A Method for Maximizing the Rate of Mobile Edge Computing Based on Deep Reinforcement Learning
A reinforcement learning and edge computing technology, applied in the field of communication, can solve the problems of low computing power, reduce the overall network performance, disturbance, etc., and achieve the effect of maximizing the computing rate, prolonging the operation life cycle, and minimizing the energy consumption
Active Publication Date: 2021-06-18
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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[0061] The present invention will be described in further detail below in conjunction with the accompanying drawings.
[0062] refer to figure 1 and figure 2 , a mobile edge computing rate maximization method based on deep reinforcement learning learning, which maximizes the total 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 As shown), an optimal individual calculation mode selection method is proposed to determine which tasks of wireless devices will be offloaded to the base station, and the optimal individual calculation mode selection method includes the following steps (such as figure 2 shown):
[0063] 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 tr...
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A method for maximizing the rate of mobile edge computing based on deep reinforcement learning, including the following steps: 1) In an edge computing system composed of a base station and multiple wireless devices powered by wireless, calculate the system The sum of the rates of all wireless devices in ; 2) Find an optimal mode selection through reinforcement learning algorithm, that is, the mode selection M of all wireless devices 0 and M 1 ; 3) Mode selection M for all wireless devices 0 and M 1 System state x as reinforcement learning t , and action a is the system state x t If the total calculation rate of the modified system is larger than that of the previous one, then 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 , repeat this iterative process until the best mode selection M is obtained 0 and M 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 deep reinforcement learning. 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 deployment of w...
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IPC IPC(8): H04W24/02H04W28/06
CPCH04W24/02H04W28/06Y02D30/70
Inventor 黄亮冯旭钱丽萍吴远
Owner ZHEJIANG UNIV OF TECH