Abstract

This paper presents an intelligent dynamic network optimization system for packet-optical transport networks as the industry moves towards 6G. Such a system leverages specific artificial intelligence techniques to dynamically manage the transport network, optimize resource allocation, and guarantee quality of services. A predictive and adaptive Markov decision process is defined by exploiting an ad hoc model of optical-packet nodes and network representation used for the environment description. Comparison of statistical and neural network-based approaches is done for traffic forecasting. QL, DQL, and PPO are compared to solve the reinforcement learning problem. Challenges and opportunities of applying this system in various scenarios are discussed, and assessment is done by simulations that showed advantages in the following aspects: minimization of bandwidth usage guaranteeing quality of services with respect to a conventional system, improvement of optical offload improvement to reduce power consumption and packet processing, and efficient load balancing.

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