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.

翻译：缓存辅助协作网络（BACNs）因其在超五代（B5G）或第六代（6G）关键场景中的潜在应用而备受关注。本文通过探讨B5G/6G网络中缓存辅助中继的多种典型应用场景，强调了融入BACN的重要性。此外，我们深入研究了BACN中的关键技术挑战，包括严格的时延约束、高可靠性、非完美信道状态信息（CSI）、传输安全以及集成网络架构。为应对这些挑战，我们提出利用基于深度学习的方法来设计和运行带有BACN的B5G/6G网络，这与传统的缓存辅助中继选择方案有所区别。特别地，我们通过两个案例研究展示了集中式深度强化学习（DRL）和分布式DRL在缓存辅助非地面网络中的有效性。最后，我们概述了B5G/6G中与BACN应用相关的未来研究方向。