Research on Energy Consumption Minimization for a Data Compression Based NOMA-MEC System

SHI Liqin; LIU Xuan; LU Guangyue

doi:10.11999/JEIT231033

Volume 46 Issue 7

Jul. 2024

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Article Navigation > Journal of Electronics & Information Technology > 2024 > 46(7): 2888-2897

Wen Xinhui, Niu Mingji . THE ABS LEARNING ALGORITHM FOR RADIAL BASIS FUNCTIONAL NETWORK[J]. Journal of Electronics & Information Technology, 1996, 18(6): 601-606.

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SHI Liqin, LIU Xuan, LU Guangyue. Research on Energy Consumption Minimization for a Data Compression Based NOMA-MEC System[J]. Journal of Electronics & Information Technology, 2024, 46(7): 2888-2897. doi: 10.11999/JEIT231033

Wen Xinhui, Niu Mingji . THE ABS LEARNING ALGORITHM FOR RADIAL BASIS FUNCTIONAL NETWORK[J]. Journal of Electronics & Information Technology, 1996, 18(6): 601-606.

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Research on Energy Consumption Minimization for a Data Compression Based NOMA-MEC System

doi: 10.11999/JEIT231033

Shaanxi Key Laboratory of Information Communication Network and Security, Xi’an University of Posts and Telecommunications Xi’an 710121, China

Funds: The National Natural Science Foundation of China (62301421)

Received Date: 2023-09-19
Rev Recd Date: 2024-03-15

Available Online: 2024-04-02

Publish Date: 2024-07-29

Abstract

Abstract

The system energy consumption minimization problem is studied for a data compression based Non-Orthogonal Multiple Access-Mobile Edge Computing (NOMA-MEC) system. Considering the partial compression and offloading schemes and the limited computation capacity at the base station, a system energy consumption minimization optimization problem is formulated by jointly optimizing the users’ data compression and offloading ratios, transmit power, data compression time, etc. In order to solve this problem, closed-form expression of each user’s optimal transmit power is firstly derived. Then the Successive Convex Approximation (SCA) method is used to approximate the non-convex constraints of the formulated problem, and An SCA based efficient iterative algorithm is proposed to solve the formulated problem, obtaining the optimal resource allocation scheme of the system. Finally, the simulation results verify the advantages of the proposed scheme via computer simulations and show that compared with other benchmark schemes, the proposed scheme can effectively reduce the system energy consumption.
- Partial compression,
- Resource allocation,
- Energy consumption minimization,
- NOMA

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References(14)

References

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