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

arXiv: https://arxiv.org/abs/2312.07362 · HTML: https://mlsanity.com/paper/2312.07362/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Dec 2023.

## Built on by (most cited first, 1 of 1)

- [DRL-Driven Edge-Aware Utility Optimization for Multi-Slice 6G Networks](https://mlsanity.com/paper/2605.23056/) (2026)

## Abstract (arXiv)

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.