Open Access

ARTICLE

A PPO-Based DRL Approach for Scalable Communication in Civilian UAV Networks

Chu Thi Minh Hue¹, Nguyen Minh Quy^2,*

1 Faculty of Software Engineering, FPT University, Hanoi, Vietnam
2 Faculty of Information Technology, Hung Yen University of Technology and Education, Hungyen, Vietnam

* Corresponding Author: Nguyen Minh Quy. Email:

(This article belongs to the Special Issue: AI-Driven Next-Generation Networks: Innovations, Challenges, and Applications)

Computers, Materials & Continua 2026, 87(2), 79 https://doi.org/10.32604/cmc.2026.074398

Received 10 October 2025; Accepted 19 January 2026; Issue published 12 March 2026

Abstract

Nowadays, Unmanned Aerial Vehicles (UAVs) are making increasingly important contributions to numerous applications that enhance human quality of life, such as sensing and data collection, computing, and communication. However, communication between UAVs still faces challenges due to high-dynamic topology, volatile wireless links, and strict energy budgets. In this work, we introduce an improved communication scheme, namely Proximal Policy Optimization (PPO). Our solution casts hop–by–hop relay selection as a Markov decision process and develops a decentralized Proximal Policy Optimization framework in an actor–critic form. A key novelty is the design of the reward function, which jointly considers the delivery ratio, end-to-end delay, and energy efficiency, enabling flexible prioritization in dynamic environments. The simulation results across swarms of 20–70 UAVs show that, the proposed framework enhances delivery ratio to 5% over a Deep Q-Network baseline (reaching ≈80% at 70 nodes), reduces latency by about 2–3 ms in medium-to-dense settings (from ∼43 to 35–36 ms), and attains comparable or slightly lower total energy consumption (typically 0.5%–2% lower). The results indicate that the proposed communication scheme, adaptive and scalable learning-based UAV scenarios, pave the way for re-world UAV deployments.

---

Keywords

---