CCRPS: Customized cross-domain routing with privacy preservation and stable quality-of-experience based on deep reinforcement learning

Abstract

Next-generation networks are predominantly heterogeneous, integrating diverse network domains built on various technologies. The transmission of data with specific requirements across multiple network domains necessitates advanced cross-domain routing solutions. However, current approaches fall short in providing cross-domain customized routing that incorporates privacy protection and adapts to dynamic network conditions, often overlooking Quality of Experience (QoE) and its stability. To tackle these challenges, we propose CCRPS, a customized cross-domain routing scheme designed for Integrated Heterogeneous Networks (Inte-HetNet), which enables routing customization, supports dynamic network environments, ensures robust cross-domain privacy protection, and delivers consistent and efficient QoE. Specifically, CCRPS begins by formalizing user customization requirements to facilitate routing customization. Next, it reformulates the cross-domain routing generation problem as a multi-agent Deep Reinforcement Learning (DRL) task and develops a Customized Cross-domain Routing algorithm based on Multi-agent DRL (CCR-MD) to address it, ensuring adaptability to dynamic network conditions. Additionally, CCRPS incorporates privacy protection mechanisms, such as virtual topology construction, node attribute calculation, and random obfuscation, to safeguard privacy during cross-domain routing. Moreover, it introduces a QoE-centric reward function to maintain QoE stability. Extensive experimental evaluations demonstrate the superior performance of CCRPS through comparison with existing related schemes.