NOMA-Aided Mobile Edge Computing via User Cooperation

Huang, Yu‐Wen; Liu, Yuan; Chen, Fangjiong

doi:10.1109/tcomm.2020.2964541

Cited by 69 publications

(48 citation statements)

References 28 publications

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“…Figure 4 illustrates the effectiveness of the DQN user-grouping algorithm proposed in this paper. By setting the numbers of users to [6,8,10], the algorithm showed a similar performance that the average energy consumption decreased over training. Although the performance may be worse than the random scheme at the beginning of the training, which was due to the random actions and unstable NN weights, it converged within the first 20 episodes for all three cases.…”

Section: Convergence Of the Frameworkmentioning

confidence: 94%

“…However, MCC will cause significant delays due to the long propagation distance between the central server and the user equipment. To address the long transmission delay issue, especially for delay-sensitive applications in the future 6G networks, multi-access edge computing (MEC) has emerged as a decentralized structure to provide the computation capability close to the terminal devices, which is generally implemented at the base stations to provide a cloud-like task processing service [ 7 , 8 , 9 , 10 ].…”

Section: Introductionmentioning

confidence: 99%

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DRL-Assisted Resource Allocation for NOMA-MEC Offloading with Hybrid SIC

Fang

Ding

2021

Entropy

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Multi-access edge computing (MEC) and non-orthogonal multiple access (NOMA) are regarded as promising technologies to improve the computation capability and offloading efficiency of mobile devices in the sixth-generation (6G) mobile system. This paper mainly focused on the hybrid NOMA-MEC system, where multiple users were first grouped into pairs, and users in each pair offloaded their tasks simultaneously by NOMA, then a dedicated time duration was scheduled to the more delay-tolerant user for uploading the remaining data by orthogonal multiple access (OMA). For the conventional NOMA uplink transmission, successive interference cancellation (SIC) was applied to decode the superposed signals successively according to the channel state information (CSI) or the quality of service (QoS) requirement. In this work, we integrated the hybrid SIC scheme, which dynamically adapts the SIC decoding order among all NOMA groups. To solve the user grouping problem, a deep reinforcement learning (DRL)-based algorithm was proposed to obtain a close-to-optimal user grouping policy. Moreover, we optimally minimized the offloading energy consumption by obtaining the closed-form solution to the resource allocation problem. Simulation results showed that the proposed algorithm converged fast, and the NOMA-MEC scheme outperformed the existing orthogonal multiple access (OMA) scheme.

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Section: Convergence Of the Frameworkmentioning

confidence: 94%

Section: Introductionmentioning

confidence: 99%

DRL-Assisted Resource Allocation for NOMA-MEC Offloading with Hybrid SIC

Fang

Ding

2021

Entropy

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“…The put forward two efficient algorithms to solve the problems of each scenario. In [30], a cooperative computing scheme was proposed by Huang et al to minimize energy consumption while maximizing the offloading data problems by jointly allocate communication and computation resources of the user and helper in a three-node MEC system, where the access point (AP) adopts a NOMA. In [31], Yang et al investigated efficient resource allocation for partial task offloading to get the minimization of completion time and energy in MEC networks with NOMA.…”

Section: Reference Delay Communication Computationmentioning

confidence: 99%

“…Besides, the time of video segmentation, stitching and storage can not be considered as they are very small compared with the communication and compression delays [33]. Similar to reference [30], the system model of this paper can also be applied to a lot of practical scenarios, like the surveillance systems, where a large number of video data coming from network cameras have to be further analyzed and stored.…”

Section: System Modelmentioning

confidence: 99%

Optimal Resource Allocation for Multimedia Applications Offloading in Mobile Edge Computing

Chen¹,

Zhao²,

Li³

et al. 2021

IEEE Open J. Comput. Soc.

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Thanks to the development of the technologies in the wireless communications and Internet of Things (IoT), the adoption of mobile devices is growing rapidly. Accordingly, the number of multimedia applications like face recognition and augmented reality generated from various mobile devices is growing at an unprecedented rate. The processing of these multimedia applications needs a lot of computation resources and has to be processed as quickly as possible. However, as these mobile devices have limited computation resources, the undesirable response delay will occur. By offloading the multimedia applications to the edge cloud close to the access point (AP) or the cellular base station (BS), mobile edge computing (MEC) is considered as a prospective approach to improve the quality of service (QoS) and enhance the computing capacity of mobile devices. Multimedia applications offloading in a MEC system are studied in this paper. The objective of the studied problem is to minimize the execution delay of multimedia applications of all mobile devices by allocating both the communication resource and the computing resource in the edge server. An optimization problem is formulated and an efficient multimedia applications offloading scheme is proposed to get the solution. Simulation results are conducted to verify the proposed application offloading method, which show that there is a significant execution delay reduction.

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“…Recently, the advantages of NOMA have also motivated the studies on the offloading of MEC [7], [8]. For example, Huang et al [7] investigated the joint task offloading and resource allocation for achieve both channel diversity and computing diversity faster in NOMA-based MEC networks.…”

Section: Introductionmentioning

confidence: 99%

Joint Multi-domain Resource Allocation and Trajectory Optimization in UAV-assisted M-IoT Networks

Qian¹

2022

Preprint

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<div>The integration of Maritime Internet of Things (M-IoT) technology and unmanned aerial/surface vehicles (UAVs/USVs) has been emerging as a promising navigational information technique in intelligent ocean systems. With the unprecedented increase of computation-intensive yet latency sensitive marine mobile Internet services, mobile edge computing (MEC) and non-orthogonal multiple access (NOMA) have been envisioned as promising approaches to providing with the low-latency as well as reliable computing services and ultra-dense connectivity. In this paper, we investigate the energy consumption minimization based energy-efficient MEC via cooperative NOMA for the UAV-assisted M-IoT networks. We consider that USVs offload their computation-workload to the UAV equipped with the edge-computing server subject to the UAV mobility. To improve the energy efficiency of offloading transmission and workload computation, we focus on minimizing the total energy consumption by jointly optimizing the USVs’ offloaded workload, transmit power, computation resource allocation as well as the UAV trajectory subject to the USVs’ latency requirements. Despite the nature of mixed discrete and non-convex programming of the formulated problem, we exploit the vertical decomposition and propose a two-layered algorithm for solving it efficiently. Specifically, the top-layered algorithm is proposed to solve the problem of optimizing the UAV trajectory based on the idea of Deep Reinforcement Learning (DRL), and the underlying algorithm is proposed to optimize the underlying multi-domain resource allocation problem based on the idea of the Lagrangian multiplier method. Numerical results are provided to validate the effectiveness of our proposed algorithms as well as the performance advantage of NOMA-enabled computation offloading in terms of overall energy consumption.</div>

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NOMA-Aided Mobile Edge Computing via User Cooperation

Cited by 69 publications

References 28 publications

DRL-Assisted Resource Allocation for NOMA-MEC Offloading with Hybrid SIC

DRL-Assisted Resource Allocation for NOMA-MEC Offloading with Hybrid SIC

Optimal Resource Allocation for Multimedia Applications Offloading in Mobile Edge Computing

Joint Multi-domain Resource Allocation and Trajectory Optimization in UAV-assisted M-IoT Networks

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