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An adaptive standardized protocol is essential for addressing inter-sliceresource contention and conflict in network slicing. Traditional protocolstandardization is a cumbersome task that yields hardcoded predefinedprotocols, resulting in increased costs and delayed rollout. Going beyond theselimitations, this paper proposes a novel multi-agent deep reinforcementlearning (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, resourceorchestration agents adapt their communication messages to promote theemergence of a protocol on-the-fly, which enables the mitigation of conflictand resource contention between network slices. STEP weaves together the notionof information bottleneck (IB) theory with deep Q-network (DQN) learningconcepts. By incorporating a stochastic bottleneck layer -- inspired byvariational autoencoders (VAEs) -- STEP imposes an information-theoreticconstraint for emergent inter-agent communication. This ensures that agentsexchange concise and meaningful information, preventing resource waste andenhancing the overall system performance. The learned protocols enhanceinterpretability, laying a robust foundation for standardizing next-generation6G networks. By considering an O-RAN compliant network slicing resourceallocation problem, a conflict resolution protocol is developed. In particular,the results demonstrate that, on average, STEP reduces inter-slice conflicts byup to 6.06x compared to a predefined protocol method. Furthermore, incomparison with an MADRL baseline, STEP achieves 1.4x and 3.5x lower resourceunderutilization and latency, respectively.

Intelligible Protocol Learning for Resource Allocation in 6G O-RAN Slicing