Energy-Aware Scheduling and Beamforming for Simultaneous Wireless Information and Power Transfer in Low-Earth-Orbit Satellite and UAV Networks Using Lyapunov Optimization, Successive Convex Approximation, and WMMSE

Sensing for Reliable UAV Communication: Robust Trajectory and Resource Optimization in Low-Altitude Networks

Simulation results validate the effectiveness of the proposed OP approximation, demonstrate the significant outage capacity improvement of the proposed robust optimization schemes over benchmark schemes, and illustrate the superiority of the IWS-based scheme over the CE-based scheme.

Multi-UAV Collaborative Energy Charging for Battery-Free SWIPT-Enabled Sensor Networks Based on MADDPG

: The emergence of Unmanned Aerial Vehicle (UAV)-enabled Wireless Energy Transfer (WET) and Simultaneous Wireless Information and Power Transfer (SWIPT) technology provide a promising solution to overcome the energy sustainability limitations of traditional harvesting-reliant sensor networks. However, in large-scale Battery-free SWIPT-enabled Sensor Networks (BSSN) characterized by sparse node distribution and heterogeneous energy consumption and harvesting rates, employing a single UAV for energy replenishment often suffers from insufficient operation continuity and low charging efficiency. To overcome these challenges, a Multi-UAV Collaborative Energy Charging for BSSN Based on Multi-Agent Deep Deterministic Policy Gradient (MCEC-MADDPG) is proposed in this paper. Specifically, we construct a collaborative one-to-one precision energy supply model where UAVs hover directly above specific nodes to achieve power transmission without complex beamforming requirements. To achieve collaborative scheduling among multiple UAVs in wide-area dynamic environments, the energy replenishment problem is first formulated as a Partially Observable Markov Decision Process (POMDP). Subsequently, the Centralized Training with Decentralized Execution (CTDE) architecture of the MADDPG algorithm is leveraged to solve this POMDP, which effectively tackles the non-stationarity challenge inherent in multi-agent environments. Simulation results demonstrate that MCEC-MADDPG exhibits superior performance in terms of convergence speed and stability. It enables the adaptive emergence of spatial-division collaborative strategies, significantly enhances the average residual energy of the network, and elevates the node survival rate to nearly 90%. Compared with Deep Deterministic Policy Gradient (DDPG), the traditional static Partition-Greedy method, the heuristic K-Means algorithm and the dynamic Two-Layer task allocation strategy, the proposed approach demonstrates substantial advantages.

Joint Beamforming and Trajectory Optimization Algorithm for RSMA-UAV-Enabled Integrated Sensing and Communication System

An unmanned aerial vehicle (UAV)-enabled ISAC system employing rate-splitting multiple access (RSMA) and a joint beamforming and trajectory optimization framework is investigated and results demonstrate that the proposed algorithm significantly improves the achievable system downlink rate.

Multi-UAV Energy Consumption Minimization for Multilayer Aerial Wireless-Powered MEC: An Online Stochastic Optimization Approach

Joint Trajectory, Power, and Blocklength Optimization for Energy-Efficient UAV-Aided Short-Packet Visible Light Communications

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.

Joint Load Balancing and Transmit Power Control for Energy Efficiency Maximization in the Satellite-Cell-Free Massive MIMO Uplink

The seamless integration of non-terrestrial and terrestrial infrastructures is a key enabler for ubiquitous connectivity in next-generation (NG) wireless networks. We investigate a hybrid satellite-cell-free Massive MIMO system, where multiple low-Earth-orbit (LEO) satellites jointly serve users in unison with terrestrial access points (APs) under realistic imperfect channel state information and practical user association constraints. We first derive closed-form expressions of the uplink ergodic throughput by exploiting maximum ratio combining (MRC) for transmission over spatially correlated Rician fading channels. Our analysis reveals the characteristic impact of both user-satellite and user-AP association patterns on both the spectral efficiency and rate-fairness achieved. We then formulate an energy efficiency optimization problem under joint user association and power control. Since the problems are inherently NP-hard due to the binary nature of the user-association variables, we develop an improved Differential Evolution (IDE) framework that efficiently explores the feasible solutions in polynomial time. Numerical results validate our analysis and show that the proposed hybrid scheme substantially improves energy efficiency and network throughput. For large-scale scenarios, the DE framework provides practical user-satellite-AP association guidelines, enabling scalable performance gains.