Intelligible Protocol Learning for Resource Allocation in 6G O-RAN Slicing
Abstract: 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.
- H.-W. Kao and E. Hsiao-Kuang Wu, “QoE Sustainability on 5G and Beyond 5G Networks,” IEEE Wireless Communications, vol. 30, no. 1, pp. 118–125, 2023.
- O-RAN.WG1, “Slicing Architecture Technical Specification 11.0,” in O-RAN Specifications, October 2023.
- C. Chaccour, W. Saad, M. Debbah, Z. Han, and H. Vincent Poor, “Less Data, More Knowledge: Building Next Generation Semantic Communication Networks,” arXiv preprint arXiv:2211.14343, 2022.
- A. Valcarce and J. Hoydis, “Toward Joint Learning of Optimal MAC Signaling and Wireless Channel Access,” IEEE Transactions on Cognitive Communications and Networking, vol. 7, no. 4, pp. 1233–1243, 2021.
- L. Miuccio, S. Riolo, S. Samarakoon, D. Panno, and M. Bennis, “Learning Generalized Wireless MAC Communication Protocols via Abstraction,” IEEE Global Communications Conference (GLOBECOM), 2022.
- L. Miuccio, S. Riolo, M. Bennis, and D. Panno, “On Learning Intrinsic Rewards for Faster Multi-Agent Reinforcement Learning based MAC Protocol Design in 6G Wireless Networks,” arXiv preprint arXiv:2302.14765, 2023.
- M. Chafii, S. Naoumi, R. Alami, E. Almazrouei, M. Bennis, and M. Debbah, “Emergent Communication in Multi-Agent Reinforcement Learning for Future Wireless Networks,” arXiv preprint arXiv:2309.06021, 2023.
- J. Shao, Y. Mao, and J. Zhang, “Learning Task-Oriented Communication for Edge Inference: An Information Bottleneck Approach,” IEEE Journal on Selected Areas in Communications, vol. 40, no. 1, pp. 197–211, 2022.
- B. C. Geiger, “On Information Plane Analyses of Neural Network Classifiers-A Review,” IEEE Transactions on Neural Networks and Learning Systems, vol. 33, no. 12, pp. 7039–7051, 2022.
- S. Roy, F. Rezazadeh, H. Chergui, and C. Verikoukis, “Joint Explainability and Sensitivity-Aware Federated Deep Learning for Transparent 6G RAN Slicing,” IEEE International Conference on Communications (ICC), 2023.
- J. Zheng, P. Caballero, G. de Veciana, S. J. Baek, and A. Banchs, “Statistical multiplexing and traffic shaping games for network slicing,” in 2017 15th International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt), 2017, pp. 1–8.
- Z. Xie and S. Song, “FedKL: Tackling Data Heterogeneity in Federated Reinforcement Learning by Penalizing KL Divergence,” IEEE Journal on Selected Areas in Communications, vol. 41, no. 4, pp. 1227–1242, 2023.
- F. Rezazadeh, L. Zanzi, F. Devoti, H. Chergui, X. Costa-Pérez, and C. Verikoukis, “On the Specialization of FDRL Agents for Scalable and Distributed 6G RAN Slicing Orchestration,” IEEE Transactions on Vehicular Technology, vol. 72, no. 3, pp. 3473–3487, 2022.
- G. Brockman, V. Cheung, L. Pettersson, J. Schneider, J. Schulman, J. Tang, and W. Zaremba, “OpenAI Gym,” arXiv preprint arXiv:1606.01540, 2016.
- S. M. Lundberg and S. Lee, “A Unified Approach to Interpreting Model Predictions,” in Advances in Neural Information Processing Systems 30, 2017.
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