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Ergodic Capacity Analysis on MIMO Communications in Internet of Vehicles

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Abstract

Multiple-input multiple-output (MIMO) communications are believed as one of the key enabling technologies to fulfill the increasing requirements of future wireless networks. However, the asymptotic performance limits of MIMO vehicular ad hoc network (VANET) systems are not thoroughly investigated. All the related works on MIMO VANETs mainly focused on measurement and geometry based channel analyzing and modeling. In this paper, we present a framework for ergodic capacity analysis on MIMO communications among multi-antenna mounted vehicles. We model a Rayleigh fading channel with standard path-loss and lognormal shadowing, then adopt the tools of stochastic geometry to characterize the MIMO geocast channels. After analyzing the co-channel interference among MIMO geocast sessions, the upper and lower bounds on ergodic capacity of single link MIMO and MIMO geocast channels are derived with careful consideration of the important issues like antenna number, shadow fading, and transmission radius. Results reveal that ergodic capacity of MIMO VANETs can be significantly improved by appropriately mounting multiple transmit and receive antennas on vehicles. As the space for antenna mounting in a vehicle is rather limited, we further give a method to calculate the minimum number of antennas for each vehicle to guarantee the quality of service (QoS) requirements in safety oriented applications of IoV.

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References

  1. Chi K, Zhu Y, Li Y (2018) Efficient data collection in wireless powered communication networks with node throughput demands. Comput Commun 126:1–10

    Google Scholar 

  2. Lu N, Cheng N, Zhang N, Shen X, Mark J (2014) Connected vehicles: Solutions and challenges. IEEE Internet J 1(4):289– 299

    Google Scholar 

  3. Bai F, Krishnan H, Sadekar V, Holland G, ElBatt T (2006) Towards characterizing and classifying communication-based automotive applications from a wireless networking perspective. In: Proceedings of IEEE Workshop on Automotive Networking and Applications, pp 1–25

  4. Nekoui M, Eslami A, Pishro-Nik H (2008) Scaling laws for distance limited communications in vehicular ad hoc networks. In: Proceedings of IEEE International Conference on Communications, pp 2253–2257

  5. Lu N, Luan T, Wang M, Shen X, Bai F (2012) Capacity and delay analysis for social-proximity urban vehicular networks. In: Proceedings of IEEE International Conference on Computer Communications, pp 1476–1484

  6. Zhang G, Xu Y, Wang X, Tian X, Liu J, Gan X, Yu H, Qian L (2012) Multicast capacity for VANETs with directional antenna and delay constraint. IEEE J Sel Areas Commun 30(4):818–833

    Google Scholar 

  7. Zeng D, Li P, Guo S, Miyazaki T, Hu J, Xiang Y (2015) Energy minimization in Multi-Task Software-Defined sensor networks. IEEE Trans Comput 64(11):3128–3139

    MathSciNet  MATH  Google Scholar 

  8. EI-Keyi A, EIBatt T, Bai F, Saraydar C (2012) MIMO VANETS: Research Challenges and opportunities. IEEE International conference on computing, networking and communications, pp 670–676

  9. Zhu Y, Chi K, Hu P, Mao K, Shao Q (2018) Velocity control of multiple mobile chargers over moving trajectories in RF energy harvesting wireless sensor networks. IEEE Trans Veh Technol 67(11):11314–11318

    Google Scholar 

  10. Zeng D, Guo S, Barnawi A, Yu S, Stojmenovic I (2015) An improved stochastic modeling of opportunistic routing in vehicular CPS. IEEE Trans Comput 64(7):1819–1829

    MathSciNet  MATH  Google Scholar 

  11. Mecklenbräuker CF, Karedal J, Paier A, Zemen T, Czink N (2011) Vehicular channel characterization and its implications for wireless system design and performance. Proc IEEE 99(7):1189–1212

    Google Scholar 

  12. Zeng D, Zhang S, Gu L, Yu S, Fu Z (2018) Quality-of-sensing aware budget constrained contaminant detection sensor deployment in water distribution system. J Netw Comput Appl 103(1):274–279

