Strategic Offloading of Grant-Based Traffic to Grant-Free Resources: A DRL Based Approach for Optimizing Bandwidth in 5G-Advanced Networks

Efficient bandwidth utilization is essential for nextgeneration cellular networks such as 5G -Advanced and 6G, to support diverse services with varying quality-of-service (QoS) requirements. Grant-free (GF) communication, introduced in 5G NR, enables unscheduled data transmission, significantly reducing latency. However, it often leads to inefficiencies, such as underutilized GF bandwidth in scenarios involving sporadic status messages. In contrast, grant-based (GB) communication, which relies on scheduled transmissions, offers greater reliability. This paper proposes a novel deep reinforcement learning (DRL)-based mechanism to strategically offload GB traffic to underutilized GF resources. Termed “soft access class barring” (SACB), the approach dynamically offloads traffic from GB to GF bandwidth to optimize spectrum utilization while maintaining QoS, without requiring complex bandwidth reconfigurations. Simulation results demonstrate the potential of SACB to address challenges in hybrid traffic management, paving the way for its adoption in future network standards.