Spatial multi-LRU Caching for Wireless Networks with Coverage Overlaps
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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.
Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). First posted on arXiv 24 Feb 2016.
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Citing papers per year: 2021: 1; 2022: 4; 2023: 1; 2024: 0; 2025: 1.
7 papers.
- 2022Optimistic No-regret Algorithms for Discrete Cachingcited by 10
- 2021TEDGE-Caching: Transformer-based Edge Caching Towards 6G Networkscited by 7
- 2022Online Caching with Optimistic Learningcited by 7
- 2022Online Caching with no Regret: Optimistic Learning via Recommendationscited by 4
- 2022Multi-Content Time-Series Popularity Prediction with Multiple-Model Transformers in MEC Networkscited by 4
- 2025Efficient and Optimal No-Regret Caching under Partial Observationcited by 1
- 2023CLSA: Contrastive Learning-based Survival Analysis for Popularity Prediction in MEC Networkscited by 0
- 2025Efficient and Optimal No-Regret Caching under Partial Observation4 Mar 2025
- 2023CLSA: Contrastive Learning-based Survival Analysis for Popularity Prediction in MEC Networks21 Mar 2023
- 2022Multi-Content Time-Series Popularity Prediction with Multiple-Model Transformers in MEC Networks12 Oct 2022
- 2022Optimistic No-regret Algorithms for Discrete Caching15 Aug 2022
- 2022Online Caching with no Regret: Optimistic Learning via Recommendations20 Apr 2022
- 2022Online Caching with Optimistic Learning22 Feb 2022
- 2021TEDGE-Caching: Transformer-based Edge Caching Towards 6G Networks1 Dec 2021