# Joint Coverage and Power Control in Highly Dynamic and Massive UAV Networks: An Aggregative Game-theoretic Learning Approach

## TL;DR

This paper introduces an aggregative game-theoretic learning approach with a novel algorithm for UAV networks, improving efficiency and speed in highly dynamic, large-scale post-disaster scenarios.

## Contribution

It develops a new aggregative game model and proposes the SPBLLA algorithm, enhancing learning speed and reducing information exchange in UAV network management.

## Key findings

- SPBLLA outperforms revised BLLA in learning speed.
- The model effectively handles large-scale, dynamic UAV networks.
- The approach reduces communication overhead and improves deployment efficiency.

## Abstract

Unmanned aerial vehicles (UAV) ad-hoc network is a significant contingency plan for communication after a natural disaster, such as typhoon and earthquake. To achieve efficient and rapid networks deployment, we employ noncooperative game theory and amended binary log-linear algorithm (BLLA) seeking for the Nash equilibrium which achieves the optimal network performance. We not only take channel overlap and power control into account but also consider coverage and the complexity of interference. However, extensive UAV game theoretical models show limitations in post-disaster scenarios which require large-scale UAV network deployments. Besides, the highly dynamic post-disaster scenarios cause strategies updating constraint and strategy-deciding error on UAV ad-hoc networks. To handle these problems, we employ aggregative game which could capture and cover those characteristics. Moreover, we propose a novel synchronous payoff-based binary log-linear learning algorithm (SPBLLA) to lessen information exchange and reduce time consumption. Ultimately, the experiments indicate that, under the same strategy-deciding error rate, SPBLLA's learning rate is manifestly faster than that of the revised BLLA. Hence, the new model and algorithm are more suitable and promising for large-scale highly dynamic scenarios.