Spectrum Access Method for Cognitive Wireless Sensor Networks Based on Deep Q-Learning

Abstract

The invention discloses a spectrum access method for cognitive wireless sensor network based on deep Q-learning. The q value of all channels in the 1 slot is the output layer, and the training samples are selected to update the weight parameters of the Q neural network; the experience samples before the t-1 slot are obtained and the priority is calculated, and the binomial tree arrangement is accumulated and summed according to the priority, and the sampling Obtain a training sample; Step 2, take the state value of all channels in the t-1 time slot as the input layer, input it to the Q neural network, obtain the q value of all channels in the t time slot, and select the channel corresponding to the largest q value; step 3. Sensing the energy of the channel, if the status value is "idle", it will be connected, and if the status value is "busy", it will not be connected. The invention has the beneficial effects of low energy consumption, fast convergence speed, avoiding loss of diversity of experience samples, avoiding overfitting phenomenon and high prediction accuracy.

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.

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