NOMA-Assisted Multi-Access Mobile Edge Computing: A Joint Optimization of Computation Offloading and Time Allocation

Wu, Yuan; Ni, Kejie; Zhang, Cheng; Qian, Li Ping; Tsang, Danny H. K.

doi:10.1109/tvt.2018.2875337

Cited by 242 publications

(80 citation statements)

References 43 publications

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“…From (2) to (5), the substitution approach has been widely adopted in the literature, e.g., [4], [13], [22], we thus omit the detailed steps to to avoid redundancy but use a two-user case,…”

Section: Optimization Problemmentioning

confidence: 99%

“…Since p i is uniform over RUs in a cell, optimizing this single variable can be carried out by a bisection search method. With the power value p i , the corresponding load l i s can be calculated for each cluster by (22). The bisection search for power p i terminates when the gap between the sum load s∈S * i l i s and the target load l i is less than a predefined tolerance .…”

Section: B Power-load Optimization For Partitioned Clustering Schemesmentioning

confidence: 99%

See 1 more Smart Citation

Load Coupling and Energy Optimization in Multi-Cell and Multi-Carrier NOMA Networks

Lei

You

Yang

et al. 2019

IEEE Trans. Veh. Technol.

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In this paper, we investigate energy optimization in multi-cell and multi-carrier non-orthogonal multiple access (NOMA) networks. We apply a load-coupling model for NOMA networks to capture the coupling relation of mutual interference among cells. With this analytical tool, we formulate an energy minimization problem in a NOMA-based load-coupled system, where optimizing load-rate-power allocation, and determining decoding order and user grouping are the key aspects. Theoretically, we prove that the minimum consumed energy can be achieved by using all the time-frequency resources in each cell to deliver users' demand, and allowing all the users to share resource units. From a practical perspective, we consider three types of NOMA grouping schemes, i.e., all-user grouping, partitioned and non-partitioned grouping. We develop tailored solutions for each grouping scheme to enable efficient load-rate-power optimization. These three algorithmic components are embedded into a power-adjustment framework to provide energy-efficient solutions for NOMA networks. Numerical results demonstrate promising energy-saving gains of NOMA over orthogonal multiple access in large-scale cellular networks, in particular for high-demand and resource-limited scenarios. The results also show fast convergence of the proposed algorithms and demonstrate the effectiveness of the solutions.

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Section: Optimization Problemmentioning

confidence: 99%

Section: B Power-load Optimization For Partitioned Clustering Schemesmentioning

confidence: 99%

Load Coupling and Energy Optimization in Multi-Cell and Multi-Carrier NOMA Networks

Lei

You

Yang

et al. 2019

IEEE Trans. Veh. Technol.

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“…Different from the OMA schemes, one resource block is allowed to be allocated to multiple users in NOMA, for improving spectral efficiency and massive connections. There are a handful of works considering NOMA-aided MEC [17]- [22]. In [21] and [22], the authors considered NOMA-MEC for latency minimization.…”

Section: Introductionmentioning

confidence: 99%

“…There are a handful of works considering NOMA-aided MEC [17]- [22]. In [21] and [22], the authors considered NOMA-MEC for latency minimization. However, these NOMA-MEC works focused on task offloading between devices and AP, without consideration of cooperative computing.…”

Section: Introductionmentioning

confidence: 99%

NOMA-Aided Mobile Edge Computing via User Cooperation

Huang

Liu

Chen

2020

IEEE Trans. Commun.

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Exploiting the idle computation resources of mobile devices in mobile edge computing (MEC) system can achieve both channel diversity and computing diversity as mobile devices can offload their computation tasks to nearby mobile devices in addition to MEC server embedded access point (AP). In this paper, we propose a non-orthogonal multiple-access (NOMA)aided cooperative computing scheme in a basic three-node MEC system consisting of a user, a helper, and an AP. In particular, we assume that the user can simultaneously offload data to the helper and the AP using NOMA, while the helper can locally compute data and offload data to the AP at the same time. We study two optimization problems, energy consumption minimization and offloading data maximization, by joint communication and computation resource allocation of the user and helper. We find the optimal solutions for the two non-convex problems by some proper mathematical methods. Simulation results are presented to demonstrate the effectiveness of the proposed schemes. Some useful insights are provided for practical designs.Index Terms-Cooperative computing, mobile edge computing (MEC), non-orthogonal multiple-access (NOMA), resource allocation.

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“…As a result, MEC has been considered as one of the enabling technologies for realizing the future IoTs and the fifth generation (5G) visions which aim at providing massive connectivity, high access speed, and low latency [ 15 ]. The recent multi-access MEC, which can be enabled by the advanced Non-orthogonal multiple access (NOMA) [ 16 , 17 , 18 ], further allows the MTs to acquire computational resources from several ESs simultaneously, and thus yields a flexible exploitation of the computational resources from multiple ESs [ 19 , 20 , 21 ]. Therefore, by integrating MEC into the framework of Blockchain, the computational capability of the MTs can be effectively enhanced, which thus facilitates solving the proof-of-work puzzle and increases the successful probability in winning the mining game [ 22 , 23 ].…”

Section: Introductionmentioning

confidence: 99%

Optimal Computational Power Allocation in Multi-Access Mobile Edge Computing for Blockchain

Chen

Shi

et al. 2018

Sensors

Self Cite

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Blockchain has emerged as a decentralized and trustable ledger for recording and storing digital transactions. The mining process of Blockchain, however, incurs a heavy computational workload for miners to solve the proof-of-work puzzle (i.e., a series of the hashing computation), which is prohibitive from the perspective of the mobile terminals (MTs). The advanced multi-access mobile edge computing (MEC), which enables the MTs to offload part of the computational workloads (for solving the proof-of-work) to the nearby edge-servers (ESs), provides a promising approach to address this issue. By offloading the computational workloads via multi-access MEC, the MTs can effectively increase their successful probabilities when participating in the mining game and gain the consequent reward (i.e., winning the bitcoin). However, as a compensation to the ESs which provide the computational resources to the MTs, the MTs need to pay the ESs for the corresponding resource-acquisition costs. Thus, to investigate the trade-off between obtaining the computational resources from the ESs (for solving the proof-of-work) and paying for the consequent cost, we formulate an optimization problem in which the MTs determine their acquired computational resources from different ESs, with the objective of maximizing the MTs’ social net-reward in the mining process while keeping the fairness among the MTs. In spite of the non-convexity of the formulated problem, we exploit its layered structure and propose efficient distributed algorithms for the MTs to individually determine their optimal computational resources acquired from different ESs. Numerical results are provided to validate the effectiveness of our proposed algorithms and the performance of our proposed multi-access MEC for Blockchain.

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NOMA-Assisted Multi-Access Mobile Edge Computing: A Joint Optimization of Computation Offloading and Time Allocation

Cited by 242 publications

References 43 publications

Load Coupling and Energy Optimization in Multi-Cell and Multi-Carrier NOMA Networks

Load Coupling and Energy Optimization in Multi-Cell and Multi-Carrier NOMA Networks

NOMA-Aided Mobile Edge Computing via User Cooperation

Optimal Computational Power Allocation in Multi-Access Mobile Edge Computing for Blockchain

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