Unmanned Aerial Vehicles (UAVs), have been widely studied as aerial Mobile Edge Computing (MEC) systems for resource-constrained Internet of Things (IoT) devices. Yet, battery limitations on both sides call for integrated energy harvesting alongside computation offloading. This paper studies a wireless-powered aerial MEC system, where a UAV harvests wireless energy from a terrestrial laser source, transfers wireless energy to IoT devices and serves as a computing unit to process their offloaded tasks, all at the same time. We formulate a long-term trade-off between the processed tasks and UAV energy consumption in this system by jointly optimizing system decision variables, including offloading decision, transmit power, computing resources and UAV motion trajectory. This trade-off adheres to the system constraints such as stability of data and energy queues, as well as limitations of transmit power budget, computing capacity and flight trajectory. Due to non-convex and long-term nature of this formulation, we initially leverage Lyapunov drift-plus-penalty theory, initially, to convert it into an instantaneous equivalent form. This step is subsequently followed by reformulating the problem as a Markov Decision Process (MDP), which facilitates the training of a Soft Actor-Critic (SAC) agent. To improve the adaptability of the SAC algorithm to system heterogeneity, such as differences in resource capacity, we integrate meta-learning and propose Meta Soft Actor-Critic (MSAC). As a result, the proposed agent is capable of efficiently optimizing system decision variables in real time. Numerical results demonstrate that our proposed resource allocation approach achieves its strongest gains under cloud-capacity scaling, delivering up to 26% improvement over SAC and up to 43% over TD3, representing the highest performance margins observed across all experiments.

Future wireless networks are anticipated to evolve by aerial communication platforms. Nonetheless, the operational lifespan and efficacy of transceivers such as unmanned aerial vehicle (UAVs) and Internet of Things (IoT) devices are strictly prohibited by their constrained onboard power sources. This paper focuses on an aerial network configuration where a UAV harvests solar power to serve energy-limited IoT devices through simultaneous wireless information and power transfer. In this setup, the UAV and the IoT devices, each are equipped with energy and data buffers. This system also benefits from rate splitting multiple access for efficient interference management. Upon optimizing the system efficacy, we formulate a long-term resource allocation problem to maximize the time-averaged energy efficiency. To address this stochastic and non-convex optimization problem, we propose a multi-stage solution strategy. Firstly, by leveraging Lyapunov optimization theory, we transform the long-term stochastic problem into an equivalent deterministic short-term form. Next, by recasting this equivalent problem into Markov decision process, we propose a resource allocation mechanism based on actor-critic hindsight experience replay (AC-HER), tailored to capture the problem dynamics and optimize its variables. Moreover, given the UAV high mobility and the system reconfigurations, we fortify the trained AC-HER model with meta-learning strategy, enhancing its adaptability to system variations. Simulations verified that the proposed resource allocation strategy considerably outperforms its counterparts.

Integrated into existing Mobile Edge Computing (MEC) systems, Unmanned Aerial Vehicles (UAVs) serve as a cornerstone in meeting the stringent requirements of future Internet of Things (IoT) networks. The current endeavor studies an MEC system, in which a computationally-empowered UAV, wirelessly linked to a cloud server, is destined for task offloading in uplink transmission of IoT devices. The performance of this system is studied by formulating a resource allocation problem, which aims to maximize the long-term computed task efficiency, while ensuring the stability of task buffers at the IoT devices, UAV and cloud. The problem jointly optimizes the uplink transmit power of IoT devices and their offloading decisions, the trajectory of the UAV and computing power at all transceivers. Regarding the non-convex and stochastic nature of the problem, we devise a multi-step solution approach. Initially, by invoking the fractional programming and Lyapunov theory, we transform the long-term optimization problem into an equivalent per-time-slot form. Subsequently, we recast the reformulated problem as a Markov Decision Process (MDP), which reflects the network dynamics. The MDP model, eventually, serves for training a Meta Twin Delayed Deep Deterministic Policy Gradient (MTD3) agent, in charge of adaptive resource allocation with respect to the MEC system variations derived from the mobility of the UAV and IoT devices. Simulations reveal the dominance of our proposed resource allocation approach over its Deep Reinforcement Learning (DRL)powered counterparts, increasing computed task efficiency and reducing task buffer lengths.

This paper centers around a multiple-input-multiple-output (MIMO) visible light communication (VLC) system, where an unmanned aerial vehicle (UAV) benefits from a light emitting diode (LED) array to serve photo-diode (PD)equipped users for illumination and communication simultaneously. Concerning the battery limitation of the UAV and considerable energy consumption of the LED array, a hybrid dimming control scheme is devised at the UAV that effectively controls the number of glared LEDs and thereby mitigates the overall energy consumption. To assess the performance of this system, a radio resource allocation problem is accordingly formulated for jointly optimizing the motion trajectory, transmit beamforming and LED selection at the UAV, assuming that channel state information (CSI) is partially available. By reformulating the optimization problem in Markov decision process (MDP) form, we propose a soft actor-critic (SAC) mechanism that captures the dynamics of the problem and optimizes its parameters. Additionally, regarding the high mobility of the UAV and thus remarkable rearrangement of the system, we enhance the trained SAC model by integrating a meta-learning strategy that enables more adaptation to system variations. By defining energy efficiency as a trade-off between the data rate and power consumption, simulations verify that upgrading a single-LED UAV by an array of 10 LEDs, exhibits 47 % and 34 % improvements in data rate and energy efficiency, albeit at the expense of 8 % more power consumption.

Unmanned aerial vehicles (UAVs) with multiple antennas have recently been explored to improve capacity in wireless networks. However, their strict energy constraint for simultaneously flying and communication tasks renders the exploration of energy-efficient multi-antenna techniques indispensable. Meanwhile, lens antenna subarrays (LASs) emerge as a promising energy-efficient multi-antenna structure that have not been previously harnessed for this purpose. In this paper, we propose a LAS-aided UAV to serve ground users in downlink transmission. We formulate a resource allocation problem aimed at initiating a trade-off between aggregate data rate of ground users and the power consumption of the UAV (energy efficiency) by optimizing the lens-based beamforming and flight trajectory of the UAV. To address this non-convex problem, we recast it in Markov decision process that captures its dynamic features and provides a framework to train an actor-critic agent. This agent is fine-tuned via hindsight experience replay for enhanced stabilization. As well, given the frequent mobility of the UAV, we fortify the trained agent with a meta-learning strategy, enhancing its adaptability to system variations. Numerically, more than 20% energy efficiency gain is achieved by incorporating a 4lens LAS for UAV, compared to its single-lens architecture in literature. Simulations also demonstrate that the proposed resource allocation strategy achieves significant superiority over counterparts in literature.