Joint Transaction Transmission and Channel Selection in Cognitive Radio Based Blockchain Networks: A Deep Reinforcement Learning Approach

C. J. Watkins and P. Dayan, “Q-learning,” Machine learning , vol. 8, no. 3-4, pp. 279–292, May 1992

1992

Earlier work this paper cites.

G. O. Karame, E. Androulaki, and S. Capkun, “Double-spending fast payments in bitcoin,” in ACM conference on Computer and communications security , Raleigh, NC, October 2012, pp. 906–917

2012

Earlier work this paper cites.

M. Rosenfeld, “Analysis of hashrate-based double spending,” arXiv preprint arXiv:1402.2009 , 2014

Original

2014

Earlier work this paper cites.

V. Mnih, K. Kavukcuoglu, D. Silver, A. A. Rusu, J. Veness, M. G. Bellemare, A. Graves, M. Riedmiller, A. K. Fidjeland, G. Ostrovski et al. , “Human-level control through deep reinforcement learning,” Nature , vol. 518, no. 7540, pp. 529–533, 2015

2015

Earlier work this paper cites.

O. Schrijvers, J. Bonneau, D. Boneh, and T. Roughgarden, “Incentive compatibility of bitcoin mining pool reward functions,” in International Conference on Financial Cryptography and Data Security . Barbados: Springer, May 2016, pp. 477–498

2016

Cited alongside, same era.

A. A. Khan, M. H. Rehmani, and A. Rachedi, “Cognitive-radio-based internet of things: Applications, architectures, spectrum related functionalities, and future research directions,” IEEE wireless communications , vol. 24, no. 3, pp. 17–25, June 2017

2017

Cited alongside, same era.

R. Neisse, G. Steri, and I. Nai-Fovino, “A blockchain-based approach for data accountability and provenance tracking,” in Proceedings of the 12th International Conference on Availability, Reliability and Security . Reggio Calabria, Italy: ACM, August 2017, pp. 93–98

2017

Cited alongside, same era.

B. Liu, X. L. Yu, S. Chen, X. Xu, and L. Zhu, “Blockchain based data integrity service framework for iot data,” in IEEE International Conference on Web Services (ICWS) , Honolulu, HI, June 2017, pp. 468–475

2017

(2018, Sept.) Explaining bitcoin transaction fees. [Online]. Available: https://support.blockchain.com/hc/en-us/articles/

2018

Closest in time.

S. Wang, H. Liu, P. H. Gomes, and B. Krishnamachari, “Deep reinforcement learning for dynamic multichannel access in wireless networks,” IEEE Transactions on Cognitive Communications and Networking , vol. 4, no. 2, pp. 257–265, Jun. 2018

2018

Closest in time.

H. Van Hasselt, A. Guez, and D. Silver, “Deep reinforcement learning with double q-learning,” in Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence . Phoenix, AZ, February 2016, pp. 2094–2100

Original

2094

Closest in time.