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International Journal of Sensors, Wireless Communications and Control

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ISSN (Print): 2210-3279
ISSN (Online): 2210-3287

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Research Article

Optimizing Performance of Worst Case User in Ultra-dense Networks Utilizing Deep Q-learning

Author(s): Sinh Cong Lam* and Duc Tan Tran

Volume 13, Issue 5, 2023

Published on: 04 October, 2023

Page: [318 - 325] Pages: 8

DOI: 10.2174/2210327913666230823094503

Price: $65

Abstract

This paper defines, analyzes, and improves the performance of the worst-case user in ultradense networks.

Background: In Ultra-Dense Networks (UDNs), where the Base Stations are distributed with a very high density, the users are possibly near the cells’ intersection. These users are called the Worst-Case Users (WCU) and usually experience very low performance.

Objectives: Thus, improving the WCU performance is an urgent problem to secure the service requirement of future cellular networks.

Methods: In this paper, the performance of the WCU is analyzed in UDNs with a maximum power algorithm and under the wireless environment with Stretched Path Loss model and Rayleigh fading. To improve the WCU data rate, the Deep Q Networks with and without Multi-Input-Multi-output (MIMO) are utilized in this paper.

Results: The simulation results show that a system–based Deep Q Learning can dramatically improve the WCU performance compared to the system with the maximum power algorithm. In addition, the deployment of the MIMO technique in a system–based Deep Q-learning only has benefits in bad channel conditions.

Conclusion: In any channel condition, utilization of Deep Q Learning is a suitable solution to improve the WCU performance. Furthermore, if the user experiences a good channel condition, the MIMO technique can be used with Deep Q Learning to obtain further performance improvement.

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[1]
Agiwal M, Roy A, Saxena N. Next generation 5G wireless networks: A comprehensive survey. IEEE Commun Surv Tutor 2016; 18(3): 1617-55.
[http://dx.doi.org/10.1109/COMST.2016.2532458]
[2]
Kamel M, Hamouda W, Youssef A. Ultra-dense networks: A survey. IEEE Commun Surv Tutor 2016; 18(4): 2522-45.
[http://dx.doi.org/10.1109/COMST.2016.2571730]
[3]
David T, Pramod V. Fundamentals of wireless communication. Cambridge, United Kingdom: Cambridge University Press 2005.
[4]
Hyun-kwan Lee. Seong-Lyun Kim, Kim S-L. Worst-case user analysis in poisson voronoi cells. IEEE Commun Lett 2013; 17(8): 1580-3.
[http://dx.doi.org/10.1109/LCOMM.2013.062113.130854]
[5]
Nigam G, Minero P, Haenggi M. Coordinated multipoint joint transmission in heterogeneous networks. IEEE Trans Commun 2014; 62(11): 4134-46.
[http://dx.doi.org/10.1109/TCOMM.2014.2363660]
[6]
Bassoy S, Farooq H, Imran MA, Imran A. Coordinated multi-point clustering schemes: A survey. IEEE Commun Surv Tutor 2017; 19(2): 743-64.
[http://dx.doi.org/10.1109/COMST.2017.2662212]
[7]
Luo F-L, Ed. Machine Learning for Future Wireless Communications. Wiley 2020.
[http://dx.doi.org/10.1002/9781119562306]
[8]
Zhang C, Patras P, Haddadi H. Deep learning in mobile and wireless networking: A survey. IEEE Commun Surv Tutor 2019; 21(3): 2224-87.
[http://dx.doi.org/10.1109/COMST.2019.2904897]
[9]
Luong NC, Hoang DT, Gong S, et al. Applications of deep reinforcement learning in communications and networking: A survey. IEEE Commun Surv Tutor 2019; 21(4): 3133-74.
[http://dx.doi.org/10.1109/COMST.2019.2916583]
[10]
Meng F, Chen P, Wu L, Cheng J. Power allocation in multi-user cellular networks: Deep reinforcement learning approaches. IEEE Trans Wirel Commun 2020; 19(10): 6255-67.
[http://dx.doi.org/10.1109/TWC.2020.3001736]
[11]
Huang XL, Li YX, Gao Y, Tang XW. Q-learning-based spectrum access for multimedia transmission over cognitive radio networks. IEEE Trans Cogn Commun Netw 2021; 7(1): 110-9.
[http://dx.doi.org/10.1109/TCCN.2020.3027297]
[12]
Iqbal MU, Ansari EA, Akhtar S, Khan AN. Improving the QoS in 5G hetnets through cooperative Q-learning. IEEE Access 2022; 10: 19654-76.
[http://dx.doi.org/10.1109/ACCESS.2022.3151090]
[13]
Garg N, Sellathurai M, Bhatia V, Ratnarajah T. Function approximation based reinforcement learning for edge caching in massive MIMO networks. IEEE Trans Commun 2021; 69(4): 2304-16.
[http://dx.doi.org/10.1109/TCOMM.2020.3047658]
[14]
Zhu Y, Li J, Zhu P, Wang D, Ye H, You X. Load-aware dynamic mode selection for network-assisted full-duplex cell-free large-scale distributed MIMO systems. IEEE Access 2022; 10: 22301-10.
[http://dx.doi.org/10.1109/ACCESS.2022.3152545]
[15]
AlAmmouri A, Andrews JG, Baccelli F. SINR and throughput of dense cellular networks with stretched exponential path loss. IEEE Trans Wirel Commun 2017; 172: 1147-60.
[16]
Study on NR positioning support. 2019. Available from: https://portal.3gpp.org/desktopmodules/Specifications/SpecificationDetails.aspx?specificationId=3501
[17]
Zhong L, Ji X, Wang Z, Qin J, Muntean GM. A Q-learning driven energy-aware multipath transmission solution for 5G media services. IEEE Trans Broadcast 2022; 68(2): 559-71.
[http://dx.doi.org/10.1109/TBC.2022.3147098]

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