Fine-Tuning TMDDPG for Dynamic IIoT Bandwidth Allocation

The advancement of Industrial IoT (IIoT) calls for devices that can provide fast communication, for real time operations. Ensuring Quality of Service (QoS) for devices in a network requires effective management of bandwidth. Traditional fixed allocation methods struggle to meet changing needs. This study examines how Software Defined Networking (SDN) with network slicing can tackle this issue. Network slicing divides the network into segments each tailored to QoS needs. In this context we suggest a system that uses a Deep Deterministic Policy Gradient (DDPG) algorithm based on Fine-tuned transfer learning for allocating bandwidth in network slices, for IIoT devices requiring ultra reliable low latency communication. This strategy uses knowledge from existing ML model to optimize allocation and reduce training time and computational resources.