Chuan-Chi Lai

Chuan-Chi Lai

Unmanned Aerial Vehicle (UAV) mounted Base Stations (UAV-BSs) provide flexible coverage for temporary hotspot scenarios; however, efficiently optimizing 3D deployment to satisfy heterogeneous user distributions remains a significant challenge. While Deep Reinforcement Learning (DRL) approaches have shown promise, they often suffer from prohibitive training overhead and poor generalization in cold-start scenarios where the user topology is unknown a priori. To address these limitations, this paper proposes Satisfaction-driven Coverage Optimization via Perimeter Extraction (SCOPE), which is a deterministic and training-free 3D deployment framework. Unlike existing heuristics that rely on fixed-altitude assumptions, SCOPE integrates a perimeter-based peeling strategy with the Welzl Smallest Enclosing Circle (SEC) algorithm to dynamically optimize 3D positions. Theoretically, we provide a rigorous convergence proof and derive a polynomial time complexity of $O(N^2 \log N)$, ensuring predictable execution for real-time applications. Experimentally, we evaluate SCOPE in unpredictable hotspot environments against both traditional heuristics and state-of-the-art DRL baselines under a matched hardware budget. Simulation results demonstrate that SCOPE maintains a high user satisfaction rate between 82% and 88% while generating solutions within millisecond-level latency on commodity hardware. Furthermore, SCOPE demonstrates exceptional resilience by maintaining an approximate 40% functional coverage rate at a minimum altitude constraint of 60 m; in this challenging regime, baseline methods suffer a significant performance degradation, dropping to approximately 20% due to altitude-induced path loss. These findings validate SCOPE as a robust and agile solution for establishing instantaneous digital lifelines in zero-day disaster response missions.

Chuan-Chi Lai, Ang-Hsun Tsai, Zhu Han

The rapid expansion of the Low-Altitude Economy (LAE) necessitates highly reliable coordination among autonomous aerial agents (AAAs). Traditional reactive communication paradigms in 6G networks are increasingly susceptible to stochastic network jitter and intermittent signaling silence, especially within complex urban canyon environments. To address this connectivity gap, this paper introduces the Embodied Proactive Inference for Coordination (EPIC) framework, featuring a Spatio-Temporal Semantic Inference (STSI) operator designed to decouple the coordination loop from physical signaling fluctuations. By projecting stale peer observations into a proactive belief manifold, EPIC maintains a deterministic reaction latency regardless of the network state. Extensive simulations demonstrate that EPIC achieves an average 93.5% reduction in end-to-end reaction latency, masking physical transmission delays of 150 ms with a deterministic 10 ms execution heartbeat. Crucially, EPIC exhibits strategic immunity to escalating network jitter up to 100 ms and improves the Weighted Coverage Efficiency (WCE) by 10.5% during extreme signaling silence lasting up to 50 s. These results provide the deterministic resilience essential for 6G Hyper-Reliable and Low-Latency Communication (HRLLC).

Chuan-Chi Lai

This paper addresses catastrophic forgetting in mobile edge UAV networks within dynamic spatiotemporal environments. Conventional deep reinforcement learning often fails during task transitions, necessitating costly retraining to adapt to new user distributions. We propose the spatiotemporal continual learning (STCL) framework, realized through the group-decoupled multi-agent proximal policy optimization (G-MAPPO) algorithm. The core innovation lies in the integration of a group-decoupled policy optimization (GDPO) mechanism with a gradient orthogonalization layer to balance heterogeneous objectives including energy efficiency, user fairness, and coverage. This combination employs dynamic z-score normalization and gradient projection to mitigate conflicts without offline resets. Furthermore, 3D UAV mobility serves as a spatial compensation layer to manage extreme density shifts. Simulations demonstrate that the STCL framework ensures resilience, with service reliability recovering to over 0.9 for moderate loads of up to 100 users. Even under extreme saturation with 140 users, G-MAPPO maintains a significant performance lead over the multi-agent deep deterministic policy gradient (MADDPG) baseline by preventing policy stagnation. The algorithm delivers an effective capacity gain of 20 percent under high traffic loads, validating its potential for scalable aerial edge swarms.

