Joint Access Selection and Heterogeneous Resources Allocation in UDNs with MEC Based on Non-Orthogonal Multiple Access

Seng, Shuming; Li, Xi; Ji, Hong; Zhang, Heli

doi:10.1109/iccw.2018.8403502

Cited by 13 publications

(13 citation statements)

References 11 publications

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“…Some authors have researched the computation offloading in MEC system with NOMA. An edge computing aware NOMA proposed in the work of Kiani and Ansari was considered to minimize the energy consumption of MEC users via optimizing the user clustering, computing and communication resource allocation, and transmit power.…”

Section: Related Workmentioning

confidence: 99%

“…A dynamic cooperative CR-NOMA scheme, which could achieve better outage performance, was proposed in the work of Lv et al 20 These studies have achieved the high spectral efficiency of downlink in CRN, but how to improve the spectral and access efficiency of uplink scenario is not mentioned. Some authors [21][22][23][24] have researched the computation offloading in MEC system with NOMA. An edge computing aware NOMA proposed in the work of Kiani and Ansari 21 was considered to minimize the energy consumption of MEC users via optimizing the user clustering, computing and communication resource allocation, and transmit power.…”

Section: Related Workmentioning

confidence: 99%

“…Although some outstanding works have been devoted to the research of a CR network (CRN) or an MEC‐enabled system both with NOMA, some works have been followed with interest an MEC‐enabled CRN . These works have not studied the computation offloading scheme for a NOMA‐enabled CRN where we believe the joint system can enhance the performance significantly.…”

Section: Introductionmentioning

confidence: 99%

“…15,16 Although some outstanding works have been devoted to the research of a CR network (CRN) or an MEC-enabled system both with NOMA, some works have been followed with interest an MEC-enabled CRN. [17][18][19][20][21][22][23][24][25][26] These works have not studied the computation offloading scheme for a NOMA-enabled CRN where we believe the joint system can enhance the performance significantly. Unfortunately, in CRN, the access of the SUs as usual will inevitably cause interference to the PU and the increased SUs brought by NOMA can lead to higher burden on the MEC server.…”

Section: Introductionmentioning

confidence: 99%

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Edge computing and power control in NOMA‐enabled cognitive radio networks

Cheng

Chen

Liang

2019

Trans Emerging Tel Tech

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Due to the limited computation resources of mobile devices in cognitive radio networks, the secondary users in the network can suffer from long executing time, which is not acceptable for latency‐sensitive and computation‐intensive tasks. To tackle this issue, this paper proposes to reduce the task computing latency for secondary networks by offloading the tasks to edge servers through leveraging mobile edge computing (MEC) that is emerging as a promising technology to augment the computation capacity of mobile devices. Specifically, under the conditions that the interference caused by secondary users is tolerable to primary user and within the available computation resources of the MEC server, the primary user and secondary users both can offload tasks to the MEC server through nonorthogonal multiple access. Thus, we jointly formulate the offloading decision and power control as an optimization problem, aiming at minimizing the overall computing latency for secondary networks. To overcome the computational complexity caused by the nonconvexity of the original problem, we transform the original problem to a solvable problem and decouple the transformed problem into the separate offloading decision and power control. An iterative algorithm is proposed based on block coordinate decent method to achieve the near‐optimal solution. Simulation results show that under the same parameters, such as the number of primary users, maximum transmit power, computational capability of the MEC server and the computational capability of the secondary users, the proposed NOMA‐enabled computation offloading scheme can effectively reduce the overall computing latency for the secondary network and improve the percentage of offloading secondary users than those of OMA‐enabled.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Edge computing and power control in NOMA‐enabled cognitive radio networks

Cheng

Chen

Liang

2019

Trans Emerging Tel Tech

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“…Among the different techniques, access selection and resource allocation play a fundamental role to ensure an efficient usage of the radio resources. Hence, it is sensible for both techniques to be tightly coordinated in joint solutions [22,1]. In addition, the appearance of new network techniques and architectures, such as SDN [28] and C-RAN [18], will allow the use of more centralized algorithms, which would actually facilitate such tight coordination among the elements of the dense network.…”

Section: Introductionmentioning

confidence: 99%

Fast and efficient energy-oriented cell assignment in heterogeneous networks

et al. 2019

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The cell assignment problem is combinatorial, with increased complexity when it is tackled considering resource allocation. This paper models joint cell assignment and resource allocation for cellular heterogeneous networks, and formalizes cell assignment as an optimization problem. Exact algorithms can find optimal solutions to the cell assignment problem, but their execution time increases drastically with realistic network deployments. In turn, heuristics are able to find solutions in reasonable execution times, but they get usually stuck in local optima, thus failing to find optimal solutions. Metaheuristic approaches have been successful in finding solutions closer to the optimum one to combinatorial problems for large instances. In this paper we propose a fast and efficient heuristic that yields very competitive cell assignment solutions compared to those obtained with three of the most widely-used metaheuristics, which are known to find solutions close to the optimum due to the nature of their search space exploration. Our heuristic approach adds energy expenditure reduction in its algorithmic design. Through simulation and formal statistical analysis, the proposed scheme has been proved to produce efficient assignments in terms of the number of served users, resource

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Deep Learning Based Dynamic Uplink Power Control for NOMA Ultra-Dense Network System

Liu

Chen

et al. 2019

Communications in Computer and Information Science

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Joint Access Selection and Heterogeneous Resources Allocation in UDNs with MEC Based on Non-Orthogonal Multiple Access

Cited by 13 publications

References 11 publications

Edge computing and power control in NOMA‐enabled cognitive radio networks

Edge computing and power control in NOMA‐enabled cognitive radio networks

Fast and efficient energy-oriented cell assignment in heterogeneous networks

Deep Learning Based Dynamic Uplink Power Control for NOMA Ultra-Dense Network System

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