To meet the requirements of computation intensive and latency sensitive applications, mobileedgecomputing (MEC) is utilized to improve quality of service, which allows user equipments (UEs) to offload urgent tasks t...
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ISBN:
(纸本)9781665457293
To meet the requirements of computation intensive and latency sensitive applications, mobileedgecomputing (MEC) is utilized to improve quality of service, which allows user equipments (UEs) to offload urgent tasks to adjacent edge base stations (BSs) for efficient data processing. Considering the deterioration of wireless channels caused by user mobility and obstacles, this paper investigates a relay-assisted MEC system, where a relay is deployed between UE and BS with better channel condition. UE has three modes for data computing, i.e., local computing, relay-assisted offloading, and direct transmission offloading. With the aim of energy consumption minimization, an iterative and low-complexity algorithm is proposed by jointly optimizing task partition, local CPU frequency, bandwidth allocation, and power control at both UE and relay, and the closedform optimal solutions are derived. Numerical results validate that the designed algorithm outperforms static and fractional power control schemes, and the relay-assisted MEC system can significantly decrease energy consumption in comparison with that without the assistance of relay.
In this paper, an energy harvesting-enabled mobileedgecomputing system is investigated. We formulate a longterm energy efficiency (EE) maximization problem under the constraints of network stability. Based on the Ly...
详细信息
ISBN:
(纸本)9781665457293
In this paper, an energy harvesting-enabled mobileedgecomputing system is investigated. We formulate a longterm energy efficiency (EE) maximization problem under the constraints of network stability. Based on the Lyapunov optimization and fractional programming frameworks, an online resource allocation algorithm is proposed to jointly optimizing task partition, CPU frequency, power allocation and energy harvesting strategy at MDs. Simulation results demonstrate the performance advantage of EH-enabled MEC.
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