Dynamic Control for Random Access in Deadline-Constrained Broadcasting

TL;DR

This paper introduces a dynamic control scheme for deadline-constrained broadcasting that optimizes timely delivery ratio by adapting transmission probabilities based on current network conditions, using MDP and POMDP frameworks.

Contribution

It develops a novel adaptive transmission control approach for deadline-constrained broadcasting, including an optimal scheme in ideal conditions and a practical heuristic for realistic environments.

Findings

Heuristic scheme achieves near-optimal TDR in realistic settings

Optimal policies exist for both fully known and partially known contention environments

Numerical results validate the effectiveness of the proposed schemes

Abstract

This paper considers random access in deadline-constrained broadcasting with frame-synchronized traffic. To enhance the maximum achievable timely delivery ratio (TDR), we define a dynamic control scheme that allows each active node to determine the transmission probability with certainty based on the current delivery urgency and the knowledge of current contention intensity. For an idealized environment where the contention intensity is completely known, we develop an analytical framework based on the theory of Markov Decision Process (MDP), which leads to an optimal scheme by applying backward induction. For a realistic environment where the contention intensity is incompletely known, we develop a framework using Partially Observable Markov Decision Process (POMDP), which can in theory be solved. We show that for both environments, there exists an optimal scheme that is optimal over all types of policies. To overcome the infeasibility in obtaining an optimal or near-optimal scheme from the POMDP framework, we investigate the behaviors of the optimal scheme for two extreme cases in the MDP framework, and leverage intuition gained from these behaviors to propose a heuristic scheme for the realistic environment with TDR close to the maximum achievable TDR in the idealized environment. In addition, we propose an approximation on the knowledge of contention intensity to further simplify this heuristic scheme. Numerical results with respect to a wide range of configurations are provided to validate our study.