Method for Maximizing Mobile Edge Computing Rate Based on Deep Deterministic Policy Gradients
An edge computing and deterministic technology, applied in the field of communication, can solve problems such as reducing overall network performance, disrupting, and prolonging the operating life cycle of wireless devices
Active Publication Date: 2021-04-06
ZHEJIANG UNIV OF TECH
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Problems solved by technology
In an IoT network, a large number of wireless devices (WDs) capable of communication and computing are deployed. Due to device size constraints and production cost considerations, IoT devices (such as sensors) often carry batteries with limited capacity and energy-saving low Performance processors, therefore, limited device lifetime and low computing power cannot support the growing number of sustainable new applications requiring high-performance computing, such as autonomous driving and augmented reality
The deployment of wireless power transfer systems (WPT) can solve the two aforementioned performance problems, but frequent device battery failures not only disrupt the normal operation of individual wireless devices but also significantly degrade the overall network performance, for example, in wireless sensor networks Sensing accuracy
Traditional wireless systems require frequent manual battery replacement, which is expensive and inconvenient. Due to strict battery capacity constraints, in battery-powered wireless systems, minimizing energy consumption and prolonging the operating life cycle of wireless devices is a key design
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[0063] The present invention will be described in further detail below in conjunction with the accompanying drawings.
[0064] refer to figure 1 with figure 2 , a deep deterministic policy gradient-based method for maximizing the computing rate of mobile edge, which maximizes the sum computing rate of all wireless devices, minimizes energy consumption, and prolongs the operating life cycle of wireless devices. The present invention is based on a system model of multiple wireless devices (such as figure 1 Shown), an optimal individual computation mode selection method is proposed to decide which wireless devices tasks will be offloaded to the base station. The optimal individual calculation mode selection method includes the following steps (such as figure 2 shown):
[0065] 1) In an edge computing system composed of a base station and multiple wireless devices powered by wireless, the base station and each wireless device have a separate antenna; the RF energy transmitte...
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A mobile edge computing rate maximization method based on a deep deterministic policy gradient method, comprising the following steps: 1) Calculate the sum of the rates of all wireless devices in the system under a given mode selection; 2) Set all wireless devices; 3) The problem of maximizing the sum of calculation rates of all wireless devices; 4) Finding an optimal mode selection through a deep deterministic policy gradient method; 5) The mode selection M of all wireless devices 0 and M 1 State x as a deep deterministic policy gradient method t , action a is to state x t After the change, the total calculation rate of the system will be compared with a set standard value. If it is greater than this standard value, the current reward r(x t , a) is set to a positive value, otherwise it is set to a negative value, and the system enters the next state x t+1 . The present invention maximizes the total calculation rate of all wireless devices under the premise of ensuring user experience.
Description
technical field [0001] The invention belongs to the field of communication, and in particular relates to a communication system for mobile edge computing and a method for maximizing the rate of mobile edge computing based on a deep deterministic policy gradient method. Background technique [0002] Recent developments in IoT technology are a critical step towards truly intelligent and autonomous control, especially in many important industrial and commercial systems. In an IoT network, a large number of wireless devices (WDs) capable of communication and computing are deployed. Due to device size constraints and production cost considerations, IoT devices (such as sensors) often carry batteries with limited capacity and energy-saving low Performance processors, therefore, limited device lifetime and low computing power cannot support the growing number of sustainable new applications requiring high-performance computing, such as autonomous driving and augmented reality. The...
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IPC IPC(8): H04W24/02H04W28/06H04W28/10
CPCH04W24/02H04W28/06H04W28/10
Inventor 黄亮冯旭钱丽萍吴远
Owner ZHEJIANG UNIV OF TECH