Intellicise Router Promotes Endogenous Intelligence in Communication Network

Abstract

Endogenous intelligence has emerged as a crucial aspect of next-generation communication networks. This concept is closely intertwined with artificial intelligence (AI), with its primary components being data, algorithms, and computility. Data collection remains a critical concern that warrants focused attention. To address the challenge of data expansion and forwarding, the intellicise router is proposed. It extends the local dataset and continuously enhances the local model through a specifically crafted algorithm, which enhances AI performance, as exemplified by its application in image recognition tasks. Service capability is employed to gauge the router’s ability to provide services and the upper bounds are derived. To analyze the algorithm’s effectiveness, a category-increase model is developed to calculate the probability of categories rising under both equal and unequal probabilities of image communication categories. The numerical analysis results align with simulation results, affirming the validity of the category-increase model. To assess the performance of the intellicise router, a communication system is simulated. A comparative analysis of these experimental results demonstrates that the intellicise router can continuously improve its performance to provide better service.