reduced false alarm rates compared to conventional spectrum sensing methods. The FPGA realization achieves efficient utilization of logic elements, DSP slices, and memory resources while maintaining high processing throughput suitable for real-time cognitive radio applications. The proposed system provides a scalable and hardware-efficient solution for next-generation intelligent spectrum management systems.
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