Random Access Optimization With Generative Adversarial Networks in Industrial IoT Using Deep Deterministic Policy Gradient Approach

To ensure extensive connectivity for a large number of Internet of Things (IoT) devices, there is a critical need for effective random access (RA). However, the substantial number of IoT users accessing the network leads to severe collisions, which cause delays in establishing successful RA connections and ultimately degrade system performance. While recent research has focused on binary preamble detection and the management of random access channel (RACH) overload, it has not specifically addressed RA overload for massive IoT users. This paper proposes a joint solution for preamble collision detection and timing advance (TA) prediction, referred to as PC-TA, inspired by generative adversarial networks (GANs). We utilize an actor-critic-based deep deterministic policy gradient (AC-DDPG) framework as one of the neural networks within the GAN, specifically designed to tackle preamble collision detection and resolution for numerous IoT users. In addition, we implement a fully connected graph neural network (GNN) as the second neural network in the GAN to predict timing advance (TA), which improves the average packet success rate and reduces overall latency. Simulation results validate the effectiveness of the proposed PC-TA, showing an approximate 85% gain over state-of-the-art methods in the existing literature. Consequently, this approach significantly enhances the chances of rapid RA success, allowing each IoT device to achieve successful RA with fewer trials.