Dynamic Caching Content Replacement in Base Station Assisted Wireless D2D Caching Networks

Lee, Ming‐Chun; Feng, Hao; Molisch, Andreas F.

doi:10.1109/access.2020.2973953

Cited by 22 publications

(16 citation statements)

References 44 publications

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“…We select an active user and 11 inactive users from the CiaoDVD dataset randomly, which concludes 72665 users, then we analyze their social relationship. It is easy to get the trust value of an active user and eleven inactive users by trust topology network in (4). As shown in Figure 3, the value range of similarity and trust is between 0 and 1.…”

Section: Simulation Resultsmentioning

confidence: 99%

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Social‐Aware Caching Strategy Based on Joint Action Deep Reinforcement Learning

Jing

Song

et al. 2021

Wireless Communications and Mobile Computing

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Caching in device-to-device (D2D) networks is emerging a promising trend, which enables to reduce backhaul traffic. Moreover, social interaction among users influences the performance of overall system network. Therefore, it is crucial to consider social attributes in the D2D networks to develop a caching strategy to resolve the problem of unbalanced content distributed. In this paper, we consider two types of users according to their activeness, i.e., active users and inactive users. Inactive users assist active users cache contents during off-peak periods and provide the contents to the active users during peak periods to relieve the pressure of base station (BS). In addition, caching system model is divided into physical domain model and social domain model. In physical domain, the quality of communication links is judged by the delay between D2D users. In social domain, based on a real-world dataset, CiaoDVD, we calculate user similarity in three dimensions and obtain user trust by a trust topology to measure user relationships. Finally, in order to maximize the cache hit ratio, a joint action deep Q -networks (JADQN) framework is proposed to pair the active users with inactive users and distribute the contents to inactive users. Simulation results indicate that the proposed strategy improves the cache hit ratio by 42.9% and reduces the download delay by 48.8% compared with least frequency used (LFU) algorithm, which validates the effectiveness of our method.

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Section: Simulation Resultsmentioning

confidence: 99%

“…Device-to-device (D2D) communication is recognized as a key technology which can establish communication links without passing through cellular infrastructure [4]. By this way, there are plenty of advantages including high data rates, short delays, and low power consumption [5].…”

Section: Introductionmentioning

confidence: 99%

Social‐Aware Caching Strategy Based on Joint Action Deep Reinforcement Learning

Jing

Song

et al. 2021

Wireless Communications and Mobile Computing

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“…The approach followed by some of the stateof-the-art works in wireless networks is learning-based cache replacement. Generally, a Q-learning algorithm can be applied by a UE to act as an independent or joint learner to learn the reward/Q-value of caching/replacement decisions [116], [52], [117]. On the other hand, some research works have not considered the sub-problem of cache replacement [45] while some have considered a caching decision problem for instance t+1 [118].…”

Section: E Cache Replacementmentioning

confidence: 99%

Applying Machine Learning Techniques for Caching in Edge Networks: A Comprehensive Survey

Shuja,

Bilal,

Alasmary

et al. 2020

Preprint

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Edge networks provide access to a group of proximate users who may have similar content interests. Caching popular content at the edge networks leads to lower latencies while reducing the load on backhaul and core networks with the emergence of high-speed 5G networks. Edge networks are complex and dynamic. User mobility, preferences, and content popularity are the dominant dynamic features of the edge networks. Temporal and social features of content, such as the number of views and likes are applied to estimate the popularity of content from a global perspective. However, such estimates may not be mapped to an edge network with particular social and geographic characteristics. In edge networks, machine learning techniques can be applied to predict content popularity based on user preferences, user mobility based on user location history, cluster users based on similar content interests, and optimize cache placement and replacement strategies provided a set of constraints and predictions about the state of the network. These applications of machine learning can help identify relevant content for an edge network to lower latencies and increase cache hits. This article surveys the application of machine learning techniques for caching content in edge networks. We survey recent state-of-the-art literature and formulate a comprehensive taxonomy based on (a) machine learning technique (application and objective), (b) caching strategy (policy, location, and objective), and edge network (type and delivery strategy). We further survey supporting concepts for optimal edge caching decisions that require the application of machine learning. These supporting concepts are social-awareness, popularity prediction, and community detection in edge networks. A comparative analysis of the state-of-the-art literature is presented with respect to the parameters identified in the taxonomy. Moreover, we debate research challenges and future directions for optimal caching decisions and the application of machine learning towards caching in edge networks.

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“…In D2D communication, [3] proposed an optimization algorithm to maximize the cache hit probability and cache-aided throughput by considering the combined impact of various cache memory sizes of UEs and content sizes. [4] proposed a practical and feasible replacement architecture for base station (BS) assisted wireless D2D caching networks by exploiting the broadcasting of the BS. [6] formulated two key problems to enable collaborative content distribution across domains by considering an integrated content distribution model with universal in-network caching in an end-to-end scope.…”

Section: A Traditional Network Cachingmentioning

confidence: 99%

Constructing Effective Caches of Shortest Path Queries on Road Networks

Wang

Qiu

et al. 2020

IEEE Access

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How to effectively utilize caching technology to support high-performance shortest path queries on road networks has become an important research problem since the popularization of locationbased services. Most of the existing methods design a statistical model to construct the cache with the shortest paths, and mainly consider the related query frequency obtained from the historical query logs. As a result, the paths in the cache may be concentrated in a few hot-spot areas of the road network, such as the prosperous areas of a city. Additionally, queries from remote areas may never be hit in the cache, which results in the cache hit ratio is not fully optimized. To address this problem, we define an effective model to quantify the effect of cached shortest paths; the model not only considers the related query frequency in the log but also the coverage of the paths on the road network. Then, we propose an efficient cache construction method to select the cached shortest paths from the query log. Furthermore, we design a hybrid index for the shortest paths in the cache, so that the efficiency of shortest path querying is significantly improved. Finally, we conduct comprehensive experimental studies on different datasets to verify that the cache hit ratio is greatly improved by using the proposed cache construction approaches.

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Dynamic Caching Content Replacement in Base Station Assisted Wireless D2D Caching Networks

Cited by 22 publications

References 44 publications

Social‐Aware Caching Strategy Based on Joint Action Deep Reinforcement Learning

Social‐Aware Caching Strategy Based on Joint Action Deep Reinforcement Learning

Applying Machine Learning Techniques for Caching in Edge Networks: A Comprehensive Survey

Constructing Effective Caches of Shortest Path Queries on Road Networks

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