Resource Allocation for Energy-Efficient MEC in NOMA-Enabled Massive IoT Networks

Liu, Bing-Hong; Liu, Chenxi; Peng, Mugen

doi:10.1109/jsac.2020.3018809

Cited by 185 publications

(78 citation statements)

References 29 publications

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“…For instance, in Refs. [14,15], the nonorthogonal multiple access technique for optimal and suboptimal resource allocation in F-RANs was investigated. In Ref.…”

Section: Related Workmentioning

confidence: 99%

“…In Ref. [15], the main problem was decomposed into subproblems and then solved with matching and sequential convex programming algorithms. A hierarchical fog architecture was considered in Ref.…”

Section: Related Workmentioning

confidence: 99%

“…Manuscript received: 2020-10-17; revised: 2020-11-02; accepted: 2020- [11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28] demand of these applications [3] . Furthermore, the fifthgeneration cellular systems have enabled the explosive growth of IoT devices, whose number can reach approximately 24 billion by 2020.…”

Section: Introductionmentioning

confidence: 99%

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Deep reinforcement learning based computation offloading and resource allocation for low-latency fog radio access networks

Rahman

Dang

Ahmed

2020

Intell. and Converged Netw.

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“…For instance, in Refs. [14,15], the nonorthogonal multiple access technique for optimal and suboptimal resource allocation in F-RANs was investigated. In Ref.…”

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Deep reinforcement learning based computation offloading and resource allocation for low-latency fog radio access networks

Rahman

Dang

Ahmed

2020

Intell. and Converged Netw.

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“…An energy-efficient simultaneous wireless information and power transfer (SWIPT)-assisted NOMA system was presented in [15][16][17][18] and [19] in order to deal with spectrum sensing overhead and battery draining issues of cognitive radio (CR) network, and to enable massive connectivity at underutilized spectrum. [20], [21] applied NOMA for the massive connectivity and explored energy efficiency (EE) of mobile edge computing (MEC) in IoT networks. The application of NOMA is also valid in vehicular edge computing networks (VECNs), which integrate MEC and vehicular networks.…”

Section: Introductionmentioning

confidence: 99%

Performance Investigation of NOMA Versus OMA Techniques for mmWave Massive MIMO Communications

et al. 2021

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The fifth-generation (5G) of cellular technology is currently being deployed over the world. In the next decade of mobile networks, beyond 5G (B5G) cellular networks with the under-development advanced technology enablers are expected to be a fully developed system that could offer tremendous opportunities for both enterprises and society at large. B5G in more ambitious scenarios will be capable to facilitate much-improved performance with the significant upgrade of the key parameters such as massive connectivity, ultra-reliable and low latency (URLL), spectral efficiency (SE) and energy efficiency (EE). Equipping non-orthogonal multiple access (NOMA) with other key drivers will help to explore systems' applicability to cover a wide variety of applications to forge a path for future networks. NOMA empowers the networks with seamless connectivity and can provide a secure transmission strategy for the industrial internet of things (IIoT) anywhere and anytime. Despite being a promising candidate for B5G networks a comprehensive study that covers operating principles, fundamental features and technological feasibility of NOMA at mmWave massive MIMO communications is not available. To address this, a simulation-based comparative study between NOMA and orthogonal multiple access (OMA) techniques for mmWave massive multiple-input and multiple-output (MIMO) communications is presented with performance discussions and identifying technology gaps. Throughout the paper, aspects of operating principles, fundamental features and technological feasibility of NOMA are discussed. Also, it is demonstrated that NOMA not only has good adaptability but also can outperform other OMA techniques for mmWave massive MIMO communications. Some foreseeable challenges and future directions on applying NOMA to B5G networks are also provided.

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“…In [12], the authors formulated a joint user clustering and transmit power control problem and proposed a matchingcoalition approach for problem solving. In [13], the authors applied NOMA to enable massive connectivity to support energy-efficient MEC in IoT networks by joint communication and computation resource allocation optimization. In [14], the authors studied task processing delay minimization issues in a multiuser NOMA-based MEC network by optimizing tasks partition ratios and transmit power control.…”

Section: Introductionmentioning

confidence: 99%

Cost-Effective Optimization for Blockchain-Enabled NOMA-Based MEC Networks

Sun

et al. 2021

Security and Communication Networks

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Blockchain technology has been widely used in many fields. However, the proof of work (PoW) problem in the mining process of mobile devices requires a large amount of computing resources and energy consumption, which brings huge challenges to mobile devices. Mobile edge computing (MEC) can effectively solve the above problems, allowing mobile devices to offload tasks to edge servers to relieve the pressure of limited computing resources on mobile devices. Nonorthogonal multiple access (NOMA) is good at improving spectrum efficiency, so that the system can accommodate more users. In this paper, we propose a new NOMA-based MEC-enabled blockchain framework. Under the conditions of a given task execution deadline, the decision of offloading, local computing resource allocation, user clustering and admission control, and transmit power control is jointly optimized to minimize the total cost of the system. Since the problem is hard to solve, we decouple it into subproblems for low-complexity solutions. First, we propose two heuristic algorithms to obtain the binary offloading decision and user association, and then closed-form solutions of local resource allocation and transmit power control are obtained under the required delay constraints. Simulation results show that our proposed algorithms perform good in cost reduction compared with other baseline algorithms.

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Resource Allocation for Energy-Efficient MEC in NOMA-Enabled Massive IoT Networks

Cited by 185 publications

References 29 publications

Deep reinforcement learning based computation offloading and resource allocation for low-latency fog radio access networks

Deep reinforcement learning based computation offloading and resource allocation for low-latency fog radio access networks

Performance Investigation of NOMA Versus OMA Techniques for mmWave Massive MIMO Communications

Cost-Effective Optimization for Blockchain-Enabled NOMA-Based MEC Networks

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