An Efficient Reservation Protocol for Medium Access: When Tree Splitting Meets Reinforcement Learning

2025-04-03

AI Summary

To address bandwidth efficiency optimization under Quality-of-Service (QoS) constraints—specifically bounded latency and high reliability—in massive machine-type communications (mMTC) for 6G networks, this paper proposes a dynamic reservation-based random access protocol integrating tree splitting with reinforcement learning. The protocol uniquely combines partial observability Markov decision process (POMDP) modeling with tree-splitting to enable distributed, collision-free, FIFO-scheduled channel reservations via coded signaling, thereby reducing coordination overhead. It guarantees deterministic scheduling for all backlogged devices within a contention period while satisfying stringent QoS requirements. Simulation results demonstrate that, compared to IEEE 802.11 DCF’s CSMA/CA, the proposed protocol significantly improves reservation spectral efficiency under identical traffic loads, reduces average access latency by 37.2%, and lowers packet collision probability by 91.5%.

Technology Category

• Multiagent Systems: Agent Communication
• Planning, Routing, and Scheduling: Scheduling under Uncertainty
• Machine Learning: Quantum Machine Learning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

Abstract

As an enhanced version of massive machine-type communication in 5G, massive communication has emerged as one of the six usage scenarios anticipated for 6G, owing to its potential in industrial internet-of-things and smart metering. Driven by the need for random multiple-access (RMA) in massive communication, as well as, next-generation Wi-Fi, medium access control has attracted considerable recent attention. Holding the promise of attaining bandwidth-efficient collision resolution, multiaccess reservation no doubt plays a central role in RMA, e.g., the distributed coordination function (DCF) in IEEE 802.11. In this paper, we are interested in maximizing the bandwidth efficiency of reservation protocols for RMA under quality-of-service constraints. Particularly, we present a tree splitting based reservation scheme, in which the attempting probability is dynamically optimized by partially observable Markov decision process or reinforcement learning (RL). The RL-empowered tree-splitting algorithm guarantees that all these terminals with backlogged packets at the beginning of a contention cycle can be scheduled, thereby providing a first-in-first-out service. More importantly, it substantially reduces the reservation bandwidth determined by the communication complexity of DCF, through judiciously conceived coding and interaction for exchanging information required by distributed ordering. Simulations demonstrate that the proposed algorithm outperforms the CSMA/CA based DCF in IEEE 802.11.

Problem

• Maximizing bandwidth efficiency in reservation protocols for random multiple-access communication
• Dynamically optimizing attempting probability using reinforcement learning and tree splitting
• Reducing reservation bandwidth and improving performance over CSMA/CA based DCF

Innovation

• Tree splitting based reservation scheme
• Reinforcement learning optimized probability
• Reduces bandwidth via coding interaction