Fresh, Fair and Energy-Efficient Content Provision in a Private and Cache-Enabled UAV Network

TL;DR

This paper presents a novel optimization algorithm for a private, cache-enabled UAV network that enhances content freshness, fairness, and energy efficiency through joint caching, trajectory, and power control.

Contribution

It introduces a decomposition-based iterative optimization method for complex UAV content delivery problems, with theoretical analysis and practical performance improvements.

Findings

Achieves content freshness close to theoretical expectations.

Improves energy efficiency by over 22%.

Enhances fairness by over 70% compared to benchmarks.

Abstract

In this paper, we investigate a private and cache-enabled unmanned aerial vehicle (UAV) network for content provision. Aiming at delivering fresh, fair, and energy-efficient content files to terrestrial users, we formulate a joint UAV caching, UAV trajectory, and UAV transmit power optimization problem. This problem is confirmed to be a sequential decision problem with mixed-integer non-convex constraints, which is intractable directly. To this end, we propose a novel algorithm based on the techniques of subproblem decomposition and convex approximation. Particularly, we first propose to decompose the sequential decision problem into multiple repeated optimization subproblems via a Lyapunov technique. Next, an iterative optimization scheme incorporating a successive convex approximation (SCA) technique is explored to tackle the challenging mixed-integer non-convex subproblems. Besides, we analyze the convergence and computational complexity of the proposed algorithm and derive the theoretical value of the expected peak age of information (PAoI) to estimate the content freshness. Simulation results demonstrate that the proposed algorithm can achieve the expected PAoI close to the theoretical value and is more 22.11% and 70.51% energy-efficient and fairer than benchmark algorithms.