Frame Generation Algorithm for Differentiated Services in Tactical Communication Networks

Abstract

In response to the complex and multidimensional nature of converged traffic on heterogeneous links in tactical communication networks, which leads to the difficulty in ensuring the quality of service (QoS) requirements for critical services, a frame generation algorithm for differentiated services (DS-FG) is proposed. DS-FG deploys an adaptive frame generation algorithm based on deep reinforcement learning (DRL-FG) for time-sensitive service, while deploying a high efficient frame generation (HEFG) algorithm for non-time-sensitive service. DRL-FG constructs a reward function by combining the queue status information of time-sensitive service and utilizes deep deterministic policy gradients (DDPG) to train a decision model for adaptive frame generation (AFG) algorithm thresholds. Furthermore, Gaussian noise sampling and prioritized experience replay strategies are employed to enhance model training efficiency and performance, achieving optimal matching between time-sensitive service QoS requirements and frame generation thresholds. Experimental results demonstrate that DS-FG outperforms traditional algorithms, achieving up to 13% improvement in throughput and over 19.7% reduction in average queueing delay for time-sensitive service.