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 article 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 6.06× compared to a predefined protocol method. Furthermore, in comparison with an MADRL baseline, STEP achieves 1.4× and 3.5× lower resource underutilization and latency, respectively.

中文翻译：

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6G O-RAN 切片中资源分配的智能协议学习


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。传统的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，从而导致成本增加和推出延迟。超越这些限制，本文提出了一种新颖的多智能体深度强化学习 (MADRL) 通信框架，称为独立可解释协议 (STEP)，用于未来第六代 (6G) 开放无线接入网络 (O-RAN) 切片。随着新情况的出现并影响网络运行，资源编排代理会调整其通信消息以促进动态协议的出现，从而减轻网络切片之间的冲突和资源争用。 STEP 将信息瓶颈 (IB) 理论的概念与深度 Q 网络 (DQN) 学习概念结合在一起。通过结合随机瓶颈层（受变分自动编码器 (VAE) 的启发），STEP 为紧急代理间通信施加了信息论约束。这确保代理交换简洁且有意义的信息，防止资源浪费并提高整体系统性能。学习到的协议增强了可解释性，为下一代 6G 网络标准化奠定了坚实的基础。通过考虑符合 O-RAN 的网络切片资源分配问题，开发了冲突解决协议。特别是，结果表明，与预定义的协议方法相比，STEP 平均将片间冲突减少了 6.06 倍。此外，与 MADRL 基线相比，STEP 的资源利用率和延迟分别降低了 1.4 倍和 3.5 倍。