An adaptive standardized protocol is essential for addressing inter-slice resource contention and conflict in network slicing. Traditional protocol standardization is a cumbersome task that yields hardcoded predefined protocols, resulting in increased costs and delayed rollout. Going beyond these limitations, this paper proposes a novel multi-agent deep reinforcement learning (MADRL) communication framework called standalone explainable protocol (STEP) for future sixth-generation (6G) open radio access network (O-RAN) slicing. As new conditions arise and affect network operation, resource orchestration agents adapt their communication messages to promote the emergence of a protocol on-the-fly, which enables the mitigation of conflict and resource contention between network slices. STEP weaves together the notion of information bottleneck (IB) theory with deep Q-network (DQN) learning concepts. By incorporating a stochastic bottleneck layer -- inspired by variational autoencoders (VAEs) -- STEP imposes an information-theoretic constraint for emergent inter-agent communication. This ensures that agents exchange concise and meaningful information, preventing resource waste and enhancing the overall system performance. The learned protocols enhance interpretability, laying a robust foundation for standardizing next-generation 6G networks. By considering an O-RAN compliant network slicing resource allocation problem, a conflict resolution protocol is developed. In particular, the results demonstrate that, on average, STEP reduces inter-slice conflicts by up to 6.06x compared to a predefined protocol method. Furthermore, in comparison with an MADRL baseline, STEP achieves 1.4x and 3.5x lower resource underutilization and latency, respectively.

翻译：一种自适应标准化协议对于解决网络切片中的切片间资源争用和冲突至关重要。传统协议标准化是一项繁琐的任务，会产生硬编码的预定义协议，导致成本增加和部署延迟。为突破这些局限，本文提出了一种名为独立可解释协议（STEP）的新型多智能体深度强化学习（MADRL）通信框架，用于未来第六代（6G）开放无线接入网络（O-RAN）切片。当新条件出现并影响网络运行时，资源编排智能体动态调整其通信消息以促进协议的即时生成，从而缓解网络切片间的冲突和资源争用。STEP融合了信息瓶颈（IB）理论与深度Q网络（DQN）学习概念。通过引入受变分自编码器（VAE）启发的随机瓶颈层，STEP为智能体间涌现式通信施加了信息论约束。这确保了智能体交换简洁且有意义的信息，从而避免资源浪费并提升整体系统性能。学习到的协议增强了可解释性，为下一代6G网络的标准化奠定了坚实基础。通过考虑符合O-RAN规范的网络切片资源分配问题，我们开发了一种冲突解决协议。结果表明，与预定义协议方法相比，STEP平均将切片间冲突减少高达6.06倍。此外，与MADRL基线相比，STEP分别实现了1.4倍和3.5倍的资源利用率提升和时延降低。