Deep Learning for Wireless Coded Caching with Unknown and Time-Variant Content Popularity

TL;DR

This paper introduces a deep learning-based approach for wireless coded caching that predicts content requests and optimizes caching policies in dynamic, high-dimensional environments, leading to improved network performance.

Contribution

It proposes a novel clustering-based LSTM prediction model combined with a supervised deep deterministic policy gradient method for adaptive caching in wireless networks.

Findings

Outperforms existing caching methods in real-world scenarios.

Effectively predicts content requests using historical data.

Achieves lower network costs through optimized caching policies.

Abstract

Coded caching is effective in leveraging the accumulated storage size in wireless networks by distributing different coded segments of each file in multiple cache nodes. This paper aims to find a wireless coded caching policy to minimize the total discounted network cost, which involves both transmission delay and cache replacement cost, using tools from deep learning. The problem is known to be challenging due to the unknown, time-variant content popularity as well as the continuous, high-dimensional action space. We first propose a clustering based long short-term memory (C-LTSM) approach to predict the number of content requests using historical request information. This approach exploits the correlation of the historical request information between different files through clustering. Based on the predicted results, we then propose a supervised deep deterministic policy gradient (SDDPG) approach. This approach, on one hand, can learn the caching policy in continuous action space by using the actor-critic architecture. On the other hand, it accelerates the learning process by pre-training the actor network based on the solution of an approximate problem that minimizes the per-slot cost. Real-world trace-based numerical results show that the proposed prediction and caching policy using deep learning outperform the considered existing methods.