Toward Smart and Cooperative Edge Caching for 5G Networks: A Deep Learning Based Approach

Pang, Haitian; Liu, Jiangchuan; Fan, Xiaoyi; Sun, Lifeng

doi:10.1109/iwqos.2018.8624176

Cited by 60 publications

(52 citation statements)

References 8 publications

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“…Finally, [8] is the closest paper to ours, because it uses an LSTM-NN as a popularity predictor and then manages the cache as a priority queue where, upon a miss, contents with the smallest predicted popularity are evicted. We have implemented the caching policy in [8] and compared it with ours in Sect. 4.…”

Section: Related Workmentioning

confidence: 99%

“…The core idea of our policy is simple and was naturally adopted by previous works like [12] and [8]: we keep in the cache the contents with the largest estimated popularities. The popularity of a content is defined as the fraction of requests for that content over a meaningful time horizon.…”

Section: Caching Policymentioning

confidence: 99%

“…We wanted also to compare FNN-caching with more complex NN-based policies. We have considered the DLSTM scheme presented in [8]. We were not able to get the code or the traces from the authors.…”

Section: Comparison With Other Caching Policiesmentioning

confidence: 99%

“…We were not able to get the code or the traces from the authors. So, we have implemented it in the same simulator and tried to select all parameters according to the suggestions in [8]. Figure 2(a) shows the hit rate the different policies achieve over Akamai 5-day trace.…”

Section: Comparison With Other Caching Policiesmentioning

confidence: 99%

See 3 more Smart Citations

Feedforward Neural Networks for Caching

Fedchenko

Neglia

Ribeiro

2019

SIGMETRICS Perform. Eval. Rev.

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We propose a caching policy that uses a feedforward neural network (FNN) to predict content popularity. Our scheme outperforms popular eviction policies like LRU or ARC, but also a new policy relying on the more complex recurrent neural networks. At the same time, replacing the FNN predictor with a naive linear estimator does not degrade caching performance significantly, questioning then the role of neural networks for these applications. * Authors are listed in alphabetical order.

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Section: Related Workmentioning

confidence: 99%

Section: Caching Policymentioning

confidence: 99%

Section: Comparison With Other Caching Policiesmentioning

confidence: 99%

Section: Comparison With Other Caching Policiesmentioning

confidence: 99%

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Feedforward Neural Networks for Caching

Fedchenko

Neglia

Ribeiro

2019

SIGMETRICS Perform. Eval. Rev.

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show abstract

“…Due to the complexity of the real environment, these conventional replacement policies cannot accurately capture dynamic characteristics of content popularity [4]. Inspired by the reinforcement learning (RL) in solving complicated control problem [5], the works in [6], [7] relied on strong feature Manuscript representation ability of deep neural network (DNN) [8] and adopted the model-free deep RL (DRL) to maximize the long-term system reward in mobile edge caching. In [6]- [8], the edge node fetches the missed content from the source server and replaces its local cache with newly fetched content.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Content Update for Wireless Edge Caching via Deep Reinforcement Learning

Shi

et al. 2019

IEEE Commun. Lett.

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This letter studies a basic wireless caching network where a source server is connected to a cache-enabled base station (BS) that serves multiple requesting users. A critical problem is how to improve cache hit rate under dynamic content popularity. To solve this problem, the primary contribution of this work is to develop a novel dynamic content update strategy with the aid of deep reinforcement learning. Considering that the BS is unaware of content popularities, the proposed strategy dynamically updates the BS cache according to the time-varying requests and the BS cached contents. Towards this end, we model the problem of cache update as a Markov decision process and put forth an efficient algorithm that builds upon the long shortterm memory network and external memory to enhance the decision making ability of the BS. Simulation results show that the proposed algorithm can achieve not only a higher average reward than deep Q-network, but also a higher cache hit rate than the existing replacement policies such as the least recently used, first-in first-out, and deep Q-network based algorithms.Index Terms-Content update, Markov decision process, deep reinforcement learning, cache hit rate, long-term reward.

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Federated learning and next generation wireless communications: A survey on bidirectional relationship

Shome

Waqar

Khan

2022

Trans Emerging Tel Tech

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In order to meet the extremely heterogeneous requirements of the next generation wireless communication networks, research community is increasingly dependent on using machine‐learning solutions for real‐time decision‐making and radio resource management. Traditional machine learning employs fully centralized architecture in which the entire training data is collected at one node for example, cloud server, that significantly increases the communication overheads and also raises severe privacy concerns. Toward this end, a distributed machine‐learning paradigm termed as federated learning (FL) has been proposed recently. In FL, each participating edge device trains its local model by using its own training data. Then, via the wireless channels the weights or parameters of the locally trained models are sent to the central parameter server (PS), that aggregates them and updates the global model. On one hand, FL plays an important role for optimizing the resources of wireless communication networks, on the other hand, wireless communications is crucial for FL. Thus, a “bidirectional” relationship exists between FL and wireless communications. Although FL is an emerging concept, many publications have already been published in the domain of FL and its applications for next generation wireless networks. Nevertheless, we noticed that none of the works have highlighted the bidirectional relationship between FL and wireless communications. Therefore, the purpose of this survey article is to bridge this gap in literature by providing a timely and comprehensive discussion on the interdependency between FL and wireless communications.

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Toward Smart and Cooperative Edge Caching for 5G Networks: A Deep Learning Based Approach

Cited by 60 publications

References 8 publications

Feedforward Neural Networks for Caching

Feedforward Neural Networks for Caching

Dynamic Content Update for Wireless Edge Caching via Deep Reinforcement Learning

Federated learning and next generation wireless communications: A survey on bidirectional relationship

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