An energy-optimized resource allocation method for joint energy harvesting in edge computing
A technology of energy acquisition and edge computing, applied in the direction of energy consumption reduction, advanced technology, climate sustainability, etc., can solve problems such as unseen MECN research, and achieve the effect of reducing energy consumption
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Embodiment 1
[0131] In this example, figure 1 Shown is a schematic diagram of the mobile edge computing network model of the joint energy harvesting technology. The network includes 30 (N=30) end users, each user has one task, and one MEC server. Task T for end user i i for (D i ,C i ), D i The size of the input data volume for the task, the range is [bits], C i The number of CPU cycles required to execute the task, in the range of [cycles]. where g 0 =-40dB, d 0 =1m,d i is the distance from user i to the edge server, N 0 =-174dB / HZ, B=20KHZ, θ=3, n i = 0.5.
[0132] S1 build scene
[0133] S1-1 assumes that there are 30 end users in the scenario, each user has one task and one edge server.
[0134] Task T for end user i i C i and D i As follows:
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[0136] The distance d from end user i to the mobile edge server i for:
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[0138] The channel gain h from the mobile edge server to end user i i for:
[0139]
[0140] S2 Based on the gradient desce...
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