  Buffer-aided cooperative networks (BACNs) have garnered significant attention
due to their potential applications in beyond fifth generation (B5G) or sixth
generation (6G) critical scenarios. This article explores various typical
application scenarios of buffer-aided relaying in B5G/6G networks to emphasize
the importance of incorporating BACN. Additionally, we delve into the crucial
technical challenges in BACN, including stringent delay constraints, high
reliability, imperfect channel state information (CSI), transmission security,
and integrated network architecture. To address the challenges, we propose
leveraging deep learning-based methods for the design and operation of B5G/6G
networks with BACN, deviating from conventional buffer-aided relay selection
approaches. In particular, we present two case studies to demonstrate the
efficacy of centralized deep reinforcement learning (DRL) and decentralized DRL
in buffer-aided non-terrestrial networks. Finally, we outline future research
directions in B5G/6G that pertain to the utilization of BACN.
