Learning-Based Delay-Aware Caching in Wireless D2D Caching Networks
TL;DR: In this paper, the authors proposed an efficient learning-based caching algorithm operating together with a non-parametric estimator to minimize the average transmission delay in D2D-enabled cellular networks.
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Abstract: Recently, wireless caching techniques have been studied to satisfy lower delay requirements and offload traffic from peak periods. By storing parts of the popular files at the mobile users, users can locate some of their requested files in their own caches or the caches at their neighbors. In the latter case, when a user receives files from its neighbors, device-to-device (D2D) communication is performed. The D2D communication underlaid with cellular networks is also a new paradigm for the upcoming wireless systems. By allowing a pair of adjacent D2D users to communicate directly, D2D communication can achieve higher throughput, better energy efficiency, and lower traffic delay. In this paper, we propose an efficient learning-based caching algorithm operating together with a non-parametric estimator to minimize the average transmission delay in D2D-enabled cellular networks. It is assumed that the system does not have any prior information regarding the popularity of the files, and the non-parametric estimator is aimed at learning the intensity function of the file requests. An algorithm is devised to determine the best pairs that provide the best delay improvement in each loop to form a caching policy with very low-transmission delay and high throughput. This algorithm is also extended to address a more general scenario, in which the distributions of fading coefficients and the values of system parameters potentially change over time. Via numerical results, the superiority of the proposed algorithm is verified by comparing it with a naive algorithm, in which all users simply cache their favorite files, and by comparing with a probabilistic algorithm, the users cache a file with a probability that is proportional to its popularity.
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Citations
Deep Reinforcement Learning-Based Edge Caching in Wireless Networks
TL;DR: This work proposes deep actor-critic reinforcement learning based policies for both centralized and decentralized content caching for offload data traffic in wireless networks and considers both the cache hit rate and transmission delay as performance metrics.
165
Energy Minimization in D2D-Assisted Cache-Enabled Internet of Things: A Deep Reinforcement Learning Approach
Jie Tang,Hengbin Tang,Xiu Yin Zhang,Kanapathippillai Cumanan,Gaojie Chen,Kai-Kit Wong,Jonathon A. Chambers +6 more
TL;DR: A novel scheme based on reinforcement learning (RL) is proposed to reveal the popularity of files as well as users’ preference and enables user devices and the SBS to prefetch the optimal files while learning, which can reduce the energy cost significantly.
Deep Multi-Agent Reinforcement Learning Based Cooperative Edge Caching in Wireless Networks
Chen Zhong,M. Cenk Gursoy,Senem Velipasalar +2 more
- 20 May 2019
TL;DR: This paper proposes a deep actor-critic reinforcement learning based multi-agent framework with the aim to minimize the overall average transmission delay and compares the learning-based performance with three other caching policies.
53
Dueling Deep-Q-Network Based Delay-Aware Cache Update Policy for Mobile Users in Fog Radio Access Networks
TL;DR: Simulation results illustrate that the proposed caching policy yields better average hit ratio and lower average transmission delay than other traditional caching policies, including first in first out, least recently used and least frequently used caching policies.
Individual Preference Aware Caching Policy Design in Wireless D2D Networks
Ming-Chun Lee,Andreas F. Molisch +1 more
TL;DR: It is shown that performance can improve significantly with proper exploitation of individual preferences and that different types of tradeoffs exist between different performance metrics and that they can be managed through caching policy and cooperation distance designs.
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