A proactive caching and offloading technique using machine learning for mobile edge computing users

Alqahtani, Fayez; Al‐Maitah, Mohammed; Elshakankiry, Osama A.

doi:10.1016/j.comcom.2021.10.017

Cited by 10 publications

(8 citation statements)

References 29 publications

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“…[ 8 ] presents a machine-centric proactive multi-cell association (PMCA) scheme that demonstrates the viability of an open-loop transmission-based architecture. With the aid of a proactive service and an edge server, a substantial study has been conducted on precaching relevant data near the user [ 9 , 19 ]. Regarding communication security, some studies have also proposed to achieve eavesdropping avoidance through proactive interference [ 20 ].…”

Section: Related Workmentioning

confidence: 99%

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RIS-Aided Proactive Mobile Network Downlink Interference Suppression: A Deep Reinforcement Learning Approach

Wang

Sun

Cui

et al. 2023

Sensors

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A proactive mobile network (PMN) is a novel architecture enabling extremely low-latency communication. This architecture employs an open-loop transmission mode that prohibits all real-time control feedback processes and employs virtual cell technology to allocate resources non-exclusively to users. However, such a design also results in significant potential user interference and worsens the communication’s reliability. In this paper, we propose introducing multi-reconfigurable intelligent surface (RIS) technology into the downlink process of the PMN to increase the network’s capacity against interference. Since the PMN environment is complex and time varying and accurate channel state information cannot be acquired in real time, it is challenging to manage RISs to service the PMN effectively. We begin by formulating an optimization problem for RIS phase shifts and reflection coefficients. Furthermore, motivated by recent developments in deep reinforcement learning (DRL), we propose an asynchronous advantage actor–critic (A3C)-based method for solving the problem by appropriately designing the action space, state space, and reward function. Simulation results indicate that deploying RISs within a region can significantly facilitate interference suppression. The proposed A3C-based scheme can achieve a higher capacity than baseline schemes and approach the upper limit as the number of RISs increases.

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Section: Related Workmentioning

confidence: 99%

“…Consequently, a proactive mobile network (PMN) architecture is proposed [ 5 , 6 , 7 ]. The PMN architecture is considered to have significant theoretical value and holds the potential for deployment in future 6G networks [ 8 , 9 , 10 ].…”

Section: Introductionmentioning

confidence: 99%

RIS-Aided Proactive Mobile Network Downlink Interference Suppression: A Deep Reinforcement Learning Approach

Wang

Sun

Cui

et al. 2023

Sensors

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“…To address these issues described above, a dynamic caching strategy that combines proactive caching and cache replacement must continuously update the cached content as the environment changes, and reinforcement learning approaches can effectively solve complex online optimization problems and maximize the long-term reward without requiring prior knowledge of the network under consideration. Reinforcement learning approaches have been proven effective in cache optimization in previous work [14]- [16]. In this study, the attention-weighted depth deterministic policy gradient (AWD2PG) is proposed to solve the dynamic joint cache optimization problem between the user and server sides under limited transmission channels and time-varying and unobservable content popularity.…”

Section: Introductionmentioning

confidence: 99%

Attention Mechanism-Aided Deep Reinforcement Learning for Dynamic Edge Caching

Teng,

Fang,

Yang

et al. 2024

IEEE Internet Things J.

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The dynamic mechanism of joint proactive caching and cache replacement, which involves placing content items close to cache-enabled edge devices ahead of time until they are requested, is a promising technique for enhancing traffic offloading and relieving heavy network loads. However, due to limited edge cache capacity and wireless transmission resources, accurately predicting users' future requests and performing dynamic caching is crucial to effectively utilizing these limited resources. This paper investigates joint proactive caching and cache replacement strategies in a general mobile edge computing (MEC) network with multiple users under a cloud-edge-device collaboration architecture. The joint optimization problem is formulated as a markov decision process (MDP) problem with an infinite range of average network load costs, aiming to reduce network load traffic while efficiently utilizing the limited available transport resources. To address this issue, we design an Attention Weighted Deep Deterministic Policy Gradient (AWD2PG) model, which uses attention weights to allocate the number of channels from server to user, and applies deep deterministic policies on both user and server sides for Cache decision-making, so as to achieve the purpose of reducing network traffic load and improving network and cache resource utilization. We verify the convergence of the corresponding algorithms and demonstrate the effectiveness of the proposed AWD2PG strategy and benchmark in reducing network load and improving hit rate.

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“…In this area, a lot of studies have been performed to utilize the communication resources as well as calculating resources in the MEC-aided IoT networks, through some conventional optimization methods such as convex optimization or some intelligent algorithms such as deep reinforcement leaning (DRL) algorithms, in order to reduce the system delay and PoCo [9]. This can help make the MEC-aided IoT networks fit the various applications [10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Chebyshev-Gauss Approximation Analysis for Mobile Edge Computing-Aided IoT Networks

Zhu¹,

Chen²,

Zhou

et al. 2022

Wireless Communications and Mobile Computing

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Recently, mobile edge computing (MEC) has been widely applied into Internet of Things (IoT) networks, which has attracted a lot of attention from researchers. A critical challenge in the MEC-aided IoT networks is that the performance analysis is often complicated, where it is quite difficult for us to obtain some analytical or closed-form solution to the performance analysis, such as outage probability and bit error rate. This has been the bottleneck of the development of MEC-aided IoT networks. To address this challenge, we deeply investigate the Chebyshev-Gauss approximation method and derive the analytical solution to implement this powerful and useful approximation. We then give several examples to show the effectiveness of the Chebyshev-Gauss approximation in the performance analysis for the MEC-aided IoT systems. The results in this work can serve as an important reference and reveal some important inherent mechanisms for the MEC-aided IoT networks.

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A proactive caching and offloading technique using machine learning for mobile edge computing users

Cited by 10 publications

References 29 publications

RIS-Aided Proactive Mobile Network Downlink Interference Suppression: A Deep Reinforcement Learning Approach

RIS-Aided Proactive Mobile Network Downlink Interference Suppression: A Deep Reinforcement Learning Approach

Attention Mechanism-Aided Deep Reinforcement Learning for Dynamic Edge Caching

Chebyshev-Gauss Approximation Analysis for Mobile Edge Computing-Aided IoT Networks

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