Placement Delivery Array Design via Attention-Based Deep Neural Network

TL;DR

This paper introduces an attention-based neural network model that learns to construct placement delivery arrays for coded caching, offering a flexible and efficient alternative to traditional optimization methods.

Contribution

It presents a novel neural architecture using attention and reinforcement learning to generate PDAs, handling variable sizes without complex optimization.

Findings

Effective implementation of coded caching demonstrated

Low complexity in PDA construction

Outperforms traditional optimization approaches

Abstract

A decentralized coded caching scheme has been proposed by Maddah-Ali and Niesen, and has been shown to alleviate the load of networks. Recently, placement delivery array (PDA) was proposed to characterize the coded caching scheme. In this paper, a neural architecture is first proposed to learn the construction of PDAs. Our model solves the problem of variable size PDAs using mechanism of neural attention and reinforcement learning. It differs from the previous attempts in that, instead of using combined optimization algorithms to get PDAs, it uses sequence-to-sequence model to learn construct PDAs. Numerical results are given to demonstrate that the proposed method can effectively implement coded caching. We also show that the complexity of our method to construct PDAs is low.