[2024 - 2026]

@article{Zhou2025b,

title = {A Feature-Aware Elite-Imitation MARL for Multi-UAV Trajectory Optimization in Mountain Terrain Detection},

author = {Quanxi Zhou and Ye Tao and Qianxiao Su and Manabu Tsukada},

url = {https://www.mdpi.com/2504-446X/9/9/645/pdf},

doi = {doi.org/10.3390/drones9090645},

year  = {2025},

date = {2025-09-13},

urldate = {2025-09-13},

journal = {Drones},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Zhou2025,

title = {Uncertainty-Aware Multi-Agent Reinforcement Learning for Anti-Interference Trajectory Planning of Cellular-Connected UAVs},

author = {Quanxi Zhou and Wencan Mao and Jin Nakazato and Yusheng Ji and Manabu Tsukada},

doi = {10.1109/TVT.2025.3606201},

isbn = {0018-9545},

year  = {2025},

date = {2025-09-04},

urldate = {2025-09-09},

journal = {IEEE Transactions on Vehicular Technology},

pages = {1 - 17},

abstract = {Cellular-connected unmanned aerial vehicles (C-UAVs) will be an integral component of future wireless networks. Thanks to the mobility and maneuverability of UAVs, we can transform the interference management and route scheduling problems of C-UAVs into an anti-interference trajectory planning problem, aiming to jointly minimize the UAV mission time and transmission outage time. However, none of the existing methods have taken both the spatio-temporal uncertainty of interference sources and multi-UAV trajectory planning into consideration. To address this issue, we propose a novel method, referred to as uncertainty-aware multi-agent reinforcement learning (UA-MARL), for anti-interference trajectory planning of C-UAVs. In UA-MARL, a transmission outage probability (TOP) has been introduced to improve the robustness of the model. A transmission outage probability experience memory (TOPEM) has been designed to increase sample efficiency and reduce inference time. MARL algorithms integrated with an adaptive post-decision state (PDS) have been introduced to accelerate the convergence and stabilize the training. Experimental results show that UA-MARL outperforms baselines in average reward, convergence efficiency, and convergence stability. Furthermore, we find that higher residential density and wider considered area will lead to a decrease in training efficiency and stability.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Liu2025,

title = {Multi-Modal Trajectory Planning for Emergency-Oriented Air-Ground Collaborative Sensing and Communication},

author = {Yaxi Liu and Quanxi Zhou and Wencan Mao and Xulong Li and Wei Huangfu and Manabu Tsukada and Yusheng Ji and Keping Long},

doi = {10.1109/TCCN.2025.3585254},

issn = {2332-7731},

year  = {2025},

date = {2025-07-04},

urldate = {2025-07-04},

journal = {IEEE Transactions on Cognitive Communications and Networking},

volume = {11},

issue = {5},

pages = {3094-3111},

abstract = {To obtain real-time situational awareness of the world, air-ground collaborative sensing and communication provide a promising solution to form a pervasive cognitive communications and networking system. However, existing schemes struggle to cope with emergencies where ground base stations and Internet of Things devices are temporarily out-of-service. Motivated by this, we envision a novel emergency-oriented air-ground collaborative sensing and communication network where multi-modal cognitive entities (i.e., static/dynamic ground/aerial nodes) cooperatively collect data from IoT devices and simultaneously perform sensing functionality. In such a novel network, an optimization for joint trajectory planning and resource allocation is established to minimize both data transmission task delay and sensing task delay under the constraints of boundary, moving distance, accessible region, and energy consumption for network nodes. To tackle the problem, we propose a transfer learning-based deep reinforcement learning (DRL) framework where three advanced DRL algorithms are included. Such a framework can rapidly adapt to potentially updated environments by facilitating knowledge transfer across tasks for emergency rescue activities. The proposed framework outperforms three state-of-the-art baselines. Moreover, the newly introduced auxiliary cognitive entities facilitate the improvement of sensing and communication functionalities, and the proposed transfer learning-based scheme boosts convergence in fast-changing environments.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Zhou2024,

title = {Cellular Connected UAV Anti-Interference Path Planning Based on PDS-DDPG and TOPEM},

author = {Quanxi Zhou and Yongjing Wang and Ruiyu Shen and Jin Nakazato and Manabu Tsukada and Zhenyu Guan},

doi = {10.1109/JMASS.2024.3490762},

issn = {2576-3164},

year  = {2024},

date = {2024-11-04},

urldate = {2024-11-04},

journal = {IEEE Journal on Miniaturization for Air and Space Systems},

abstract = {Due to the randomness of channel fading, communication devices, and malicious interference sources, unmanned aerial vehicles (UAVs) face a complex and ever-changing task scenario, which poses significant communication security challenges, such as transmission outages. Fortunately, these communication security challenges can be transformed into path planning problems that minimize the weighted sum of UAV mission time and transmission outage time. In order to design the complex communication environment faced by UAVs in actual scenarios, we propose a system model, including building distribution, communication channel, and antenna design in this paper. Besides, we introduce other UAVs with fixed flight paths and ground interference resources with random locations to ensure mission UAVs have better anti-interference ability. However, it is challenging for classical search algorithms and heuristic algorithms to cope with the complex path problems mentioned above. In this paper, we propose an improved deep deterministic policy gradient (DDPG) algorithm with better performance compared with basic DDPG and DDQN algorithms. Specifically, a post-decision state (PDS) mechanism has been introduced to accelerate the convergence rate and enhance the stability of the training process. In addition, a transmission outage probability experience memory (TOPEM) has been designed to quickly generate wireless communication quality maps and provide temporary experience for the post-decision process, resulting in better training results. Simulation experiments have proven that, compared to basic DDPG, the improved algorithm increases training speed by at least 50%, significantly improves convergence rate, and reduces the episode required for convergence to 20%. It can also help UAVs choose better paths than basic DDPG and DDQN algorithms.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}