# Spatial multi-LRU Caching for Wireless Networks with Coverage Overlaps

arXiv: https://arxiv.org/abs/1602.07623 · HTML: https://mlsanity.com/paper/1602.07623/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Feb 2016.

## Built on by (most cited first, 7 of 7)

- [Optimistic No-regret Algorithms for Discrete Caching](https://mlsanity.com/paper/2208.06414/) (2022)
- [TEDGE-Caching: Transformer-based Edge Caching Towards 6G Networks](https://mlsanity.com/paper/2112.00633/) (2021)
- [Online Caching with Optimistic Learning](https://mlsanity.com/paper/2202.10590/) (2022)
- [Online Caching with no Regret: Optimistic Learning via Recommendations](https://mlsanity.com/paper/2204.09345/) (2022)
- [Multi-Content Time-Series Popularity Prediction with Multiple-Model Transformers in MEC Networks](https://mlsanity.com/paper/2210.05874/) (2022)
- [Efficient and Optimal No-Regret Caching under Partial Observation](https://mlsanity.com/paper/2503.02758/) (2025)
- [CLSA: Contrastive Learning-based Survival Analysis for Popularity Prediction in MEC Networks](https://mlsanity.com/paper/2303.12097/) (2023)

## Abstract (arXiv)

This article introduces a novel family of decentralised caching policies, applicable to wireless networks with finite storage at the edge-nodes (stations). These policies are based on the Least-Recently-Used replacement principle, and are, here, referred to as spatial multi-LRU. Based on these, cache inventories are updated in a way that provides content diversity to users who are covered by, and thus have access to, more than one station. Two variations are proposed, namely the multi-LRU-One and -All, which differ in the number of replicas inserted in the involved caches. By introducing spatial approximations, we propose a Che-like method to predict the hit probability, which gives very accurate results under the Independent Reference Model (IRM). It is shown that the performance of multi-LRU increases the more the multi-coverage areas increase, and it approaches the performance of other proposed centralised policies, when multi-coverage is sufficient. For IRM traffic multi-LRU-One outperforms multi-LRU-All, whereas when the traffic exhibits temporal locality the -All variation can perform better.