A Proactive Caching Strategy Based on Deep Learning in EPC of 5G

Lei, Fangyuan; Dai, Qinyun; Cai, Jun; Zhao, Huimin; Liu, Xun; Liu, Yan

doi:10.1007/978-3-030-00563-4_72

Cited by 13 publications

(18 citation statements)

References 17 publications

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“…e papers that applied AE in solving problem in 5G wireless mobile network are discussed in this section. For example, Lei et al [61] proposed caching strategy based on Stacked Sparse AE (SSAE) in Evolved Packet Core of the 5G mobile wireless networks. e network functions virtual (NFV)/software defined network (SDN) is used for the development of the virtual distributed deep learning on the SSAE.…”

Section: E Autoencoder Architecture Applications In 5g Wireless Mobil...mentioning

confidence: 99%

Deep Learning-Based Solutions for 5G Network and 5G-Enabled Internet of Vehicles: Advances, Meta-Data Analysis, and Future Direction

Almutairi

2022

Mathematical Problems in Engineering

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The advent of the 5G mobile network has brought a lot of benefits. However, it prompted new challenges on the 5G network cybersecurity defense system, resource management, energy, cache, and mobile network, therefore making the existing approaches obsolete to tackle the new challenges. As a result of that, research studies were conducted to investigate deep learning approaches in solving problems in 5G network and 5G powered Internet of Vehicles (IoVs). In this article, we present a survey on the applications of deep learning algorithms for solving problems in 5G mobile network and 5G powered IoV. The survey pointed out the recent advances on the adoption of deep learning variants in solving the challenges of 5G mobile network and 5G powered IoV. The deep learning algorithm solutions for security, energy, resource management, 5G-enabled IoV, and mobile network in 5G communication systems were presented including several other applications. New comprehensive taxonomies were created, and new comprehensive taxonomies were suggested, analysed, and presented. The challenges of the approaches are already discussed in the literature, and new perspective for solving the challenges was outlined and discussed. We believed that this article can stimulate new interest in practical applications of deep learning in 5G network and provide clear direction for novel approaches to expert researchers.

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Section: E Autoencoder Architecture Applications In 5g Wireless Mobil...mentioning

confidence: 99%

Deep Learning-Based Solutions for 5G Network and 5G-Enabled Internet of Vehicles: Advances, Meta-Data Analysis, and Future Direction

Almutairi

2022

Mathematical Problems in Engineering

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“…The majority of published work deals with caching and main goal is to increase CHR [56]. CHR variations are intimately linked to all other caching targets [57]. When content is available in-network and not accessed via CDNs and backhaul lines, it directly affects content access latency, user QoE and,data offload ratio [58].…”

Section: Related Workmentioning

confidence: 99%

Intelligent Stretch Optimization in Information Centric Networking-Based Tactile Internet Applications

et al. 2021

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The fifth-generation (5G) mobile network services are currently being made available for different use case scenarios like enhanced mobile broadband, ultra-reliable and low latency communication, and massive machine-type communication. The ever-increasing data requests from the users have shifted the communication paradigm to be based on the type of the requested data content or the so-called information-centric networking (ICN). The ICN primarily aims to enhance the performance of the network infrastructure in terms of the stretch to opt for the best routing path. Reduction in stretch merely reduces the end-to-end (E2E) latency to ensure the requirements of the 5G-enabled tactile internet (TI) services. The foremost challenge tackled by the ICN-based system is to minimize the stretch while selecting an optimal routing path. Therefore, in this work, a reinforcement learning-based intelligent stretch optimization (ISO) strategy has been proposed to reduce stretch and obtain an optimal routing path in ICN-based systems for the realization of 5G-enabled TI services. A Q-learning algorithm is utilized to explore and exploit the different routing paths within the ICN infrastructure. The problem is designed as a Markov decision process and solved with the help of the Q-learning algorithm. The simulation results indicate that the proposed strategy finds the optimal routing path for the delay-sensitive haptic-driven services of 5G-enabled TI based upon their stretch profile over ICN, such as the augmented reality /virtual reality applications. Moreover, we compare and evaluate the simulation results of propsoed ISO strategy with random routing strategy and history aware routing protocol (HARP). The proposed ISO strategy reduces 33.33% and 33.69% delay as compared to random routing and HARP, respectively. Thus, the proposed strategy suggests an optimal routing path with lesser stretch to minimize the E2E latency.

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“…The authors also think about the implementation in a distributed way by SDN/NFV technical, i.e., the input layer is deployed on the sink node, while the rest layers are implemented on the main controller. A related work applying auto encoder in 5G network proactive caching can be found in Reference [74]. In Reference [75], two auto encoders are utilized for extracting the features of users and content, respectively.…”

Section: Auto Encodermentioning

confidence: 99%

A Survey of Deep Learning for Data Caching in Edge Network

Wang

Friderikos

2020

Informatics

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The concept of edge caching provision in emerging 5G and beyond mobile networks is a promising method to deal both with the traffic congestion problem in the core network, as well as reducing latency to access popular content. In that respect, end user demand for popular content can be satisfied by proactively caching it at the network edge, i.e., at close proximity to the users. In addition to model-based caching schemes, learning-based edge caching optimizations have recently attracted significant attention, and the aim hereafter is to capture these recent advances for both model-based and data-driven techniques in the area of proactive caching. This paper summarizes the utilization of deep learning for data caching in edge network. We first outline the typical research topics in content caching and formulate a taxonomy based on network hierarchical structure. Then, many key types of deep learning algorithms are presented, ranging from supervised learning to unsupervised learning, as well as reinforcement learning. Furthermore, a comparison of state-of-the-art literature is provided from the aspects of caching topics and deep learning methods. Finally, we discuss research challenges and future directions of applying deep learning for caching.

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A Proactive Caching Strategy Based on Deep Learning in EPC of 5G

Cited by 13 publications

References 17 publications

Deep Learning-Based Solutions for 5G Network and 5G-Enabled Internet of Vehicles: Advances, Meta-Data Analysis, and Future Direction

Deep Learning-Based Solutions for 5G Network and 5G-Enabled Internet of Vehicles: Advances, Meta-Data Analysis, and Future Direction

Intelligent Stretch Optimization in Information Centric Networking-Based Tactile Internet Applications

A Survey of Deep Learning for Data Caching in Edge Network

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