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Theoretical Limits of Language Model Alignment $f$-Divergence Regularized RLHF: Two Tales of Sampling and Unified Analyses A Unified Measure-Theoretic View of Diffusion, Score-Based, and Flow Matching Generative Models When Can Voting Help, Hurt, or Change Course? Exact Structure of Binary Test-Time Aggregation When Semantic Communication Meets Queueing: Cross-Layer Latency and Task Fidelity Optimization Convexity in Disguise: A Theoretical Framework for Nonconvex Low-Rank Matrix Estimation Conditional Diffusion Under Linear Constraints: Langevin Mixing and Information-Theoretic Guarantees Sharp Capacity Thresholds in Linear Associative Memory: From Winner-Take-All to Listwise Retrieval Expert Routing for Communication-Efficient MoE via Finite Expert Banks Contextual Memory-Enhanced Source Coding for Low-SNR Communications Realizable Bayes-Consistency for General Metric Losses Leveraging Code Automorphisms for Improved Syndrome-Based Neural Decoding A Hierarchical Sampling Framework for bounding the Generalization Error of Federated Learning Dueling DDQN-Based Adaptive Multi-Objective Handover Optimization for LEO Satellite Networks The Causal Description Gap: Information-Theoretic Separations Across Pearl's Hierarchy Optimization of CV-QKD Under Practical Constraints Benchmarking Wireless Representations: High-Dimensional vs. Compressed Embeddings for Efficiency and Robustness Real-Time Text Transmission via LLM-Based Entropy Coding over Fixed-Rate Channels SwiftChannel: Algorithm-Hardware Co-Design for Deep Learning-Based 5G Channel Estimation Evolving Token Communication with Parametric Memory Network Remote Action Generation: Remote Control with Minimal Communication The (Marginal) Value of a Search Ad: An Online Causal Framework for Repeated Second-price Auctions Stabilizing Private LASSO under Heterogeneous Covariates via Anisotropic Objective Perturbation Linear-Readout Floors and Threshold Recovery in Computation in Superposition Soft Graph Diffusion Transformer for MIMO Detection Hierarchical Federated Learning for Networked AI: From Communication Saving to Architecture-Aware Design Exponential families from a single KL identity MIFair: A Mutual-Information Framework for Intersectionality and Multiclass Fairness Diffusion-OAMP for Joint Image Compression and Wireless Transmission Decoupled Descent: Exact Test Error Tracking Via Approximate Message Passing
Minimum Throughput Maximization in UAV-Aided Wireless Pow...
Junhee Park, Hoon Lee, Subin Eom, Inkyu Lee · 2018-01-09 · via cs.IT updates on arXiv.org

This paper investigates unmanned aerial vehicle (UAV)-aided wireless powered communication network (WPCN) systems where a mobile access point (AP) at the UAV serves multiple energy-constrained ground terminals (GTs). Specifically, the UAVs first charge the GTs by transmitting the wireless energy transfer (WET) signals in the downlink. Then, by utilizing the harvested wireless energy from the UAVs, the GTs send their uplink wireless information transmission (WIT) signals to the UAVs. In this paper, depending on the operations of the UAVs, we adopt two different scenarios, namely integrated UAV and separated UAV WPCNs. First, in the integrated UAV WPCN, a UAV acts as a hybrid AP in which both energy transfer and information reception are processed at a single UAV. In contrast, for the separated UAV WPCN, we consider two UAVs each of which behaves as an energy AP and an information AP independently, and thus the energy transfer and the information decoding are separately performed at two different UAVs. For both systems, we jointly optimize the trajectories of the UAVs, the uplink power control, and the time resource allocation for the WET and the WIT to maximize the minimum throughput of the GTs. Since the formulated problems are non-convex, we apply the concave-convex procedure by deriving appropriate convex bounds for non-convex constraints. As a result, we propose iterative algorithms which efficiently identify a local optimal solution for the minimum throughput maximization problems. Simulation results verify the efficiency of the proposed algorithms compared to conventional schemes.