Low-Rank Matrix Completion for Mobile Edge Caching in Fog-RAN via Riemannian Optimization

TL;DR

This paper introduces a Riemannian optimization-based low-rank matrix completion method to enhance content delivery in Fog-RAN, reducing latency and improving convergence speed compared to existing algorithms.

Contribution

It develops a novel Riemannian trust-region approach leveraging quotient manifold geometry for efficient low-rank matrix completion in Fog-RAN caching.

Findings

Faster convergence rate than existing algorithms

Achieves optimal content delivery results

Outperforms state-of-the-art methods in simulations

Abstract

The upcoming big data era is likely to demand tremendous computation and storage resources for communications. By pushing computation and storage to network edges, fog radio access networks (Fog-RAN) can effectively increase network throughput and reduce transmission latency. Furthermore, we can exploit the benefits of cache enabled architecture in Fog-RAN to deliver contents with low latency. Radio access units (RAUs) need content delivery from fog servers through wireline links whereas multiple mobile devices acquire contents from RAUs wirelessly. This work proposes a unified low-rank matrix completion (LRMC) approach to solving the content delivery problem in both wireline and wireless parts of Fog-RAN. To attain a low caching latency, we present a high precision approach with Riemannian trust-region method to solve the challenging LRMC problem by exploiting the quotient manifold geometry of fixed-rank matrices. Numerical results show that the new approach has a faster convergence rate, is able to achieve optimal results, and outperforms other state-of-art algorithms.