Article · IEEE Communications Letters · September 16, 2019
Multi-Armed Bandit Learning for Cache Content Placement in Vehicular Social Networks
Saeid Akhavan Bitaghsir, Aresh Dadlani, Mohammad Borhani, Ahmad Khonsari
Abstract
In this letter, the efficient dissemination of content in a socially-aware cache-enabled hybrid network using multi-armed bandit learning theory is analyzed. Specifically, an overlay cellular network over a vehicular social network is considered, where commuters request for multimedia content from either the stationary road-side units (RSUs), the base station, or the single mobile cache unit (MCU), if accessible. Firstly, we propose an algorithm to optimally distribute popular contents among the locally deployed RSU caches. To further maximize the cache hits experienced by vehicles, we then present an algorithm to find the best traversal path for the MCU based on commuters' social degree distribution. For performance evaluation, the asymptotic regret upper bounds of the two algorithms are also derived. Simulations reveal that the proposed algorithms outperform existing content placement methods in terms of overall network throughput.
More by Aresh Dadlani
- Joint Freshness and Age-Dispersion Control over Finite-State Markov Wireless ChannelsarXiv · 2026
- Dynamic Link Prediction with Temporally Enhanced Signed Graph Neural NetworksarXiv · 2026
- DeepIrrigo: Deep Reinforcement Learning for Continuous and Discrete Action Irrigation Optimization--A Case Study in IranSSRN Electronic Journal · 2026
- Age Performance Analysis in Resource-Constrained Adversarial IoT SystemsarXiv · 2025