HALO: Hybrid Adaptive Load Offloading of Grant-Based Traffic to Grant-Free Bandwidth for Spectrum Efficiency in Next Generation Networks

Abstract

Efficient spectrum utilization is critical for next-generation cellular networks such as 5G-Advanced and 6G, which must support diverse services with heterogeneous quality-of-service (QoS) requirements. Grant-free (GF) communication in 5G new radio enables low-latency unscheduled transmissions but often suffers from underutilized resources due to sporadic traffic patterns. In contrast, grant-based (GB) communication offers reliable scheduled access but can experience congestion and resource exhaustion under high load. This paper proposes hybrid adaptive load offloading (HALO), a context-oriented deep reinforcement learning (DRL)-based framework that dynamically offloads low-priority GB traffic to underutilized GF resources during congestion periods. HALO effectively implements soft access class barring, redirecting rather than blocking traffic, without requiring complex bandwidth reconfiguration. The framework adapts to real-time network conditions to balance spectrum efficiency, information freshness of GF traffic, and QoS satisfaction across multiple service classes. Simulation results demonstrate significant bandwidth utilization gains of up to 40% compared to baseline approaches, highlighting the effectiveness of HALO for hybrid traffic management and its applicability to future 5G-Advanced and emerging 6G standards.