A Novel Base-Station Selection Strategy for Cellular Vehicle-to-Everything (C-V2X) Communications
Source abstract
Abstract
Cellular vehicle-to-everything (C-V2X) communication facilitates the improved safety, comfort, and efficiency of vehicles and mobility by exchanging information between vehicles and other entities. In general, only the macrocell or only the femtocell is the communication infrastructure for C-V2X. Currently, a macro-femtocell network is used as the new C-V2X networking architecture. However, there are two unresolved problems for C-V2X in macro-femtocell networks. Firstly, vehicle mobility requires the frequent switching of connections between different base stations; invalid switching results in worse communication quality. Secondly, unintelligent base station selections cause network congestion and network-load imbalance. To address the above challenges, this paper proposes a base station selection strategy based on a Markov decision policy for a vehicle in a macro-femtocell system. Firstly, we present a mechanism to predict received signal strength (RSS) for base station selection. Secondly, a comparing Markov decision policy algorithm is presented in C-V2X. To the best of our knowledge, this is the first attempt to achieve predicted RSS based on a Markov decision policy in C-V2X technology. To validate the proposed mechanism, we simulated the traditional base station selection and our proposal when the vehicle moved at different speeds. This demonstrates that the effectiveness of a traditional base station selection policy is obvious only at high speeds, and this weakness can be resolved by our proposal. Then, we compare our solution with the traditional base station selection policy. The simulation results show that our solution is effective at switching connections between base stations, and it can effectively prevent the overloading of network resources.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Vehicular Ad Hoc Networks (VANETs) · Advanced MIMO Systems Optimization · Wireless Body Area Networks
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Faculty of Science and Engineering, Waseda University, Shinjuku, Tokyo 169-8555, Japan
Global Information and Telecommunication Institute, Waseda University, Shinjuku, Tokyo 169-8555, Japan
Funding
Japan Society for the Promotion of Science
References (30)
Show source-provided references
1. GSMA (2018, December 31). Cellular Communications between Vehicles and Everything (C-V2X). Available online: https://www.gsma.com/iot/wp-content/uploads/2017/12/C-2VX-Enabling-Intelligent-Transport-2.pdf.
2. Hua, Q., Su, Y., Sato, T., and Yu, K. (2017, January 17–20). Utilization efficient gametheoretical handover scheme for macro-femtocell networks. Proceedings of the 2017 20th International Symposium on Wireless Personal Multimedia Communications (WPMC), Bali, Indonesia.10.1109/WPMC.2017.8301877 ↗
3. Masini, B., Bazzi, A., and Zanella, A. (2018). A Survey on the Roadmap to Mandate on Board Connectivity and Enable V2V-Based Vehicular Sensor Networks. Sensors, 18.10.3390/s18072207 ↗
4. Poli, F. (2018). Vehicular Communications: From DSRC to Cellular V2X. [Ph.D. Thesis, Politecnico di Torino].
5. Enb Selection for Machine Type Communications Using Reinforcement Learning Based Markov Decision Process10.1109/TVT.2017.2730230 ↗
6. Talavera, E., Díaz-Álvarez, A., Jiménez, F., and Naranjo, J.E. (2018). Impact on Congestion and Fuel Consumption of a Cooperative Adaptive Cruise Control System with Lane-Level Position Estimation. Energies, 11.10.3390/en11010194 ↗
7. Papathanassiou, A., and Khoryaev, A. (2019, February 01). Cellular V2X as the Essential Enabler of Superior Global Connected Transportation Services. Available online: https://futurenetworks.ieee.org/tech-focus/june-2017/cellular-v2x.
