Paper Info
Title
Joint Transaction Transmission And Channel Selection In Cognitive Radio Based Blockchain Networks: A Deep Reinforcement Learning Approach
Abstract
To ensure that the data aggregation, data storage, and data processing are all performed in a decentralized but trusted manner, we propose to use the blockchain with the mining pool to support IoT services based on cognitive radio networks. As such, the secondary user can send its sensing data, i.e., transactions, to the mining pools. After being verified by miners, the transactions are added to the blocks. However, under the dynamics of the primary channel and the uncertainty of the mempool state of the mining pool, it is challenging for the secondary user to determine an optimal transaction transmission policy. In this paper, we propose to use the deep reinforcement learning algorithm to derive an optimal transaction transmission policy for the secondary user. Specifically, we adopt a Double Deep-Q Network (DDQN) that allows the secondary user to learn the optimal policy. The simulation results clearly show that the proposed deep reinforcement learning algorithm outperforms the conventional Q-learning scheme in terms of reward and learning speed.
Year
DOI
Venue
2018
10.1109/icassp.2019.8683228
2019 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP)
Keywords
Field
DocType
Cognitive radio, blockchain, IoT, channel access, deep reinforcement learning
Data processing,Primary channel,Computer science,Computer data storage,Communication channel,Computer network,Database transaction,Data aggregator,Distributed computing,Cognitive radio,Reinforcement learning
Journal
Volume
ISSN
Citations 
abs/1810.10139
1520-6149
3
PageRank 
References 
Authors
0.38
0
6
Name
Order
Citations
PageRank
nguyen cong luong11685.98
Tran The Anh2122.96
Huynh Thi Thanh Binh38324.97
Niyato Dusit49486547.06
Dong In Kim53784220.90
Liang Ying-Chang610007593.03