Optimized Resource Management Using Binarized Spiking Neural Network With Pyramid Attention and Smart Contract Blockchain for Sustainable Spectrum Allocation in 6G

Abstract

The rapid expansion of connected devices and the need for high-speed communication make sustainable spectrum allocation and resource management crucial issues in 6G networks. Traditional spectrum management methods often rely on static or centralized techniques, limiting adaptability and efficiency in dynamic network environments. Additionally, these methods struggle to balance energy efficiency, latency, and spectrum utilization. To address limitations, this work proposes a novel Binarized Spiking Neural Network with Pyramid Attention Network (BSNN-PAN)-based spectrum allocation approach that integrates Pyramid Attention Networks (PANs) with binarized spiking neural networks (BSNNs). The approach employs Time-to-First-Spike (TTFS) coding to encode input spectrum data into spike trains, which are processed by binary Integrate-and-Fire (IF) neurons. PAN introduces attention weights to capture multiscale spatial and temporal dependencies, enhancing feature extraction and decision-making. The prairie dog optimization algorithm (PDOA) is applied to optimize hyperparameters such as weight, error, and loss, improving adaptability, efficiency, and resource allocation. The collaboration of the three significantly improves decision-making efficiency and security in dynamic environments, demonstrating the added value of their technological integration. Experimental results demonstrate significant improvements, including 1.8-kJ energy consumption, 412-ms transaction latency, and 15-ms average channel allocation time. These outcomes validate the proposed BSNN-PAN framework as an effective and sustainable solution for next-generation 6G spectrum management.