Dynamic Power Control Method Based on Stacked SRU Network Combined with NoisyNet DQN for CRN

With the rapid development of 5G communication technology, wireless communication equipment has also surged, resulting in a scarcity of spectrum resources. Cognitive radio networks (CRN) offer a solution to this problem by leveraging radio resource management technology and dynamic spectrum access, which can greatly enhance spectrum utilization. This paper addresses the challenge of how secondary users (SUs) can adopt a reasonable power control strategy to maximize their success rate and throughput while sharing the primary user’s spectrum without causing communication interference. To tackle this challenge, we propose a power control algorithm based on a stacked simple recurrent unit network combined with NoisyNet DQN. This approach combines the advantages of fast parallel training of network parameters in simple recurrent units (SRU) with the high exploratory and robustness provided by the noise network. By training the secondary users with this network model, they can independently adjust their transmission power according to the changing environment without interfering with the primary user (PU). This ultimately improves the average success rate and throughput of secondary users sharing the primary user’s spectrum resources.