2020
DOI: 10.1049/el.2020.1495
|View full text |Cite
|
Sign up to set email alerts
|

Novel deep reinforcement learning‐based delay‐constrained buffer‐aided relay selection in cognitive cooperative networks

Abstract: In this Letter, a deep reinforcement learning‐based approach is proposed for the delay‐constrained buffer‐aided relay selection in a cooperative cognitive network. The proposed learning algorithm can efficiently solve the complicated relay selection problem, and achieves the optimal throughput when the buffer size and number of relays are large. In particular, the authors use the deep‐Q‐learning to design an agent to estimate a specific action for each state of the system, which is then utilised to provide an … Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
3

Citation Types

0
3
0

Year Published

2022
2022
2024
2024

Publication Types

Select...
2
2

Relationship

0
4

Authors

Journals

citations
Cited by 4 publications
(3 citation statements)
references
References 13 publications
0
3
0
Order By: Relevance
“…With the development of deep learning (DL), DL has been applied to wireless relay networks [ 33 , 34 , 35 ]. A large number of researchers have started to use deep learning to study buffer-aided relay selection [ 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 ]. The authors in [ 36 ] model the buffer-aided relay selection as a multi-classification problem and uses a deep neural network (DNN) to predict the suitable link to transmit the signals.…”
Section: Related Workmentioning
confidence: 99%
See 2 more Smart Citations
“…With the development of deep learning (DL), DL has been applied to wireless relay networks [ 33 , 34 , 35 ]. A large number of researchers have started to use deep learning to study buffer-aided relay selection [ 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 ]. The authors in [ 36 ] model the buffer-aided relay selection as a multi-classification problem and uses a deep neural network (DNN) to predict the suitable link to transmit the signals.…”
Section: Related Workmentioning
confidence: 99%
“…The comparison experiments in [ 39 ] demonstrated that DQL has better learning results and lower complexity than those of TQL, and the implemented scheme via DQL is more suitable for practical scenarios, as the implemented scheme via DQL could work without prior information. On this basis, the authors in [ 40 , 41 ] realized reliable communications for IoTs [ 40 ] and CRNs [ 41 ] by using the DQL-based buffer-aided relay selection schemes. The authors in [ 40 , 41 ] extend their work further and use the proposed DQL-based buffer-aided relay selection scheme to realize reliable and secure communications in CRNs [ 42 , 43 ].…”
Section: Related Workmentioning
confidence: 99%
See 1 more Smart Citation