An Evolutionary Game for Mobile User Access Mode Selection in Sub-6 GHz/mmWave Cellular Networks | IEEE Journals & Magazine | IEEE Xplore

An Evolutionary Game for Mobile User Access Mode Selection in Sub-6 GHz/mmWave Cellular Networks


Abstract:

By utilizing the combination of two powerful tools i.e., stochastic geometry (SG) and evolutionary game theory (EGT), in this paper, we study the problem of mobile user (...Show More

Abstract:

By utilizing the combination of two powerful tools i.e., stochastic geometry (SG) and evolutionary game theory (EGT), in this paper, we study the problem of mobile user (MU) mode selection in heterogeneous sub-6 GHz/millimeter wave (mmWave) cellular networks. Particularly, by using SG tools, we first propose an analytical framework to assess the performance of the considered networks in terms of average signal-to-interference-plus-noise (SINR) ratio, average rate, and mobility-induced time overhead, for scenarios with user mobility. According to the SG-based framework, an EGT-based approach is presented to solve the problem of access mode selection. Specifically, two EGT-based models are considered, where for each MU its utility function depends on the average SINR and the average rate, respectively, while the time overhead is considered as a penalty term. A distributed algorithm is proposed to reach the evolutionary equilibrium, where the existence and stability of the equilibrium is theoretically analyzed and proved. Moreover, we extend the formulation by considering information delay exchange and evaluate its impact on the convergence of the proposed algorithm. Our results reveal that the proposed technique can offer better spectral efficiency and connectivity in heterogeneous sub-6 GHz/mmWave cellular networks with mobility, compared with the conventional access mode selection techniques.
Published in: IEEE Transactions on Wireless Communications ( Volume: 21, Issue: 7, July 2022)
Page(s): 5644 - 5657
Date of Publication: 20 January 2022

ISSN Information:

Funding Agency:

References is not available for this document.

I. Introduction

Future wireless networks, namely beyond fifth generation (B5G) and sixth generation (6G), are required to handle and accommodate a diverse set of both static and mobile end-user devices (e.g., remote sensors, unmanned aerial vehicles and autonomous cars), designating the support of mobility as a fundamental aspect of wireless networks. Moreover, this unprecedented number of connected devices with such diverse requirements is also contributing to the tremendous growing demand for network scalability, latency, and spectral efficiency (SE) [1], [2]. In order to meet this explosive throughput demand of future wireless connectivity, there has been an increasing interest in the synergy of network densification technique by using small cells (SCells) and the millimeter-wave (mmWave) communications [1], [2]. Initially, the concept of network densification refers to the massive deployment of SCells (such as femptocells and picocells) by overlaying the conventional sub-6 GHz networks. Such heterogeneous network (HetNet) architectures can provide high throughput to the static users, but may significantly deteriorate the performance of mobile (i.e., moving) users (MUs). Indeed, the higher number of randomly deployed cells leads a MU to experience an increased number of handovers at cell boundaries, thereby resulting in potentially significant signaling overhead among the base stations (BSs) and MUs, compromising the HetNets performance [3].

