Power Control Algorithm Based on Q-Learning in Femtocell

Yun LI; Ying TANG; Hanxiao LIU

doi:10.11999/JEIT181191

Volume 41 Issue 11

Nov. 2019

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Article Navigation > Journal of Electronics & Information Technology > 2019 > 41(11): 2557-2564

Yun LI, Ying TANG, Hanxiao LIU. Power Control Algorithm Based on Q-Learning in Femtocell[J]. Journal of Electronics & Information Technology, 2019, 41(11): 2557-2564. doi: 10.11999/JEIT181191

Citation:

Yun LI, Ying TANG, Hanxiao LIU. Power Control Algorithm Based on Q-Learning in Femtocell[J]. Journal of Electronics & Information Technology, 2019, 41(11): 2557-2564. doi: 10.11999/JEIT181191

Yun LI, Ying TANG, Hanxiao LIU. Power Control Algorithm Based on Q-Learning in Femtocell[J]. Journal of Electronics & Information Technology, 2019, 41(11): 2557-2564. doi: 10.11999/JEIT181191

Citation:

Yun LI, Ying TANG, Hanxiao LIU. Power Control Algorithm Based on Q-Learning in Femtocell[J]. Journal of Electronics & Information Technology, 2019, 41(11): 2557-2564. doi: 10.11999/JEIT181191

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Power Control Algorithm Based on Q-Learning in Femtocell

doi: 10.11999/JEIT181191

Chongqing Key Laboratory of Mobile Communication Technology, The Chongqing University of Posts and Telecommunications, Chongqing 400065, China

Funds: The National Natural Science Foundation of China (61671096), The Chongqing Research and Innovation Program of Graduated Students (CYS17220), The Chongqing Science and Technology Innovation Leadership Talent Support Program (CSTCCXLJRC201710), The Chongqing Research Program of Basic Science and Frontier Technology (cstc2017jcyjBX0005), The Chongqing Overseas Students Entrepreneurship and Innovation Support Plan

Received Date: 2018-12-28
Rev Recd Date: 2019-04-10

Available Online: 2019-05-21

Publish Date: 2019-11-01

Abstract

Abstract

The power control problem of mobile users in macro-femto heterogeneous cellular networks is studied. Firstly, an optimization model that maximizes the total energy efficiency of femtocells with the minimum received signal-to-noise ratio as the constraint is established. Then, a femtocell centralized Power Control algorithm based on Q-Learning (PCQL) is proposed. Based on reinforcement learning, the algorithm can adjust the transmit power of the user terminal without accurate channel state information simultaneously. The simulation results show that the algorithm can effectively control the power of the user terminal and improve system energy efficient.
- Centralized power control,
- Q-Learning algorithm,
- Energy-efficient optimization

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References(22)

References

LÓPEZ-PÉREZ D, DING M, CLAUSSEN H, et al. Towards 1 Gbps/UE in cellular systems: understanding ultra-dense small cell deployments[J]. IEEE Communications Surveys & Tutorials, 2015, 17(4): 2078–2101. doi: 10.1109/COMST.2015.2439636

YUNAS S F, VALKAMA M, and NIEMELÄ J. Spectral and energy efficiency of ultra-dense networks under different deployment strategies[J]. IEEE Communications Magazine, 2015, 53(1): 90–100. doi: 10.1109/MCOM.2015.7010521

MARTOLIA D, SATHYA V, RANGISETTI A K, et al. Enhancing performance of victim macro users via joint ABSF and dynamic power control in LTE HetNets[C]. The Twenty-third National Conference on Communications, Chennai, India, 2017: 1–6.

SHIN D and CHOI S. Dynamic power control for balanced data traffic with coverage in femtocell networks[C]. The 8th International Wireless Communications and Mobile Computing Conference, Limassol, Cyprus, 2012: 648–653.

ZHANG Jinzhu, HONG Peilin, XUE Kaiping, et al. A novel power control scheme for femtocell in heterogeneous networks[C]. 2012 IEEE Consumer Communications and Networking Conference, Las Vegas, USA, 2012: 802–806.

PAN Zhenni, MEGUMI, SAITOU, et al. Neuron control-based power adjustment scheme for sleep two-tier cellular networks[C]. 2014 IEEE Wireless Communications and Networking Conference, Istanbul, Turkey, 2014: 3201–3206.