8. Study of the Impact of PHY and MAC Parameters in 3GPP C-V2V Mode 410.1109/ACCESS.2018.2883401 ↗
9. Gao, L., Li, Y., Misener, J., and Patil, S. (2017, January 24–27). C-V2X based basic safety related ITS spectrum requirement analysis. Proceedings of the 2017 IEEE 86th Vehicular Technology Conference, (VTC-Fall), Toronto, ON, Canada.10.1109/VTCFall.2017.8288393 ↗
10. Berlt, P., Wollenschläger, F., Bornkessel, C., and Hein, M.A. (2018, January 9–13). Spatial Correlation of C-V2X Antennas Measured in the Virtual Road Simulation and Test Area. Proceedings of the 12th European Conference on Antennas and Propagation, (EuCAP 2018), London, UK.10.1049/cp.2018.0393 ↗
11. Abbas, F., Fan, P., and Khan, Z. (2018). A Novel Low-Latency V2V Resource Allocation Scheme Based on Cellar V2X Communications. IEEE Trans. Intell. Transp. Syst.10.1109/ICCW.2018.8403512 ↗
12. Imran, A., Bennis, M., and Giupponi, L. (2012, January 1). Use of Learning, Game Theory and Optimization as Biomimetic Approachees for Self-Organization in Macro-Femtocell Coexistence. Proceedings of the WCNC 2012 workshop on Broadband Femtocell Technologies, Paris, France.10.1109/WCNCW.2012.6215470 ↗
13. HeNB-Aided Virtual-Handover for Range Expansion in LTE Femtocell Networks10.1109/JCN.2013.000054 ↗
14. A survey on 3GPP heterogeneous networks10.1109/MWC.2011.5876496 ↗
15. Chen, X., Wang, H., Xiang, X., and Gao, C.L. (2014, January 25–27). Joint Handover Decision and Channel Allocation for LTE-A Femtocell Networks. Proceedings of the 2014 5th International Conference on Game Theory for Networks (GAMENETS), Beijing, China.10.1109/GAMENETS.2014.7043725 ↗
16. Han, K., Choi, Y., Kim, D., Na, M., Choi, S., and Han, K. (2009, January 23–27). Optimization of Femtocell network configuration under interference constraints. Proceedings of the 7th IEEE International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks, WiOPT 2009, Seoul, Korea.
17. Zahir, T., Arshad, K., Ko, Y., and Moessner, K. (2011, January 5–8). A downlink power control scheme for interference avoidance in femtocells. Proceedings of the 2011 7th IEEE International Wireless Communications and Mobile Computing Conference (IWCMC), Istanbul, Turkey.10.1109/IWCMC.2011.5982714 ↗
18. Power minimization based resource allocation for interference mitigation in ofdma femtocell networks10.1109/JSAC.2014.141213 ↗
19. A hybrid frequency assign ment for femtocells and coverage area analysis for co-channel operation10.1109/LCOMM.2008.081273 ↗
20. Dynamic resource allocation in hybrid ac-cess femtocell network10.1155/2014/539720 ↗
21. Femtocell networks:A survey10.1109/MCOM.2008.4623708 ↗
22. Nai, S.E., and Quek, T.Q. (February, January 30). Coexistence in two-tier femtocell networks: Cognition and optimization. Proceedings of the 2012 International Conference on Computing, Networking and Communications (ICNC), Maui, HI, USA.10.1109/ICCNC.2012.6167504 ↗
23. A Survey of Self Organisation in Future Cellular Networks10.1109/SURV.2012.021312.00116 ↗
24. Femtocells:Past, present, and future10.1109/JSAC.2012.120401 ↗
25. Distributed call admission control in mobile/wireless networks
26. Population-based call admission control in wireless cellular networks10.1109/ICC.2000.853750 ↗
27. Intercell Interference Coordination in OFDMA Networks and in the 3GPP Long Term Evolution System10.4304/jcm.4.7.445-453 ↗
28. Interference Cancellation for Cellular Systems: A Contemporary Overview10.1109/MWC.2005.1421925 ↗
29. Niu, B., and Wong, V.W. (2013, January 9–13). The design of resource management mechanism with hybrid access in a marco-femto system. Proceedings of the IEEE Globecom Workshops (GC Wkshps), Atlanta, GA, USA.10.1109/GLOCOMW.2013.6855690 ↗
30. Liu, C., Wei, J., Huang, S., and Cao, Y. (2012, January 21–23). A Distance-Based Handover Scheme for Femtocell and Macrocell Overlaid Networks. Proceedings of the 2012 8th International Conference on Wireless Communications, Networking and Mobile Computing (WiCOM), Shanghai, China.10.1109/WiCOM.2012.6478281 ↗