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The model-based power allocation algorithm has been investigated for decades, but it requires the mathematical models to be analytically tractable and it usually has high computational complexity.
Bottomley, G.E, and Croft, “Jakes fading model revisited,” Electron. Lett. , vol. 29, no. 13, pp. 1162–1163, Jun. 1993
1993
Earlier work this paper cites.
R. S. Sutton, D. A. McAllester, S. P. Singh, and Y. Mansour, “Policy gradient methods for reinforcement learning with function approximation,” Adv. Neural inf. Process. Syst. , pp. 1057–1063, 2000
2000
Earlier work this paper cites.
C. M. Bishop, Pattern Recognition and Machine Learning (Information Science and Statistics) . Springer-Verlag New York, Inc., 2006
2006
Earlier work this paper cites.
Z. Q. Luo and S. Zhang, “Dynamic spectrum management: Complexity and duality,” IEEE J. Sel. Topics Signal Process. , vol. 2, no. 1, pp. 57–73, Feb. 2008
2008
Earlier work this paper cites.
M. Chiang, P. Hande, T. Lan, and C. W. Tan, “Power control in wireless cellular networks,” Found. Trends Netw. , vol. 2, no. 4, pp. 381–533, 2008
2008
Earlier work this paper cites.
N. H. Viet, N. A. Vien, and T. Chung, “Policy gradient SMDP for resource allocation and routing in integrated services networks,” in 2008 IEEE Int. Conf. Netw., Sens., Control , 2008, pp. 1541–1546
2008
Earlier work this paper cites.
L. R. Busoniu, R. Babuska, B. D. Schutter, and D. Ernst, Reinforcement Learning and Dynamic Programming Using Function Approximators . CRC Press, Inc., 2010
2010
Earlier work this paper cites.
M. Bennis and D. Niyato, “A q-learning based approach to interference avoidance in self-organized femtocell networks,” in 2010 IEEE Globecom Workshops , 2010, pp. 706–710
2010
Earlier work this paper cites.
Q. Shi, M. Razaviyayn, Z. Q. Luo, and C. He, “An iteratively weighted mmse approach to distributed sum-utility maximization for a mimo interfering broadcast channel,” in IEEE Int. Conf. Acoust., Speech, Signal Process. , 2011, pp. 4331–4340
2011
Earlier work this paper cites.
H. Zhang, L. Venturino, N. Prasad, P. Li, S. Rangarajan, and X. Wang, “Weighted sum-rate maximization in multi-cell networks via coordinated scheduling and discrete power control,” IEEE J. Sel. Areas Commun. , vol. 29, no. 6, pp. 1214–1224, Jun. 2011
2011
Earlier work this paper cites.
M. Simsek, A. Czylwik, A. Galindo-Serrano, and L. Giupponi, “Improved decentralized q-learning algorithm for interference reduction in lte-femtocells,” in 2011 Wireless Adv. , 2011, pp. 138–143
2011
Earlier work this paper cites.
F. Boccardi, R. W. Heath, A. Lozano, T. L. Marzetta, and P. Popovski, “Five disruptive technology directions for 5G,” IEEE Commun. Mag. , vol. 52, no. 2, pp. 74–80, Feb. 2013
2013
Earlier work this paper cites.
W. Yu, T. Kwon, and C. Shin, “Multicell coordination via joint scheduling, beamforming, and power spectrum adaptation,” IEEE Trans. Wireless Commun. , vol. 12, no. 7, pp. 3300–3313, Jul. 2013
2013
Earlier work this paper cites.
R. Q. Hu and Y. Qian, “An energy efficient and spectrum efficient wireless heterogeneous network framework for 5G systems,” IEEE Commun. Mag. , vol. 52, no. 5, pp. 94–101, May 2014
2014
Cited alongside, same era.
2014
Cited alongside, same era.
H. Zhang, X. Chu, W. Guo, and S. Wang, “Coexistence of wi-fi and heterogeneous small cell networks sharing unlicensed spectrum,” IEEE Commun. Mag. , vol. 53, no. 3, pp. 158–164, Mar. 2015
2015
Cited alongside, same era.
V. Mnih, K. Kavukcuoglu, D. Silver, A. A. Rusu, J. Veness, M. G. Bellemare, A. Graves, M. Riedmiller, A. K. Fidjeland, and G. Ostrovski, “Human-level control through deep reinforcement learning.” Nature , vol. 518, no. 7540, p. 529, Feb. 2015
2015
Cited alongside, same era.
