Cognitive wireless sensor network spectrum access method based on deep Q learning

Abstract

The invention discloses a cognitive wireless sensor network spectrum access method based on deep Q learning. The cognitive wireless sensor network spectrum access method comprises the following steps:step 1, constructing a Q neural network: selecting a training sample to update a weight parameter of the Q neural network by taking the state values of all channels of a t-2 time slot as an input layer and taking the q values of all channels of a t-1 time slot as an output layer; obtaining an experience sample before the t-1 time slot, calculating the priority, summing the arrangement of the binary tree according to the priority accumulation, and performing sampling to obtain a training sample; step 2, taking the state values of all channels of the t-1 time slot as an input layer, inputting the state values into a Q neural network to obtain q values of all channels of the t time slot, and selecting a channel corresponding to the maximum q value; and step 3, sensing the channel energy, accessing if the state value is idle, and not accessing if the state value is busy. The method has the beneficial effects that the energy consumption is low, the convergence speed is high, the diversityloss of experience samples is avoided, the overfitting phenomenon is avoided, and the prediction accuracy is high.

Description

technical field [0001] The present invention relates to the field of cognitive wireless sensor networks. More specifically, the present invention relates to a spectrum access method for cognitive wireless sensor networks based on deep Q-learning. Background technique [0002] Compared with the traditional mobile communication that mainly solves the problem of human-to-human communication, the fifth-generation mobile communication 5G pays more attention to the communication between things and things, and between people and things. With the development of 5G technology, IoT scenarios represented by smart cities, smart factories, and smart homes are deeply integrated with mobile communications. It is expected that the number of devices connected to 5G networks will reach more than 100 billion. Wireless sensor network (WSN) is an important component network of the sensing layer of the Internet of Things. Its network nodes access the free frequency band near the 2.4G frequency b...

AI Technical Summary

Problems solved by technology

[0004] However, in the traditional DSA scheme, due to the large state space and the local observability of the state, the computational load to obtain the optimal solution is generally high

However, sensor networks require low energy consumption, and most DSA algorithms based on deep learning do not consider energy consumption.

Images