Improved Rate of Intelligent Surfaces for Vehicular Networks


Liu, Z., Sun, H., & Liu, Q. (2022). Improved Rate of Intelligent Surfaces for Vehicular Networks. Journal of Intelligent Communication, 2(2), 1–7.


  • Zhening Liu
    University of Bahrain
  • Hongxing Sun Hankuk University of Foreign Studies
  • Qiang Liu nha University, Incheon 22212, Republic of Korea

An intelligent reflecting surface (IRS) is an array that consists of a large number of passive reflecting elements. Such a device possesses the potential to extend the coverage of transmission in future communication networks by overcoming the effects of non line-of-sight propagation. Accordingly, to present the case for utilizing IRS panels in future wireless networks, in this paper, we analyze a multi-user downlink network aided by IRS. In particular, by using a realistic 5G channel model, we compare the performance of the IRS-aided network with a decode and forward (DF) relay-aided scenario and a network without IRS or relay. Our analysis revealed the following: (i) At best, communication aided by a DF relay with perfect channel state information (CSI) could match the performance of the IRS-aided network with imperfect CSI when the channel estimation error was high and the number of users was large. (ii) IRS-aided communication outright outperformed the DF relay case when the transmit power was high or the number of users in the network was low. (iii) Increasing the number of elements in an IRS translated to greater quality of service for the users. (iv) IRS-aided communication showed better energy efficiency compared with the other two scenarios for higher quality of service requirements.


intelligent reflecting surface multi-users communications energy-efficiency power-minimization


  1. Brooks, R.R.; Yun, S.B.; Deng, J. Cyber-Physical Security of Automotive Information Technology; Morgan Kaufmann: Boston, MA, USA, 2012; pp. 655–676.
  2. Han, B.; Peng, S.; Wu, C.; Wang, X.; Wang, B. LoRa-based physical layer key generation for secure V2V/V2I communica-tions. Sensors 2020, 20, 682.
  3. Pereira, J.; Ricardo, L.; Luís, M.; Senna, C.; Sar-gento, S. Assessing the reliability of fog compu-ting for smart mobility applications in VANETs. Future Gener. Comput. Syst. 2019, 94, 317–332.
  4. Arif, M.; Wang, G.; Bhuiyan, M.Z.A.; Wang, T.; Chen, J. A survey on security attacks in VANETs: Communication, applications and challeng-es. Veh. Commun. 2019, 19, 100179.
  5. Tanwar, S.; Vora, J.; Tyagi, S.; Kumar, N.; Obai-dat, M.S. A systematic review on security issues in vehicular ad hoc network. Secur. Priv. 2018, 1, e39.
  6. Pepper, R. Cisco Visual Networking Index (VNI) Global Mobile Data Traffic Forecast Update. Technical Report, Cisco, February 2013. Availa-ble online: (accessed on 19 December 2022).
  7. Waqas, M.; Ahmed, M.; Li, Y.; Jin, D.; Chen, S. Social-aware secret key generation for secure device-to-device communication via trusted and non-trusted relays. IEEE Trans. Wirel. Com-mun. 2018, 17, 3918–3930.
  8. Renault, É.; Mühlethaler, P.; Boumerdassi, S. Communication security in vanets based on the physical unclonable function. In Proceedings of the ICC 2021-IEEE International Conference on Communications, Montreal, QC, Canada, 14–23 June 2021; pp. 1–6.
  9. Ali, I.; Hassan, A.; Li, F. Authentication and pri-vacy schemes for vehicular ad hoc networks (VANETs): A survey. Veh. Commun. 2019, 16, 45–61.