Wireless Internet-of-Things resource allocation method based on probability transfer deep reinforcement learning

Abstract

The invention discloses a wireless Internet-of-Things resource allocation method based on probability transfer deep reinforcement learning. According to the method, a decision agent is placed in eachedge server in a distributed manner, so that each agent only needs to make a decision on the user served by the agent, the service migration model based on the distributed partially observable Markovdecision process is provided, and the problem that the decision cannot reach the optimal solution due to the fact that the state information observable by each agent is limited is solved.

Description

technical field [0001] The invention relates to the technical field of networks and Internet of Things, in particular to a method for allocating wireless Internet of Things resources based on probabilistic transfer deep reinforcement learning. Background technique [0002] MEC (Multi-access Edge Computing) is an edge cloud platform that provides a new network architecture by combining with the operator's network (the data plane function is the point of integration), and uses the wireless access network to provide nearby IT services required by telecom users and cloud computing functions, thereby creating a carrier-class service environment with high performance, low latency and high bandwidth, so that consumers can enjoy high-quality business experience. [0003] Such as figure 1 As shown, the MEC network of an application scenario consists of N car users driving in a fixed direction M base stations and the edge server to which each base station belongs composition. ...

AI Technical Summary

Problems solved by technology

[0005] (1) The decision variable space will increase linearly with the increase of the number of users, making it difficult for the algorithm to converge

[0006] (2) The increase of decision variables will also increase the decision-making delay sharply, which cannot guarantee the real-time performance of the system

[0007] (3) Global information is required for each decision, so the system must have a central node to collect the status information of all devices in real time, which will undoubtedly increase the transmission burden of the network and the status information search delay

But the ensuing problem is that the state information that each agent can observe is limited, so that the decision-making cannot reach the optimal solution.

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