A Reinforcement Learning Approach for D2D-Assisted Cache-Enabled HetNets

Tang, Jie; Tang, Hengbin; Zhao, Nan; Cumanan, Kanapathippillai; Zhang, Shunqing; Zhou, Yijing

doi:10.1109/globecom38437.2019.9014027

Cited by 10 publications

(2 citation statements)

References 12 publications

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“…However, these methods have limitations in dealing with dynamically changing content popularity. Certain studies [14][15][16][17] have adopted reinforcement learning (RL) techniques based on insights into content popularity to proactively manage cache decisions, thereby demonstrating a shift toward dynamic, adaptive caching strategies; however, these can still fail to capture complex data patterns.…”

Section: Related Workmentioning

confidence: 99%

Enhancing Heterogeneous Network Performance: Advanced Content Popularity Prediction and Efficient Caching

Sun,

Chen

2024

Electronics

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With the popularity of smart devices and the growth of high-bandwidth applications, the wireless industry is facing an increased surge in data traffic. This challenge highlights the limitations of traditional edge-caching solutions, especially in terms of content-caching effectiveness and network-communication latency. To address this problem, we investigated efficient caching strategies in heterogeneous network environments. The caching decision process becomes more complex due to the heterogeneity of the network environment, as well as due to the diversity of user behaviors and content requests. To address the problem of increased system latency due to the dynamically changing nature of content popularity and limited cache capacity, we propose a novel content placement strategy, the long-short-term-memory–content-population-prediction model, to capture the correlation of request patterns between different contents and the periodicity in the time domain, in order to improve the accuracy of the prediction of content popularity. Then, to address the heterogeneity of heterogeneous network environments, we propose an efficient content delivery strategy: the multi-intelligent critical collaborative caching policy. This strategy models the edge-caching problem in heterogeneous scenarios as a Markov decision process using multi-base-station-environment information. In order to fully utilize the multi-intelligence information, we have improved the actor–critic approach by integrating the attention mechanism into a neural network. Whereas the actor network is responsible for making decisions based on local information, the critic network evaluates and enhances the actor’s performance. We conducted extensive simulations, and the results showed that the Long Short Term Memory content population prediction model was more advantageous, in terms of content-popularity-prediction accuracy, with a 28.61% improvement in prediction error, compared to several other existing methods. The proposed multi-intelligence actor–critic collaborative caching policy algorithm improved the cache-hit-rate metric by up to 32.3% and reduced the system latency by 1.6%, demonstrating the feasibility and effectiveness of the algorithm.

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Section: Related Workmentioning

confidence: 99%

Enhancing Heterogeneous Network Performance: Advanced Content Popularity Prediction and Efficient Caching

Sun,

Chen

2024

Electronics

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“…In [7], the fixed global content popularity is estimated based on collaborative filtering and then exploited for cache decision to maximize the average user request satisfaction ratio in small-cell networks. The work [8] considers the minimization of energy cost for systematic traffic transmission under a framework consisting of mobile edge caching and cache-enabled D2D communications. In [9], the authors propose a scheme based on LSTM and external memory to enhance the decision making ability of the base station.…”

Section: B Related Workmentioning

confidence: 99%

Deep Learning for Wireless Coded Caching with Unknown and Time-Variant Content Popularity

Zhang

Tao

2020

Preprint

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Coded caching is effective in leveraging the accumulated storage size in wireless networks by distributing different coded segments of each file in multiple cache nodes. This paper aims to find a wireless coded caching policy to minimize the total discounted network cost, which involves both transmission delay and cache replacement cost, using tools from deep learning. The problem is known to be challenging due to the unknown, time-variant content popularity as well as the continuous, high-dimensional action space. We first propose a clustering based long short-term memory (C-LTSM) approach to predict the number of content requests using historical request information. This approach exploits the correlation of the historical request information between different files through clustering.Based on the predicted results, we then propose a supervised deep deterministic policy gradient (SDDPG) approach. This approach, on one hand, can learn the caching policy in continuous action space by using the actor-critic architecture. On the other hand, it accelerates the learning process by pre-training the actor network based on the solution of an approximate problem that minimizes the per-slot cost. Realworld trace-based numerical results show that the proposed prediction and caching policy using deep learning outperform the considered existing methods.

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Cost Performance Driven Multi-request Allocation in D2D Service Provision Systems

Chen

et al. 2022

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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A Reinforcement Learning Approach for D2D-Assisted Cache-Enabled HetNets

Cited by 10 publications

References 12 publications

Enhancing Heterogeneous Network Performance: Advanced Content Popularity Prediction and Efficient Caching

Enhancing Heterogeneous Network Performance: Advanced Content Popularity Prediction and Efficient Caching

Deep Learning for Wireless Coded Caching with Unknown and Time-Variant Content Popularity

Cost Performance Driven Multi-request Allocation in D2D Service Provision Systems

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