National University of Defense Technology

Yongxiang Liu

Fuqiang Yao

Jianzhao Zhang

Linghui Zeng

The present disclosure provides a spectrum access method and system using prior knowledge-based double-action reinforcement learning, and belongs to the technical field of electromagnetic spectrum. The method includes evaluating and screening prior knowledge, initializing a Q-table, and confirming a current state; and performing Q-learning by: firstly, decomposing an action space into two dimensions with an action in one dimension defined as a channel chosen by an agent, and an action in the other dimension defined as a number of time slots of an access channel, and choosing actions in turn according to the dimensions; then performing spectrum access according to the actions chosen; and finally, updating the Q-table in combination with biased information, wherein the biased information is a reward value. The system is configured to implement the proposed method. By adoption of the method, better performance is achieved, and the efficiency of spectrum access can be improved.

Spectrum access method and system using prior knowledge-based double-action reinforcement learning

A dynamic spectrum sharing method based on user online learning and low-overhead cooperation is provided. In the method, a secondary user (SU) obtains spectrum sharing scene information and requirement parameters; then initializes state parameters and spectrum sensing parameters of a channel. At the beginning of each slot, the SU determines whether a direct transmission condition is met according to the transmission condition of the previous slot, and directly continues transmission on the channel of the previous slot or obtains an idle channel according to the channel state and spectrum sensing for transmission, and updates state parameter and spectrum sensing parameter. Finally, an SU base station performs conflict coordination according to the channel occupancy condition for a fixed number of slots. Hence, low-overhead and efficient spectrum sharing is realized by the SU learning the channel state and the transmission condition, such that the spectrum utilization rate in a high user density and dynamic spectrum environment can be effectively improved.

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Nanjing, China

Yongxiang Liu