The field of 6G networks and beyond is rapidly evolving, with a focus on enhancing reliability, reducing latency, and improving overall network performance. Researchers are exploring innovative solutions, such as integrating non-terrestrial networks and leveraging reinforcement learning, to address the challenges posed by dynamic wireless environments. Notably, the use of high-altitude platform stations (HAPS) and mobile base stations (gNBs) is being investigated to improve network reliability and maintain Line-of-Sight (LoS) connectivity. Additionally, there is a growing interest in developing site-specific cellular network simulation tools, such as ray-tracing-driven ns-3, to accurately model and evaluate the performance of 5G and 6G networks.

Some noteworthy papers in this area include: AoI-Aware Resource Allocation with Deep Reinforcement Learning for HAPS-V2X Networks, which presents a reinforcement learning-based approach to optimize age-of-information in HAPS-enabled vehicle-to-everything networks. A Reinforcement Learning Framework for Mobility Control of gNBs in Dynamic Radio Access Networks, which introduces a Deep Q-Network agent that learns to reposition gNBs proactively in response to dynamic environmental changes. Enabling Site-Specific Cellular Network Simulation Through Ray-Tracing-Driven ns-3, which extends the 5G-LENA module with a trace-based channel model to deliver site-specific geometric fidelity. CONVERGE: A Multi-Agent Vision-Radio Architecture for xApps, which proposes a novel architecture for delivering real-time radio and video sensing information to O-RAN xApps through a multi-agent approach.