ZHOU Tianqing, LIU Zunxiong, ZHAO Junhui, et al. Joint user association and power control for load balancing in downlink heterogeneous cellular networks[J]. IEEE Transactions on Vehicular Technology, 2018, 67(3): 2582–2593. doi: 10.1109/TVT.2017.2768574

MARTIN-VEGA F J, GOMEZ G, AGUAYO-TORRES M C, et al. Analytical modeling of interference aware power control for the uplink of heterogeneous cellular networks[J]. IEEE Transactions on Wireless Communications, 2016, 15(10): 6742–6757. doi: 10.1109/TWC.2016.2588469

ZHANG Jing, LIAO Yan, and XIN Yili. Uplink power control for heterogeneous small cell networks[C]. 2016 IEEE 83rd Vehicular Technology Conference, Nanjing, China, 2016: 1–5.

WANG Min, GAO Hui, and LV Tiejun. Energy-efficient user association and power control in the heterogeneous network[J]. IEEE Access, 2017, 5: 5059–5068. doi: 10.1109/ACCESS.2017.2690305

ZHANG Jing, XIANG Lin, NG D W K, et al. Energy efficiency evaluation of multi-tier cellular uplink transmission under maximum power constraint[J]. IEEE Transactions on Wireless Communications, 2017, 16(11): 7092–7107. doi: 10.1109/TWC.2017.2739142

PAN Zhenni and SHIMAMOTO S. Cell sizing based energy optimization in joint macro-femto deployments via sleep activation[C]. 2013 IEEE Wireless Communications and Networking Conference, Shanghai, China, 2013: 4765–4770.

SHIFAT A S M Z, CHOWDHURY M Z, and JANG Y M. Game-based approach for QoS provisioning and interference management in heterogeneous networks[J]. IEEE Access, 2018, 6: 10208–10220. doi: 10.1109/ACCESS.2017.2704094

MISHRA S and MURTHY C S R. Increasing energy efficiency via transmit power spreading in dense femto cell networks[J]. IEEE Systems Journal, 2018, 12(1): 971–980. doi: 10.1109/JSYST.2016.2573845

GURUACHARYA S, NIYATO D, KIM D I, et al. Hierarchical competition for downlink power allocation in OFDMA femtocell networks[J]. IEEE Transactions on Wireless Communications, 2013, 12(4): 1543–1553. doi: 10.1109/TWC.2013.022213.120016

WANG Haining, WANG Jiaheng, and DING Zhi. Distributed power control in a two-tier heterogeneous network[J]. IEEE Transactions on Wireless Communications, 2015, 14(12): 6509–6523. doi: 10.1109/TWC.2015.2456055

MAO Tingli, FENG Gang, LIANG Liang, et al. Energy-efficient power control for macro-femto networks[C]. The 22nd Wireless and Optical Communication Conference, Chongqing, China, 2013: 122–125.

MAO Tingli, FENG Gang, LIANG Liang, et al. Distributed energy-efficient power control for macro-femto networks[J]. IEEE Transactions on Vehicular Technology, 2016, 65(2): 718–731. doi: 10.1109/TVT.2015.2402618

LAI Weisheng, CHANG T H, and LEE T S. Joint power and admission control for spectral and energy efficiency maximization in heterogeneous OFDMA networks[J]. IEEE Transactions on Wireless Communications, 2016, 15(5): 3531–3547. doi: 10.1109/TWC.2016.2522958

LOODARICHEH R A, MALLICK S, BHARGAVA V K. Energy-efficient resource allocation for OFDMA cellular networks with user cooperation and QoS provisioning[J]. IEEE Transactions on Wireless Communications, 2014, 13(11): 6132–6146. doi: 10.1109/TWC.2014.2329877

GHADIMI E, CALABRESE F D, PETERS G, et al. A reinforcement learning approach to power control and rate adaptation in cellular networks[C]. 2017 IEEE International Conference on Communications, Paris, France, 2017: 1–7.

周志华. 机器学习[M]. 北京: 清华大学出版社, 2016: 372–390.

ZHOU Zhihua. Machine Learning[M]. Beijing: Tsinghua University Press, 2016: 372–390.

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