C-RAN calculation unloading and resource allocation method based on deep reinforcement learning

A technology of reinforcement learning and resource allocation, applied in computing, computing models, machine learning, etc., can solve problems such as the large distance between the cloud and terminal devices, and the inability of cloud computing services to provide guarantees for low-latency applications, so as to reduce service delays and reduce Energy consumption of user equipment and the effect of improving user service quality

Pending Publication Date: 2019-12-10
NANJING UNIV OF POSTS & TELECOMM
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However, cloud computing services may not provide guarantees for low-latency applicatio

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  • C-RAN calculation unloading and resource allocation method based on deep reinforcement learning

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Embodiment Construction

[0020] The technical solutions of the present invention will be further described in detail below in conjunction with specific embodiments and drawings:

[0021] See figure 1 This embodiment provides a C-RAN computing offloading and resource allocation method based on deep reinforcement learning. The method includes the following steps.

[0022] Step 101: Formulate a computing task, and combine the data size of the computing task and the computing resources required to execute the task into a system state as the input of the deep neural network;

[0023] The calculation task of user u is formulated as: R u ={D u ,C u ,τ u }; where, D u Represents the task data size of user u, C u Represents the number of CPU cycles required to execute the task, τ u Indicates the time that the user can tolerate.

[0024] Step 102: The reward function is Where Y max Indicates the time delay of the user's calculation tasks performed locally, that is, the maximum value of the calculation delay, Is the ...

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Abstract

The invention discloses a C-RAN calculation unloading and resource allocation method based on deep reinforcement learning in the technical field of mobile communication. The method comprises the following steps: 1) firstly constructing a deep reinforcement learning neural network; calculating a task data size and calculation resources required for executing a task; 2) inputting the system state into a deep reinforcement learning model; performing neural network training, and obtaining system actions, 3) enabling the user to unload the calculation task according to the unloading proportionalitycoefficient; enabling the mobile edge computing server to execute a computing task according to the computing resource allocation coefficient; obtaining a reward value of the system action accordingto the reward function; updating neural network parameters according to the reward value; and 4) repeating the above steps until the reward value tends to be stable, completing the training process, and unloading the user computing task and allocating the computing resources of the MEC server according to the final system action. The method can greatly reduce the user service time and energy consumption, so that the real-time low-energy-consumption service becomes possible.

Description

Technical field [0001] The invention relates to a wireless access network resource allocation method, in particular to a wireless access network computing offloading and resource allocation method, and belongs to the technical field of mobile communication. Background technique [0002] With the gradual promotion and application of 5G technology, the design of the radio access network (Cloud radio access network, C-RAN) system requires a more intelligent and flexible architecture to make full use of the performance of the 5G network. C-RAN is a core technology of 5G, which enables various application services to have unprecedented spectrum efficiency and energy efficiency in 5G networks. C-RAN integrates base station infrastructure with cloud computing, and uses optical fiber to connect them with low-latency networks to form a large baseband unit pool. This centralized baseband processing can greatly reduce the number of base stations covering the same area; wireless remote modu...

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Application Information

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IPC IPC(8): H04W24/02G06N20/00
CPCH04W24/02G06N20/00
Inventor 张军靳晓岩
Owner NANJING UNIV OF POSTS & TELECOMM
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