Chuan-Chi Lai

Ensuring continuous service coverage under unexpected hardware failures is a fundamental challenge for 3D Aerial-Ground Integrated Networks. Although Multi-Agent Reinforcement Learning facilitates autonomous coordination, traditional architectures often lack resilience to sudden topology deformations. This paper proposes the Topology-Aware Graph MAPPO (TAG-MAPPO) framework to enhance system survivability through autonomous 3D spatial reconfiguration. Our framework integrates graph-based feature aggregation with a residual ego-state fusion mechanism to capture intricate inter-agent dependencies. To achieve structural robustness, we introduce a Random Observation Shuffling mechanism that fosters strong generalization to agent population fluctuations by breaking coordinate-index dependencies. Extensive simulations across heterogeneous environments, including high-speed mobility at 15 meters per second, demonstrate that TAG-MAPPO significantly outperforms Multi-Layer Perceptron baselines. Specifically, the framework reduces redundant handoffs by up to 50 percent while maintaining superior energy efficiency. Most notably, TAG-MAPPO exhibits exceptional self-healing capabilities, restoring over 90 percent of pre-failure coverage within 15 time steps. In dense urban scenarios, the framework achieves a post-failure fairness index surpassing its original four-UAV configuration by autonomously resolving service overlaps and interference. These findings confirm that topology-aware coordination is essential for resilient 6G aerial networks.

Haoran Peng, Chao Chen, Chuan-Chi Lai et al.

The unmanned aerial vehicles base stations (UAV-BSs) have great potential in being widely used in many dynamic application scenarios. In those scenarios, the movements of served user equipments (UEs) are inevitable, so the UAV-BSs needs to be re-positioned dynamically for providing seamless services. In this paper, we propose a system framework consisting of UEs clustering, UAV-BS placement, UEs trajectories prediction, and UAV-BS reposition matching scheme, to serve the UEs seamlessly as well as minimize the energy cost of UAV-BSs' reposition trajectories. An Echo State Network (ESN) based algorithm for predicting the future trajectories of UEs and a Kuhn-Munkres-based algorithm for finding the energy-efficient reposition trajectories of UAV-BSs is designed, respectively. We conduct a simulation using a real open dataset for performance validation. The simulation results indicate that the proposed framework achieves high prediction accuracy and provides the energy-efficient matching scheme.

Li-Chun Wang, Chuan-Chi Lai, Hong-Han Shuai et al.

Future mobile communication networks require an Aerial Base Station (ABS) with fast mobility and long-term hovering capabilities. At present, unmanned aerial vehicles (UAV) or drones do not have long flight times and are mainly used for monitoring, surveillance, and image post-processing. On the other hand, the traditional airship is too large and not easy to take off and land. Therefore, we propose to develop an "Artificial Intelligence (AI) Drone-Cruiser" base station that can help 5G mobile communication systems and beyond quickly recover the network after a disaster and handle the instant communications by the flash crowd. The drone-cruiser base station can overcome the communications problem for three types of flash crowds, such as in stadiums, parades, and large plaza so that an appropriate number of aerial base stations can be accurately deployed to meet large and dynamic traffic demands. Artificial intelligence can solve these problems by analyzing the collected data, and then adjust the system parameters in the framework of Self-Organizing Network (SON) to achieve the goals of self-configuration, self-optimization, and self-healing. With the help of AI technologies, 5G networks can become more intelligent. This paper aims to provide a new type of service, On-Demand Aerial Base Station as a Service. This work needs to overcome the following five technical challenges: innovative design of drone-cruisers for the long-time hovering, crowd estimation and prediction, rapid 3D wireless channel learning and modeling, 3D placement of aerial base stations and the integration of WiFi front-haul and millimeter wave/WiGig back-haul networks.