WLAN protocol design and optimization method based on energy harvesting and deep reinforcement learning

Abstract

The invention discloses a WLAN protocol design and optimization method based on energy collection and deep reinforcement learning. AEH‑CSMA / CA protocol; S3, make an optimization decision based on deep reinforcement learning for intelligent STAs in the network. The invention combines the energy collection technology with the wireless communication technology, effectively solves the energy supply problem of the massive devices of the Internet of Things in the future, and realizes the vision of a green network. At the same time, based on deep reinforcement learning technology, intelligent decision-making is made for intelligent STAs in the network to reduce the probability of energy interruption and increase the amount of data transmission, making the wireless network more intelligent.

Description

technical field [0001] The invention belongs to the technical field of wireless communication networks, and particularly designs a WLAN protocol design and optimization method based on energy collection and deep reinforcement learning. Background technique [0002] With the rapid development of wireless communication technology, more and more network devices are gradually joining this Internet of Everything network. In order to better perceive the surrounding environment and make corresponding decisions (such as resource allocation, etc.) to improve communication quality, A large number of tiny sensors are also distributed in spaces that can be seen everywhere. Due to the huge number of devices, in addition to ensuring their communication needs, how to provide continuous energy supply for them has become a relatively serious problem. If the replacement of batteries or active charging is done artificially, due to the relationship between the labor cost consumed and the numbe...

AI Technical Summary

Problems solved by technology

However, the energy status of each device is different, which will also affect its own data transmission status

If the user has more energy, it is possible to try to send more frequently, thus causing unnecessary data collisions and greatly increasing the probability of energy interruption

Due to the complexity of perception of the surrounding environment, it is difficult to use traditional modeling methods

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