Method for maximizing mobile edge calculation rate based on depth deterministic policy gradient method
An edge computing, deterministic technology, applied in the field of communication, which can solve the problems of prolonging the operation life cycle of wireless devices, inconvenience, and disturbance.
Active Publication Date: 2018-11-02
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 and figure 2 , a mobile edge computing rate maximization method based on a deep deterministic policy gradient method, 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 transmit...
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The invention relates to a method for maximizing a mobile edge calculation rate based on a depth deterministic policy gradient method, which comprises the following steps: 1) figured out is a sum of rates of all wireless devices with mode selection given; 2) a set of all wireless devices; 3) maximization problem of the sum of the calculation rate of all the wireless devices; 4) an optimal mode selection is found through a depth deterministic policy gradient method; 5) the mode selection M0 and M1 of all the wireless devices is used as the state xt of the depth deterministic policy gradient method; action a is a change to the state xt; after the change, the total calculation rate of the system is compared with a set standard value; if larger than the standard value, the current reward r (xt, a) is set to be a positive value; otherwise, the current reward is set to be a negative value; meanwhile, the system enters a next state xt+1. The method for maximizing a mobile edge calculation rate based on a depth deterministic policy gradient method in the present invention maximizes the sum calculation rate of all the wireless devices while ensuring the 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