    Google Scholar 

  13. Zeng D, Gu L, Yao H (2018) Towards energy efficient service composition in green energy powered Cyber-Physical Fog Systems. Future Generation Computer Systems

  14. Abbas T, Karedal J, Tufvesson F (2013) Measurement-based analysis: The effect of complementary antennas and diversity on vehicle-to-vehicle communication. IEEE Antenn Wirel Propag Lett 12:309–312

    Google Scholar 

  15. Gupta P, Kumar PR (2000) The capacity of wireless networks. IEEE Trans Inf Theory 46(2):388–404

    MathSciNet  MATH  Google Scholar 

  16. Yuan Y, Wang C, He Y, Alwakeel MM, Aggoune EM (2015) 3D wideband non-stationary geometry-based stochastic models for non-isotropic MIMO vehicle-to-vehicle channels. IEEE Trans Wirel Commun 14(12):6883–6895

    Google Scholar 

  17. Fu Y, Wang C, Yuan Y, Mesleh R, Aggoune EM, Alwakeel MM, Haas H (2016) BER Performance of spatial modulation systems under 3-D V2V MIMO channel models. IEEE Trans Veh Technol 65 (7):5725–5730

    Google Scholar 

  18. Chelli A, Pätzold M, Chen W, He Z (2011) A non-stationary MIMO vehicle-to-vehicle channel model derived from the geometrical street model. In: Proceedings of IEEE Vehicular Technology Conference (VTC Fall), pp 1–6

  19. Zhou W, Pätzold M, Chen W, He Z (2010) An ergodic wideband MIMO channel simulator based on the geometrical T-junction scattering model for vehicle-to-vehicle communications. In: Proceedings of IEEE International Conference on Communications and Electronics, pp 323–328

  20. He R, Renaudin O, Kolmonen V, Haneda K, Zhong Z, Ai B, Hubert S, Oestges C (2016) Vehicle-to-vehicle radio channel characterization in crossroad scenarios. IEEE Trans Veh Technol 65(8):5850–5861

    Google Scholar 

  21. Avazov N, Pätzold M (2016) A novel wideband MIMO car-to-car channel model based on a geometrical semi-circular tunnel scattering model. IEEE Trans Veh Technol 65(3):1070– 1082

    Google Scholar 

  22. Matthaiou M, Laurenson DI, Wang C (2008) Capacity study of vehicle-to-roadside MIMO channels with a line-of-sight component. In: Proceedings of IEEE Wireless Communications and Networking Conference, pp 775–779

  23. Karedal J, Tufvesson F, Czink N, Paier A, Dumard C, Zemen T, Mecklenbräuker C F, Molisch AF (2009) A geometry-based stochastic MIMO model for vehicle-to-vehicle communications. IEEE Trans Wirel Commun 8(7):3646–3657

    Google Scholar 

  24. Theodorakopoulos A, Papaioannou P, Abbas T, Tufvesson F (2013) A geometry based stochastic model for MIMO V2V channel simulation in cross-junction scenario. In: Proceedings of IEEE International Conference on ITS Telecommunications, pp 290–295

  25. Telatar IE (1999) Capacity of multi-antenna Gaussian channels. Eur Trans Telecommun 10(6):585–595

    MathSciNet  Google Scholar 

  26. Marzetta TL, Hochwald BM (1999) Capacity of a mobile multiple-antenna communication link in Rayleigh flat fading. IEEE Trans Inf Theory 45(1):139–157

    MathSciNet  MATH  Google Scholar 

  27. Chiani M, Win MZ, Zanella A (2003) On the capacity of spatially correlated MIMO Rayleigh-fading channels. IEEE Trans Inf Theory 49(10):2363–2371

    MathSciNet  MATH  Google Scholar 

  28. Smith PJ, Roy S, Shafi M (2003) Capacity of MIMO systems with semicorrelated flat fading. IEEE Trans Inf Theory 49(10):2781–2788

    MathSciNet  MATH  Google Scholar 

  29. Shin H, Lee JH (2003) Capacity of multiple-antenna fading channels: Spatial fading correlation, double scattering, and keyhole. IEEE Trans Inf Theory 49(10):2636–2647

