Abstract
In cognitive relay networks, the cognitive user opportunistically accesses the authorized spectrum segment of the primary user and simultaneously serves as the data relay node of the primary user while sharing the spectrum resource of the primary user. This not only improves the utilization efficiency of the network spectrum resources but also improves the throughput of the primary users. However, if the primary user randomly selects the relay node, there is no guarantee for an optimal throughput. Moreover, the system power consumption may increase. In order to improve the throughput of cognitive relay network and optimize system utility, this paper proposes a cognitive relay network throughput optimization algorithm based on deep reinforcement learning. For the system model of cognitive relay networks, the Markov decision process is used to describe the channel transition probability of the system model in the paper. The algorithm proposes a cooperative wireless network cooperative relay strategy, analyzes the system outage probability under different transmission modes, and optimizes the system throughput by minimizing the outage probability. Then, the maximum utility optimization strategy based on deep reinforcement learning is proposed to maximize the system utility revenue by selecting the optimal behavior. The experimental results show that the proposed algorithm has a good effect in improving system throughput and optimizing system energy efficiency.
Highlights
Cognitive radio technology allows unauthorized cognitive users to access the spectrum resources of authorized primary users, which is an important technical means to solve the scarcity of spectrum resources
The contributions of this paper are as follows: (1) propose a cooperative relay transmission mode of cognitive wireless networks, and the system interruption probability analysis is carried out for different transmission methods; (2) use the deep reinforcement learning algorithm to learn and explore the state transition information of the system when the state transition probability is unknown; (3) combining system throughput and power optimization problems, a system utility function is proposed to maximize the benefits by selecting the optimal behavior by deep reinforcement learning algorithm; (4) in the experimental scheme, it is proved that the proposed algorithm has better performance in improving system throughput and system utility than cognitive wireless network algorithm based on reinforcement learning or energy overhead minimization
In the Markov decision process (MDP) problem, for the rewards R obtained by the agent after performing a certain behavior, we mainly study the throughput optimization problem of the cognitive wireless network in the model of this paper
Summary
Cognitive radio technology allows unauthorized cognitive users to access the spectrum resources of authorized primary users, which is an important technical means to solve the scarcity of spectrum resources. The scheme could effectively improve the sum rate of SUs. The contributions of this paper are as follows: (1) propose a cooperative relay transmission mode of cognitive wireless networks, and the system interruption probability analysis is carried out for different transmission methods; (2) use the deep reinforcement learning algorithm to learn and explore the state transition information of the system when the state transition probability is unknown; (3) combining system throughput and power optimization problems, a system utility function is proposed to maximize the benefits by selecting the optimal behavior by deep reinforcement learning algorithm; (4) in the experimental scheme, it is proved that the proposed algorithm has better performance in improving system throughput and system utility than cognitive wireless network algorithm based on reinforcement learning or energy overhead minimization.
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