Next-generation wireless communication requires ultra-low latency, high data rate, and superior energy efficiency (EE). UAV-aided short-packet visible light communication (VLC) emerges as a promising paradigm to meet these requirements. This paper investigates the joint optimization of UAV trajectory, transmit power, and blocklength to maximize the EE of a UAV-aided short-packet VLC system. We establish the UAV motion and short-packet VLC transmission models, and formulate an EE maximization problem subject to practical constraints. To tackle the resulting fractional and non-convex optimization problem, we adopt the Dinkelbach iterative framework and decompose the problem into three subproblems: the trajectory optimization subproblem, power allocation subproblem, and blocklength optimization subproblem. Each subproblem is transformed into a convex form via cyclic maximization and successive convex approximation. Based on this, we propose a Dinkelbach-based iteration (DBI) algorithm and a low-complexity fixed power (FP) algorithm. Theoretical analysis shows that both algorithms are convergent and computationally efficient. Numerical results demonstrate that the proposed DBI algorithm achieves the best EE performance, while the FP algorithm obtains comparable performance with significantly reduced complexity. Both proposed algorithms consistently outperform existing benchmarks.
Jin-yuan Wang, Yuan-Yuan Li, Xin-Run Yan et al.· IEEE Transactions on Green C...· 0 citations
Unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) is a key enabler for meeting the stringent low-latency and energy-efficiency requirements of emerging low-altitude economy applications. However, achieving these objectives remains challenging due to dynamic environments, limited communication and computation resources, and the heterogeneity of network entities. This paper investigates the long-term joint optimization framework that minimizes system-wide latency and energy consumption simultaneously by coordinating UAV association, subchannel selection, uplink/downlink power allocation, and computational resource distribution. This sequential decision-making process is formulated into a partially observable Markov decision process (POMDP) to account for localized observations and dynamic channel states. To solve it, we propose a heterogeneous multi-agent proximal policy optimization (MAPPO)-based framework where both user devices (UDs) and UAVs act as heterogeneous agents. This architecture utilizes a centralized training and decentralized execution (CTDE) paradigm to enable collaborative strategies between computing requesters and providers. Numerical results demonstrate that the proposed scheme effectively navigates the high-dimensional action space and achieves superior convergence and cost reduction compared to benchmarks, including PPO, independent PPO (iPPO), Q-learning multi-agent extension (QMIX), value decomposition networks (VDN), independent deep Q-network (iDQN), and genetic algorithm (GA).
Ming Cheng, Canlin Zhu, Jianghang Tang et al.· Journal of King Saud Univers...· 0 citations