scholarly journals Joint Channel Selection and Data Scheduling in HF Jamming Environment: An Interference-Aware Reinforcement Learning Approach

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 157072-157084
Author(s):  
Wen Li ◽  
Yuhua Xu ◽  
Qiuju Guo ◽  
Yuli Zhang ◽  
Xin Liu ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Channel Selection ◽  
Learning Approach ◽  
Data Scheduling ◽  
Joint Channel

scholarly journals Joint relay and channel selection against mobile and smart jammer: A deep reinforcement learning approach

2021 ◽  
Author(s):  
Hongcheng Yuan ◽  
Fei Song ◽  
Xiaojing Chu ◽  
Wen Li ◽  
Ximing Wang ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Channel Selection ◽  
Learning Approach

scholarly journals Deep Reinforcement Learning for Channel Selection and Power Allocation in D2D Communications

2021 ◽  
Vol 2082 (1) ◽  
pp. 012003
Author(s):  
Jun Zhou
Keyword(s):  
Reinforcement Learning ◽  
Power Allocation ◽  
Dynamic Environment ◽  
Channel Selection ◽  
Spectrum Efficiency ◽  
D2d Communication ◽  
Maximization Problem ◽  
Multi Agent ◽  
Sum Rate ◽  
Joint Channel

Abstract Device-to-device (D2D) communication is regarded as a key technical component of the fifth-generation (5G), D2D communication usually reuses spectrum resources with cellular users (CUs). To mitigate interference to cellular links and improve spectrum efficiency, this paper investigates a sum-rate maximization problem in the underlay of D2D communication. Particularly, a joint channel selection and power allocation framework based on multi-agent deep reinforcement learning is proposed, named Double Deep Q-Network (DDQN). It can adeptly select the channel and allocate power in a dynamic environment. The proposed scheme only requires local information and some outdated nonlocal information, which reduces signaling overheads significantly. Simulation results show that the proposed scheme can improve the D2D sum rate and ensure quality-of-service (QoS) of CUs compared with other benchmarks.

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