K. Shen and W. Yu, “Fractional programming for communication systems - Part I: Power control and beamforming,” IEEE Trans. Signal Process. , vol. 66, no. 10, pp. 2616–2630, May 2018
2018
Later among the works it cites.
F. Meng, P. Chen, L. Wu, and X. Wang, “Automatic modulation classification: A deep learning enabled approach,” IEEE Trans. Veh. Technol. , vol. 67, no. 11, pp. 10 760–10 772, Nov. 2018
2018
Later among the works it cites.
H. Ye, G. Y. Li, and B. Juang, “Power of deep learning for channel estimation and signal detection in OFDM systems,” IEEE Wireless Commun. Lett. , vol. 7, no. 1, pp. 114–117, Feb. 2018
2018
Later among the works it cites.
F. Meng, P. Chen, and L. Wu, “NN-based IDF demodulator in band-limited communication system,” IET Commun. , vol. 12, no. 2, pp. 198–204, Feb. 2018
2018
Later among the works it cites.
R. Sutton and A. Barto, Reinforcement Learning: An Introduction . MIT Press, 2018
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M. Simsek, M. Bennis, and I. Güvenç, “Learning based frequency- and time-domain inter-cell interference coordination in hetnets,” IEEE Trans. Veh. Technol. , vol. 64, no. 10, pp. 4589–4602, Oct. 2015
2015
Cited alongside, same era.
T. P. Lillicrap, J. J. Hunt, A. Pritzel, N. Heess, T. Erez, Y. Tassa, D. Silver, and D. Wierstra, “Continuous control with deep reinforcement learning,” Comput. Sci. , vol. 8, no. 6, p. A187, 2015
2015
Cited alongside, same era.
I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning . The MIT Press, 2016
2016
Cited alongside, same era.
T. Oshea, J. Hoydis, T. Oshea, and J. Hoydis, “An introduction to deep learning for the physical layer,” IEEE Trans. Cogn. Commun. Netw. , vol. 3, no. 4, pp. 563–575, Dec. 2017
2017
Cited alongside, same era.
T. Wang, C. K. Wen, H. Wang, F. Gao, T. Jiang, and S. Jin, “Deep learning for wireless physical layer: Opportunities and challenges,” China Commun. , vol. 14, no. 11, pp. 92–111, Nov. 2017
2017
Cited alongside, same era.
2017
Cited alongside, same era.
D. Silver, J. Schrittwieser, K. Simonyan, I. Antonoglou, A. Huang, A. Guez, T. Hubert, L. Baker, M. Lai, and A. Bolton, “Mastering the game of Go without human knowledge,” Nature , vol. 550, no. 7676, pp. 354–359, Oct. 2017
2017
Cited alongside, same era.
E. Ghadimi, F. D. Calabrese, G. Peters, and P. Soldati, “A reinforcement learning approach to power control and rate adaptation in cellular networks,” in 2017 IEEE Int. Conf. Commun (ICC) , May 2017, pp. 1–7
2017
Cited alongside, same era.
2018
Later among the works it cites.
H. Sun, X. Chen, Q. Shi, M. Hong, X. Fu, and N. D. Sidiropoulos, “Learning to optimize: Training deep neural networks for interference management,” IEEE Trans. Signal Process. , vol. 66, no. 20, pp. 5438–5453, Oct. 2018
2018
Later among the works it cites.
2018
Later among the works it cites.
R. Amiri, H. Mehrpouyan, L. Fridman, R. K. Mallik, A. Nallanathan, and D. Matolak, “A machine learning approach for power allocation in hetnets considering QoS,” in 2018 IEEE Int. Conf. Commun (ICC) , 2018, pp. 1–7
2018
Later among the works it cites.
2018
Later among the works it cites.
H. Ye and G. Y. Li, “Deep reinforcement learning based distributed resource allocation for V2V broadcasting,” in 2018 14th Int. Wireless Commun. Mobile Comput. Conf. (IWCMC) , 2018, pp. 440–445
2018
Later among the works it cites.
Y. Wei, F. R. Yu, M. Song, and Z. Han, “User scheduling and resource allocation in hetnets with hybrid energy supply: An actor-critic reinforcement learning approach,” IEEE Trans. Wireless Commun. , vol. 17, no. 1, pp. 680–692, Jan. 2018
2018
Later among the works it cites.
2018
Later among the works it cites.
F. D. Calabrese, L. Wang, E. Ghadimi, G. Peters, L. Hanzo, and P. Soldati, “Learning radio resource management in RANs: Framework, opportunities, and challenges,” IEEE Commun. Mag. , vol. 56, no. 9, pp. 138–145, Sep. 2018
2018
Later among the works it cites.