Abstract
Dynamic spectrum access (DSA) has been considered as a promising technology to address spectrum scarcity and improve spectrum utilization. Normally, the channels are related to each other. Meanwhile, collisions will be inevitably caused by communicating between multiple PUs or multiple SUs in a real DSA environment. Considering these factors, the deep multi-user reinforcement learning (DMRL) is proposed by introducing the cooperative strategy into dueling deep Q network (DDQN). With no demand of prior information about the system dynamics, DDQN can efficiently learn the correlations between channels, and reduce the computational complexity in the large state space of the multi-user environment. To reduce the conflicts and further maximize the network utility, cooperative channel strategy is explored by utilizing the acknowledge (ACK) signals without exchanging spectrum information. In each time slot, each user selects a channel and transmits a packet with a certain probability. After sending, ACK signals are utilized to judge whether the transmission is successful or not. Compared with other popular models, the simulation results show that the proposed DMRL can achieve better performance on effectively enhancing spectrum utilization and reducing conflict rate in the dynamic cooperative spectrum sensing.
Highlights
Due to the increasing demand for wireless communication, spectrum resources become severely scarce, which has promoted demand of developing high-efficiency dynamic spectrum access (DSA) schemes [1,2,3]
multi-agent reinforcement learning (MARL) and multi-agent deep reinforcement learning (MADRL) are chosen as compared approaches to verify the performance of the proposed deep multi-user reinforcement learning (DMRL)
This paper studies the multichannel access problem by proposing deep multi-user reinforcement learning
Summary
Due to the increasing demand for wireless communication, spectrum resources become severely scarce, which has promoted demand of developing high-efficiency dynamic spectrum access (DSA) schemes [1,2,3]. To the high complexity in DSA, we develop a dueling deep Q-network approach, known whichCompared has the ability determine best action for each state in theshow largethat dynamic withtoMARL and the MADRL, the simulation results the proposed unknown environments. Compared with MARL and MADRL, the simulation results show that the proposed our method can effectively achieve the dynamic cooperative spectrum sensing, and sig DMRL model can obtain a higher average reward and a lower average collision. It means nificantly the chance of conflict between. PUs and SUs. sensing, and signifiour methodreduce can effectively achieve the dynamic cooperative spectrum cantly reduce the chance of conflict between PUs and SUs
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
