An efficient computation offloading and resource allocation algorithm in RIS empowered MEC

Zhang, Xiangjun; Wu, Weiguo; Liu, Song; Wang, Jinyu

doi:10.1016/j.comcom.2022.10.012

Cited by 13 publications

(6 citation statements)

References 28 publications

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“…The problem is modeled as a Markov decision process and the Double Deep Q-Network algorithm is used to minimize the task drop rate. Xiangjun Zhang et al [13] propose a task offloading algorithm for Reconfigurable Intelligent Surface (RIS) empowered Mobile Edge Computing networks. The problem is formulated as a Markov Decision Process (MDP) where latency, energy consumption and operating costs are minimized.…”

Section: Related Workmentioning

confidence: 99%

Actor Critic-based Multi Objective Reinforcement Learning for Multi Access Edge Computing

Khot,

Pai

et al. 2024

IJACSA

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In recent times, large applications that need near real-time processing are increasingly being used on devices with limited resources. Multi access edge computing is a computing paradigm that provides a solution to this problem by placing servers as close to resource constrained devices as possible. However, the edge device must consider multiple conflicting objectives, viz., energy consumption, latency, task drop rate and quality of experience. Many previous approaches optimize on only one objective or a fixed linear combination of multiple objectives. These approaches don't ensure best performance for applications that run on edge servers, as there is no guarantee that the solution obtained by these approaches lies on the paretofront. In this work, Multi Objective Reinforcement Learning with Actor-Critic model is proposed to optimize the drop rate, latency and energy consumption parameters during offloading decision. The model is compared with MORL-Tabular, MORL-Deep Q Network and MORL-Double Deep Q Network models. The proposed model outperforms all the other models in terms of drop rate and latency.

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Section: Related Workmentioning

confidence: 99%

Actor Critic-based Multi Objective Reinforcement Learning for Multi Access Edge Computing

Khot,

Pai

et al. 2024

IJACSA

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“…Zhou et al [ 9 ] formulate the optimization problem of computation offloading, service caching, and resource allocation as mixed-integer non-linear programming (MINLP) and use the Deep Deterministic Policy Gradient (DDPG) algorithm to find the optimal strategy to minimize the long-term energy consumption. Zhang et al [ 10 ] use the reconfigurable intelligent surface (RIS) technique to adjust the phase shift and amplitude of reflective elements for improving the wireless network link status and energy efficiency and also use the DDPG algorithm for the computation offloading strategy. Li et al [ 28 ] also design a offloading strategy based on a DDPG approach where all computing tasks sequentially decide the computation location through an agent.…”

Section: Related Workmentioning

confidence: 99%

“…The centralized DRL will not only put pressure on the wireless network but also lead to the risk of user data leakage. However, the existing DRL-based methods [ 8 , 9 , 10 ] rarely consider the issue of data privacy protection.…”

Section: Introductionmentioning

confidence: 99%

A Federated Learning and Deep Reinforcement Learning-Based Method with Two Types of Agents for Computation Offload

Liu

Yang

Zhang

et al. 2023

Sensors

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With the rise of latency-sensitive and computationally intensive applications in mobile edge computing (MEC) environments, the computation offloading strategy has been widely studied to meet the low-latency demands of these applications. However, the uncertainty of various tasks and the time-varying conditions of wireless networks make it difficult for mobile devices to make efficient decisions. The existing methods also face the problems of long-delay decisions and user data privacy disclosures. In this paper, we present the FDRT, a federated learning and deep reinforcement learning-based method with two types of agents for computation offload, to minimize the system latency. FDRT uses a multi-agent collaborative computation offloading strategy, namely, DRT. DRT divides the offloading decision into whether to compute tasks locally and whether to offload tasks to MEC servers. The designed DDQN agent considers the task information, its own resources, and the network status conditions of mobile devices, and the designed D3QN agent considers these conditions of all MEC servers in the collaborative cloud-side end MEC system; both jointly learn the optimal decision. FDRT also applies federated learning to reduce communication overhead and optimize the model training of DRT by designing a new parameter aggregation method, while protecting user data privacy. The simulation results showed that DRT effectively reduced the average task execution delay by up to 50% compared with several baselines and state-of-the-art offloading strategies. FRDT also accelerates the convergence rate of multi-agent training and reduces the training time of DRT by 61.7%.

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“…The IRS comprises several passive reflective components that may be adjusted for amplitude and phase. The system can change the environment of the communication channel by modifying these reflecting components [ [3] , [4] , [5] ]. Compared to the more established wireless communication system relay technology, IRS may alter the wireless transmission channel's characteristics without using external energy, improve the received signal, and efficiently conserve energy while enhancing system performance [ 6 ].…”

Section: Introductionmentioning

confidence: 99%

An efficient algorithm for resource optimization in IRS-mmWave-NOMA B5G wireless networks

Liang,

Abdrabou,

Orumwense

et al. 2024

Heliyon

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An efficient computation offloading and resource allocation algorithm in RIS empowered MEC

Cited by 13 publications

References 28 publications

Actor Critic-based Multi Objective Reinforcement Learning for Multi Access Edge Computing

Actor Critic-based Multi Objective Reinforcement Learning for Multi Access Edge Computing

A Federated Learning and Deep Reinforcement Learning-Based Method with Two Types of Agents for Computation Offload

An efficient algorithm for resource optimization in IRS-mmWave-NOMA B5G wireless networks

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