[Paper Review] Mobility-Aware Caching in D2D Networks

Rui Wang, Jun Zhang+2 more|arXiv (Cornell University)|Jun 16, 2016

Caching and Content Delivery44 references3 citations

TL;DR

This paper proposes a mobility-aware caching strategy in device-to-device (D2D) networks that leverages inter-contact times between users to maximize data offloading via D2D links. It formulates the caching placement as a monotone submodular maximization under a matroid constraint, enabling a greedy algorithm with a 1/2 approximation ratio, and shows via real-world traces that mobility patterns significantly influence optimal caching decisions.

ABSTRACT

Caching at mobile devices can facilitate device-to-device (D2D) communications, which may significantly improve spectrum efficiency and alleviate the heavy burden on backhaul links. However, most previous works ignored user mobility, thus having limited practical applications. In this paper, we take advantage of the user mobility pattern by the inter-contact times between different users, and propose a mobility-aware caching placement strategy to maximize the data offloading ratio, which is defined as the percentage of the requested data that can be delivered via D2D links rather than through base stations (BSs). Given the NP-hard caching placement problem, we first propose an optimal dynamic programming (DP) algorithm to obtain a performance benchmark, but with much lower complexity than exhaustive search. We will then prove that the problem falls in the category of monotone submodular maximization over a matroid constraint, and propose a time-efficient greedy algorithm, which achieves an approximation ratio as 1/2. Simulation results with real-life data-sets will validate the effectiveness of our proposed mobility-aware caching placement strategy. We observe that users moving at either a very low or very high speed should cache the most popular files, while users moving at a medium speed should cache less popular files to avoid duplication.

Motivation & Objective

• Address the limitation of prior D2D caching schemes that ignore user mobility, which reduces practical applicability.
• Maximize the data offloading ratio—defined as the fraction of requested data delivered via D2D links rather than through base stations.
• Design a caching placement strategy that exploits mobility patterns, particularly inter-contact times, to improve offloading performance.
• Formulate the caching problem as a monotone submodular function maximization under a matroid constraint to enable efficient computation.
• Provide both an optimal dynamic programming benchmark and a time-efficient greedy algorithm with theoretical approximation guarantees.

Proposed method

• Model user mobility using inter-contact times between devices to estimate contact frequency and duration, which informs caching utility.
• Formulate the caching placement problem as maximizing a monotone submodular function subject to a matroid constraint, capturing storage and contact constraints.
• Propose a dynamic programming (DP) algorithm to compute the optimal caching placement with lower complexity than exhaustive search.
• Develop a greedy algorithm that achieves a 1/2 approximation ratio to the optimal solution, ensuring near-optimal performance with low computational cost.
• Derive closed-form expressions for the marginal gain of caching a file at a device, incorporating probabilistic contact models and file transfer limits.
• Use a probabilistic model to compute the expected number of files successfully delivered via D2D links based on contact duration, transmission rate, and cache capacity.

Experimental results

Research questions

• RQ1 How does user mobility, particularly inter-contact time patterns, affect the performance of D2D caching in mobile networks?
• RQ2 Can a caching strategy that exploits mobility patterns significantly improve data offloading compared to static or random caching?
• RQ3 What is the optimal caching placement strategy that maximizes offloading ratio under realistic mobility constraints?
• RQ4 Can the caching problem be formulated as a monotone submodular maximization under a matroid constraint to enable efficient approximation algorithms?
• RQ5 How does the performance of the proposed greedy algorithm compare to the optimal solution in terms of approximation ratio and computational complexity?

Key findings

• The proposed mobility-aware caching strategy significantly improves data offloading compared to conventional caching schemes that ignore mobility.
• Users moving at very low or very high speeds should cache the most popular files, while those at medium speeds should cache less popular files to minimize redundant caching.
• The greedy algorithm achieves a theoretical approximation ratio of 1/2, providing strong performance guarantees with low computational cost.
• The dynamic programming algorithm provides a performance benchmark with much lower complexity than exhaustive search, making it practical for moderate-sized networks.
• Simulation results using real-life mobility traces confirm that mobility-aware caching outperforms random and non-mobility-aware schemes in terms of offloading gain.
• The marginal gain of caching a file at a device depends on the contact probability and transmission capacity, with gains decreasing as cache occupancy increases.