  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.
