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Age of Information (AoI) measures the freshness of the information at a remote location.
V. Mnih, A. P. Badia, M. Mirza, A. Graves, T. Lillicrap, T. Harley, D. Silver, and K. Kavukcuoglu, “Asynchronous methods for deep reinforcement learning,” in International conference on machine learning , 2016, pp. 1928–1937
1937
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R. S. Sutton, D. A. McAllester, S. P. Singh, and Y. Mansour, “Policy gradient methods for reinforcement learning with function approximation,” in Advances in neural information processing systems , 2000, pp. 1057–1063
2000
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R. S. Sutton and A. G. Barto, “Reinforcement learning: An introduction,” 2011
2011
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S. Kaul, R. Yates, and M. Gruteser, “Real-time status: How often should one update?” in 2012 Proceedings IEEE INFOCOM , March 2012, pp. 2731–2735
2012
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H. Riiser, P. Vigmostad, C. Griwodz, and P. Halvorsen, “Commute path bandwidth traces from 3g networks: analysis and applications,” in Proceedings of the 4th ACM Multimedia Systems Conference . ACM, 2013, pp. 114–118
2013
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M. Abadi, P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean, M. Devin, S. Ghemawat, G. Irving, M. Isard et al. , “Tensorflow: a system for large-scale machine learning.” in OSDI , vol. 16, 2016, pp. 265–283
2016
Cited alongside, same era.
“Federal Communications Commission. 2016. Raw Data - Measuring Broadband America. (2016).” https://www.fcc.gov/reports-research/reports/ measuring- broadband- america/raw- data- measuring- broadband- america- 2016
2016
Cited alongside, same era.
H. Mao, R. Netravali, and M. Alizadeh, “Neural adaptive video streaming with pensieve,” in Proceedings of the Conference of the ACM Special Interest Group on Data Communication . ACM, 2017, pp. 197–210
2017
Cited alongside, same era.
I. Kadota, A. Sinha, and E. Modiano, “Optimizing age of information in wireless networks with throughput constraints,” in IEEE INFOCOM 2018 - IEEE Conference on Computer Communications , April 2018, pp. 1844–1852
2018
Cited alongside, same era.
C. Kam, S. Kompella, G. D. Nguyen, J. E. Wieselthier, and A. Ephremides, “On the age of information with packet deadlines,” IEEE Transactions on Information Theory , vol. 64, no. 9, pp. 6419–6428, Sept 2018
2018
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A. Kosta, N. Pappas, A. Ephremides, and V. Angelakis, “Age of Information and Throughput in a Shared Access Network with Heterogeneous Traffic,” ArXiv e-prints , Jun. 2018
2018
Closest in time.
M. K. Abdel-Aziz, C.-F. Liu, S. Samarakoon, M. Bennis, and W. Saad, “Ultra-Reliable Low-Latency Vehicular Networks: Taming the Age of Information Tail,” ArXiv e-prints , nov 2018
2018
Closest in time.
Y. Sun, M. Peng, Y. Zhou, Y. Huang, and S. Mao, “Application of Machine Learning in Wireless Networks: Key Techniques and Open Issues,” ArXiv e-prints , Sep. 2018
2018
Closest in time.
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