Select All
1.
I. F. Akyildiz, A. Kak and S. Nie, "6G and beyond: The future of wireless communications systems", IEEE Access, vol. 8, pp. 133995-134030, 2020.
2.
Z. Zhang et al., "6G wireless networks: Vision requirements architecture and key technologies", IEEE Veh. Technol. Mag., vol. 14, no. 3, pp. 28-41, Sep. 2019.
3.
X. Xu, Z. Sun, X. Dai, T. Svensson and X. Tao, "Modeling and analyzing the cross-tier handover in heterogeneous networks", IEEE Trans. Wireless Commun., vol. 16, no. 12, pp. 7859-7869, Dec. 2017.
4.
M. Xiao et al., "Millimeter wave communications for future mobile networks", IEEE J. Sel. Areas Commun., vol. 35, no. 9, pp. 1909-1935, Sep. 2017.
5.
J. G. Andrews, T. Bai, M. N. Kulkarni, A. Alkhateeb, A. K. Gupta and R. W. Heath, "Modeling and analyzing millimeter wave cellular systems", IEEE Trans. Commun., vol. 65, no. 1, pp. 403-430, Jan. 2017.
6.
S. S. Kalamkar, F. Baccelli, F. M. Abinader, A. S. M. Fani and L. G. U. Garcia, "Beam management in 5G: A stochastic geometry analysis", IEEE Trans. Wireless Commun., Sep. 2021.
7.
M. F. Ozkoc, A. Koutsaftis, R. Kumar, P. Liu and S. S. Panwar, "The impact of multi-connectivity and handover constraints on millimeter wave and terahertz cellular networks", IEEE J. Sel. Areas Commun., vol. 39, no. 6, pp. 1833-1853, Jun. 2021.
8.
Y. Liu, X. Fang, M. Xiao and S. Mumtaz, "Decentralized beam pair selection in multi-beam millimeter-wave networks", IEEE Trans. Commun., vol. 66, no. 6, pp. 2722-2737, Jun. 2018.
9.
H. Elshaer, M. N. Kulkarni, F. Boccardi, J. G. Andrews and M. Dohler, "Downlink and uplink cell association with traditional macrocells and millimeter wave small cells", IEEE Trans. Wireless Commun., vol. 15, no. 9, pp. 6244-6258, Sep. 2016.
10.
G. Ghatak, A. De Domenico and M. Coupechoux, "Coverage analysis and load balancing in HetNets with millimeter wave multi-RAT small cells", IEEE Trans. Wireless Commun., vol. 17, no. 5, pp. 3154-3169, May 2018.
11.
M. Shi, K. Yang, C. Xing and R. Fan, "Decoupled heterogeneous networks with millimeter wave small cells", IEEE Trans. Wireless Commun., vol. 17, no. 9, pp. 5871-5884, Sep. 2018.
12.
R. Liu, Q. Chen, G. Yu and G. Y. Li, "Joint user association and resource allocation for multi-band millimeter-wave heterogeneous networks", IEEE Trans. Commun., vol. 67, no. 12, pp. 8502-8516, Dec. 2019.
13.
H. Zhang, S. Huang, C. Jiang, K. Long, V. C. M. Leung and H. V. Poor, "Energy efficient user association and power allocation in millimeter-wave-based ultra dense networks with energy harvesting base stations", IEEE J. Sel. Areas Commun., vol. 35, no. 9, pp. 1936-1947, Sep. 2017.
14.
C. Skouroumounis, C. Psomas and I. Krikidis, "A hybrid cooperation scheme for sub-6 GHz/mmWave cellular networks", IEEE Commun. Lett., vol. 24, no. 7, pp. 1539-1543, Jul. 2020.
15.
M. Polese, M. Giordani, M. Mezzavilla, S. Rangan and M. Zorzi, "Improved handover through dual connectivity in 5G mmWave mobile networks", IEEE J. Sel. Areas Commun., vol. 35, no. 9, pp. 2069-2084, Sep. 2017.
16.
M. G. Kibria, K. Nguyen, G. P. Villardi, W.-S. Liao, K. Ishizu and F. Kojima, "A stochastic geometry analysis of multiconnectivity in heterogeneous wireless networks", IEEE Trans. Veh. Technol., vol. 67, no. 10, pp. 9734-9746, Oct. 2018.
17.
S. Choi, J.-G. Choi and S. Bahk, "Mobility-aware analysis of millimeter wave communication systems with blockages", IEEE Trans. Veh. Technol., vol. 69, no. 6, pp. 5901-5912, Jun. 2020.
18.
V. F. Monteiro, M. Ericson and F. R. P. Cavalcanti, "Fast-RAT scheduling in a 5G multi-RAT scenario", IEEE Commun. Mag., vol. 55, no. 6, pp. 79-85, Jun. 2017.
19.
J. Zhao, S. Zhao, H. Qu, G. Ren and Y. Shi, "Analysis and optimization of probabilistic caching in micro/millimeter wave hybrid networks with dual connectivity", IEEE Access, vol. 6, pp. 72372-72380, 2018.
20.
L. Yan et al., "Machine learning-based handovers for sub-6 GHz and mmWave integrated vehicular networks", IEEE Trans. Wireless Commun., vol. 18, no. 10, pp. 4873-4885, Oct. 2019.
21.
D. Niyato and E. Hossain, "Dynamics of network selection in heterogeneous wireless networks: An evolutionary game approach", IEEE Trans. Veh. Technol., vol. 58, no. 4, pp. 2008-2017, May 2009.
22.
S. Yan, M. Peng, M. A. Abana and W. Wang, "An evolutionary game for user access mode selection in fog radio access networks", IEEE Access, vol. 5, pp. 2200-2210, 2017.
23.
P. Semasinghe, E. Hossain and K. Zhu, "An evolutionary game for distributed resource allocation in self-organizing small cells", IEEE Trans. Mobile Comput., vol. 14, no. 2, pp. 274-287, Feb. 2015.
24.
M. Haenggi, Stochastic Geometry for Wireless Networks, Cambridge, U.K.:Cambridge Univ. Press, 2012.
25.
M. Banagar and H. S. Dhillon, "3GPP-inspired stochastic geometry-based mobility model for a drone cellular network", Proc. IEEE Global Commun. Conf. (GLOBECOM), pp. 1-6, Dec. 2019.
26.
I. S. Gradshteyn and I. M. Ryzhik, Table of Integrals Series and Products, Amsterdam, The Netherlands:Elsevier, 2007.
27.
H. Alzer, "On some inequalities for the incomplete gamma function", Math. Comput., vol. 66, no. 218, pp. 771-778, 1997.
28.
A. Jain, E. Lopez-Aguilera and I. Demirkol, "Improved handover signaling for 5G networks", Proc. IEEE 29th Annu. Int. Symp. Pers. Indoor Mobile Radio Commun. (PIMRC), pp. 164-170, Sep. 2018.
29.
R. Arshad, H. ElSawy, S. Sorour, T. Y. Al-Naffouri and M.-S. Alouini, "Handover management in dense cellular networks: A stochastic geometry approach", Proc. IEEE Int. Conf. Commun. (ICC), pp. 1-7, May 2016.
30.
T. Bai, R. Vaze and R. W. Heath, "Analysis of blockage effects on urban cellular networks", IEEE Trans. Wireless Commun., vol. 13, no. 9, pp. 5070-5083, Jun. 2014.

Contact IEEE to Subscribe

References

References is not available for this document.