Spectral Efficiency and Energy Efficiency Tradeoff in Multiuser RIS-Aided Mobile Edge Computing Networks
Mobile edge computing (MEC) is emerging as a critical technology for supporting latency-sensitive and computation-intensive services-however, random wireless channel fading limits offloading rates, posing a significant challenge to MEC performance. In MEC systems, effective energy management and hig...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2024-01-01
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| Series: | IEEE Open Journal of the Communications Society |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10752574/ |
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| Summary: | Mobile edge computing (MEC) is emerging as a critical technology for supporting latency-sensitive and computation-intensive services-however, random wireless channel fading limits offloading rates, posing a significant challenge to MEC performance. In MEC systems, effective energy management and high-speed communication links between user devices and MEC servers are essential for supporting services that require low latency and high computation power. Reconfigurable intelligent surfaces (RIS) have been proposed as a promising solution to enhance the quality of communication links between users and MEC servers by dynamically reconfiguring the wireless propagation environment to overcome these challenges. We formulate a trade-off optimization problem to balance SE and EE in RIS-aided MEC systems, which is crucial due to limited system resources and the need for dynamic adaptation to varying network requirements-aimed at joint optimization of transmission power, phase-shift matrix, and MEC offloading and computation delays. Given the problem’s intractability, we develop an alternating optimization-based iterative algorithm incorporating quadratic transformation and successive convex approximation techniques to obtain sub-optimal solutions. Firstly, we address the minimum delay power allocation and task offloading by using quadratic transformations for fractional problems and closed-form solutions. Afterward, we optimize the phase shifts through semidefinite programming and a penalty-based approach. Simulation results validate the effectiveness of the proposed framework, demonstrating significant improvements in SE and EE compared to conventional systems without RIS or with static RIS configurations. |
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| ISSN: | 2644-125X |