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High Altitude Platform-Based Caching and Multicasting for Rural Connectivity

Yongqiang Zhang, Mustafa A. Kishk, Mohamed-Slim Alouini

TL;DR

This paper tackles energy-efficient content delivery in rural areas by deploying caching-enabled high-altitude platforms that use FSO backhaul and RF access. It introduces a hierarchical solution that couples a PPO-based DRL caching controller with convex optimization-based per-slot resource allocation, and uses network coding to enable multicast backhaul transmissions. The framework jointly optimizes proactive caching, backhaul routing, and beamforming to minimize the long-term weighted power cost, PC(t) = P_{ m FSO}^{ m DC}(t) + ext{ω}igl(P_{ m FSO}^{ m HAP}(t) + P_{ m RF}(t)igr). Numerical results show substantial power savings over multiple baselines across varying content catalogs, user counts, data-rate demands, and channel conditions, demonstrating the practical potential for bridging the rural digital divide with scalable HAP-based caching and multicasting.

Abstract

Providing efficient and reliable content delivery in rural areas remains a significant challenge due to the lack of communication infrastructure. To bridge the digital divide, this paper investigates the potential of leveraging multiple high-altitude platforms (HAPs) for energy-efficient content delivery in wide rural regions. Each caching-enabled HAP is equipped with both Free-Space Optical (FSO) transceivers for backhaul links and Radio Frequency (RF) antenna arrays for access links. To further enhance network efficiency, we consider a network coding-based multicasting scheme, where different types of content are treated as distinct multicast sessions. With the objective of minimizing long-term power cost, we propose a hierarchical framework that integrates deep reinforcement learn-ing (DRL) and convex optimization to jointly optimize dynamic caching strategies and resource allocation across the network. Simulation results demonstrate that our approach significantly reduces power cost compared to several baseline approaches, providing a practical solution for improving rural connectivity.

High Altitude Platform-Based Caching and Multicasting for Rural Connectivity

TL;DR

This paper tackles energy-efficient content delivery in rural areas by deploying caching-enabled high-altitude platforms that use FSO backhaul and RF access. It introduces a hierarchical solution that couples a PPO-based DRL caching controller with convex optimization-based per-slot resource allocation, and uses network coding to enable multicast backhaul transmissions. The framework jointly optimizes proactive caching, backhaul routing, and beamforming to minimize the long-term weighted power cost, PC(t) = P_{ m FSO}^{ m DC}(t) + ext{ω}igl(P_{ m FSO}^{ m HAP}(t) + P_{ m RF}(t)igr). Numerical results show substantial power savings over multiple baselines across varying content catalogs, user counts, data-rate demands, and channel conditions, demonstrating the practical potential for bridging the rural digital divide with scalable HAP-based caching and multicasting.

Abstract

Providing efficient and reliable content delivery in rural areas remains a significant challenge due to the lack of communication infrastructure. To bridge the digital divide, this paper investigates the potential of leveraging multiple high-altitude platforms (HAPs) for energy-efficient content delivery in wide rural regions. Each caching-enabled HAP is equipped with both Free-Space Optical (FSO) transceivers for backhaul links and Radio Frequency (RF) antenna arrays for access links. To further enhance network efficiency, we consider a network coding-based multicasting scheme, where different types of content are treated as distinct multicast sessions. With the objective of minimizing long-term power cost, we propose a hierarchical framework that integrates deep reinforcement learn-ing (DRL) and convex optimization to jointly optimize dynamic caching strategies and resource allocation across the network. Simulation results demonstrate that our approach significantly reduces power cost compared to several baseline approaches, providing a practical solution for improving rural connectivity.
Paper Structure (20 sections, 2 theorems, 30 equations, 8 figures, 2 tables, 2 algorithms)

This paper contains 20 sections, 2 theorems, 30 equations, 8 figures, 2 tables, 2 algorithms.

Table of Contents

  1. Introduction
  2. Related Studies
  3. Contribution
  4. System Model
  5. Network Model
  6. Communication Model
  7. Problem Formulation
  8. Optimization Framework
  9. Overview of Proposed Solution
  10. DRL-Based Caching Control Strategy
  11. Power Cost Minimization in Single Time Slot
  12. Numerical Results
  13. Effect of Weighting Parameter
  14. Effect of Number of Contents
  15. Effect of Number of Users
  16. ...and 5 more sections

Key Result

Lemma 1

$\tilde{p}_{l,t}$ is jointly convex in $\gamma_{l,t}$ and $\tau_{l,t}$.

Figures (8)

  • Figure 1: System model: (a) network architecture, (b) illustration of the workflow for servicing content requests from users.
  • Figure 2: Performance of Algorithm \ref{['alg:PropAlg']} with respect to the number of learning iterations: (a) Average weighted sum of power cost, (b) Average power consumption of HAPs, (c) Average power consumption of DCs.
  • Figure 3: Power cost versus number of contents.
  • Figure 4: Power cost versus number of users.
  • Figure 5: Effect of the data Rate Requirements: (a) content caching, (b) content accessing.
  • ...and 3 more figures

Theorems & Definitions (2)

  • Lemma 1
  • Lemma 2