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Distributed adaptive stable topology generation method based on reinforcement learning

A technology of reinforcement learning and topology generation, which is applied in the field of communication, can solve problems such as not considering the comprehensive influence of links, failure to transmit information in time, and increased energy consumption of nodes, so as to achieve real-time model update, improve stable topology connections, and reduce energy consumption Effect

Active Publication Date: 2020-01-21
XIAN UNIV OF POSTS & TELECOMM
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The existing methods are divided into the following aspects: 1.) Predict the stability degree and network topology of the link connection in the network through the mobility characteristics of the node, and predict the trajectory of the node based on the adaptive neuro-fuzzy system to select the link The node transmits, but a large amount of control information generated between nodes in the prediction process causes excessive energy consumption and large computing overhead; 2.) Collect the received signal strength of the node, and perform deep learning training on it to predict the node's Movement, to build a stable link connection according to the movement trajectory, only considering the relative movement characteristics of the nodes in the process of predicting the position can not reflect the changes of the movement characteristics of the nodes in time, and the collected data only use the movement parameters of a certain period can not reflect well The current movement characteristics of the node; 3.) The method of selecting a stable path according to the received signal strength, dividing the link into two types of strong connection and weak connection according to the average value of the received signal strength of the node within a period of time, and setting the threshold to select a certain threshold However, this method does not consider the comprehensive influence of other factors on the link
[0004] To sum up, in the information collection process of the existing methods, when the number of nodes is large, there will be disadvantages such as network communication blockage, large amount of node calculation, and high energy consumption of nodes. The information cannot be transmitted in time or the information is lost, which leads to the shortcomings of the distributed MANET topology stability prediction method that cannot efficiently predict the link stability.
[0005] The above defects limit the performance of MANET, lead to increased node energy consumption, shortened life cycle and increased network delay, thus affecting the application of link stability prediction methods in MANET

Method used

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  • Distributed adaptive stable topology generation method based on reinforcement learning
  • Distributed adaptive stable topology generation method based on reinforcement learning
  • Distributed adaptive stable topology generation method based on reinforcement learning

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Embodiment 1

[0034] The mobile ad hoc network plays an important role in the communication network without infrastructure. The network has no infrastructure support. Each mobile node has both router and host functions, and can form any network topology through wireless connection. Mobile ad hoc networks have broad application prospects in military communications, mobile networks, connecting personal area networks, emergency services and disaster recovery, and wireless sensor networks. Therefore, the mobile ad hoc network has also become one of the current research hotspots. The mobility of nodes in the mobile ad hoc network causes the network topology formed by the entire wireless channel to change at any time. In order to effectively reduce the impact of dynamic topology changes, the existing methods use the mobility of nodes to predict the link connection in the network. The degree of stability and network topology to reduce the impact of dynamic topology changes. However, the existing ...

Embodiment 2

[0053] The distributed self-adaptive stable topology generation method based on reinforcement learning is the same as embodiment 1, the reinforcement learning described in step 4 in the present invention, the current node receives the received signal strength value (RSSI) of the neighbor node and needs to carry out partition processing, Only when the RSSI value falls into the interval [a,b], adaptive reinforcement learning is required, which specifically includes the following steps:

[0054] Step 4.1 Determine the overall structure of reinforcement learning: the overall structure of the reinforcement learning model is that each mobile node in the interval [a,b] is regarded as an Agent, so that the dynamic changes of the entire network can be considered as a distributed multi-agent agent collaboration system. For each distributed agent Agent, suppose its environment state set is S, action set is A, and the reward function is The action selection strategy is π(s i ,a j ). ...

Embodiment 3

[0076] The distributed self-adaptive stable topology generation method based on reinforcement learning is the same as embodiment 1-2, and the update formula of the adaptive interval described in step 6 of the present invention is as follows:

[0077]

[0078] In the formula: a is the upper boundary of the interval; b is the lower boundary of the interval; RSSI is the received signal strength indicator value of the neighbor node; s' is the actual connection variable state of the node and the neighbor node at the next moment; It is the prediction of the joint variable state between the node and the neighbor node at the next moment. In the self-adaptive interval update process of the present invention, the first condition that needs to be satisfied is It indicates that the prediction of the current node does not match the actual connection variable state, and it also indicates that the node has made an error during the boundary adjustment process. On this basis, when the RS...

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Abstract

The invention discloses a distributed adaptive stable topology generation method based on reinforcement learning, and solves the problem of poor link node connection continuity of a mobile ad hoc network in a routing process. The method specifically comprises the following steps: constructing a node dynamic topology in the mobile ad hoc network; dividing an adaptive reinforcement learning intervaland initializing a Q value table; enabling the received signal intensity values to be subjected to interval processing; carrying out reinforcement learning and connection state stability judgment ina self-adaptive interval; judging a direct decision interval state; updating an adaptive interval boundary; and generating a distributed adaptive stable connection topology. According to the method, the received signal strength value is combined with the reinforcement learning method and the adaptive interval method, so that a stable topology link in the dynamic topology change process of the mobile ad hoc network is accurately realized, the energy consumption of nodes is reduced, the relatively high network overhead is avoided, the learning rate is high, and the complexity is low. The methodis used for mobile ad hoc network distributed topology generation.

Description

technical field [0001] The invention belongs to the technical field of communication, and relates to stable topology generation of a mobile ad hoc network, in particular to a distributed self-adaptive stable topology generation method based on reinforcement learning of a mobile ad hoc network, which is used in a mobile ad hoc distributed network. Background technique [0002] Mobile Ad hoc networks (MANET) integrates wireless communication technology, embedded computing technology, sensor technology, distributed information processing technology, etc., and can coordinate information collection and transmission in different scenarios through integrated mobile receiving devices. . When the mobile ad hoc network operates under poor environmental conditions, it is often affected by external factors and mobile node factors, especially in the case of limited energy and storage resources and high mobility, how to reduce energy consumption And enhancing the communication quality of...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): H04L12/24H04W84/18
CPCH04L41/12H04L41/145H04L41/147H04L41/0823H04W84/18Y02D30/70
Inventor 黄庆东石斌宇蒋彦渊
Owner XIAN UNIV OF POSTS & TELECOMM