Distributed Cooperation Under Uncertainty in Drone-Based Wireless Networks: A Bayesian Coalitional Game

TL;DR

This paper introduces a Bayesian cooperative game framework for drone-based wireless networks, enabling distributed resource sharing under uncertainty to optimize network transmission rates.

Contribution

It presents a novel coalition formation strategy with belief updating using maximum likelihood and KL divergence, ensuring stable cooperation in uncertain environments.

Findings

The proposed method converges to stable coalition structures.

The approach improves network transmission rates under uncertainty.

Theoretical analysis confirms the effectiveness of the coalition strategy.

Abstract

We study the resource sharing problem in a drone-based wireless network. We consider a distributed control setting under uncertainty (i.e. unavailability of full information). In particular, the drones cooperate in serving the users while pooling their spectrum and energy resources in the absence of prior knowledge about different system characteristics such as the amount of available power at the other drones. We cast the aforementioned problem as a Bayesian cooperative game in which the agents (drones) engage in a coalition formation process, where the goal is to maximize the overall transmission rate of the network. The drones update their beliefs using a novel technique that combines the maximum likelihood estimation with Kullback-Leibler divergence. We propose a decision-making strategy for repeated coalition formation that converges to a stable coalition structure. We analyze the performance of the proposed approach by both theoretical analysis and simulations.