Intelligent Resource Allocation for NB-IoT Networks Using TD3 Reinforcement Learning

Compared to previous IoT principles, NB-IoT provides end customers with a higher quality of service (QoS) in an era where everything is connected to the internet. The Third-Generation Partnership Project (3GPP) for Low-Power Wide-Area Networks (LPWAN) introduced the narrowband Internet of Things (NB-IoT), a new cellular radio access technology based on Long-Term Evolution (LTE). The main objectives of NB-IoT are to enable low-power, low-cost, and low-data-rate communication and to support massive machine-type communication (mMTC). One of the more difficult tasks in uplink transmission is resource allocation. While numerous suggestions have been made for efficient resource distribution, a comprehensive and successful solution has not yet been provided. In this article, we attempt to suggest a resource allocation technique based on reinforcement learning (RL). Reinforcement learning is a subset of machine learning that essentially employs an agent to act in the environment and gather rewards, both positive and negative. We will be using the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm as it's an improvement to its predecessor algorithm (DDPG). We will be tweaking the parameters in RL for better efficiency: Latency, Throughput, energy efficiency, fairness, and rewards.