    MathSciNet  MATH  Google Scholar 

  30. Chuah C, Tse DNC, Kahn JM, Valenzuela RA (2002) Capacity scaling in MIMO wireless systems under correlated fading. IEEE Trans Inf Theory 48(3):637–650

    MathSciNet  MATH  Google Scholar 

  31. Liu K, Raghavan V, Sayeed AM (2003) Capacity scaling and spectral efficiency in wide-band correlated MIMO channels. IEEE Trans Inf Theory 49(10):2504–2526

    MathSciNet  MATH  Google Scholar 

  32. Xiao C, Zheng YR (2004) Ergodic capacity of doubly selective Rayleigh fading MIMO channels. In: Proceedings of IEEE Wireless Communications and Networking Conference, pp 345–350

  33. Xiao C, Zheng YR (2008) On the ergodic capacity of MIMO triply selective Rayleigh fading channels. IEEE Trans Wirel Commun 7(6):2272–2279

    Google Scholar 

  34. Yoo T, Goldsmith A (2006) Capacity and power allocation for fading MIMO channels with channel estimation error. IEEE Trans Inf Theory 52(5):2203–2214

    MathSciNet  MATH  Google Scholar 

  35. Jin S, Gao X, You X (2007) On the ergodic capacity of rank-1 Ricean fading MIMO channels. IEEE Trans Inf Theory 53(2):502–517

    MathSciNet  MATH  Google Scholar 

  36. Jayaweera SK, Poor HV (2003) MIMO Capacity results for Rician fading channels. In: Proceedings of IEEE Global Telecommunications Conference, pp 1806–1810

  37. Kong C, Zhong C, Matthaiou M, Zhang Z (2015) Performance of downlink massive MIMO in Ricean fading channels with ZF precoder. In: Proceedings of IEEE International Conference on Communications, pp 1776–1782

  38. Jose J, Ashikhmin A, Marzetta TL, Vishwanath S (2011) Pilot contamination and precoding in multi-cell TDD systems. IEEE Trans Wirel Commun 10(8):2640–2651

    Google Scholar 

  39. Héliot F, Hoshyar R, Tafazolli R (2011) An accurate closed-form approximation of the distributed MIMO outage probability. IEEE Trans Wirel Commun 10(1):5–11

    Google Scholar 

  40. Matthaiou M, Chatzidiamantis ND, Karagiannidis GK (2011) A new lower bound on the ergodic capacity of distributed MIMO systems. IEEE Signal Process Lett 18(4):227– 230

    Google Scholar 

  41. Caire G, Shamai (shitz) S (2003) On the achievable throughput of a multiantenna Gaussian broadcast channel. IEEE Trans Inf Theory 49(7):1691–1706

    MathSciNet  MATH  Google Scholar 

  42. Webb M, Beach M, Nix A (2004) Capacity limits of MIMO channels with co-channel interference. In: Proceedings of IEEE Vehicular Technology Conference (VTC Spring), pp 703– 707

  43. Oyman Ö, Paulraj AJ (2006) Design and analysis of linear distributed MIMO relaying algorithms. IEE Proceed Commun 153(4):565–572

    Google Scholar 

  44. Bölcskei H, Nabar RU, Özgür O, Paulraj AJ (2006) Capacity scaling laws in MIMO relay networks. IEEE Trans Wirel Commun 5(6):1433–1444

    Google Scholar 

  45. Chiani M, Win MZ, Shin H (2010) MIMO Networks: The effects of interference. IEEE Trans Inf Theory 56(1):336– 349

    MathSciNet  MATH  Google Scholar 

  46. Matthaiou M, Zhong C, McKay MR, Ratnarajah T (2013) Sum rate analysis of ZF receivers in distributed MIMO systems. IEEE J Sel Areas Commun 31(2):180–191

    Google Scholar 

  47. Rusek F, Persson D, Lau BK, Larsson EG, Marzetta TL, Edfors O, Tufvesson F (2013) Scaling up MIMO: Opportunities and challenges with very large arrays. IEEE Signal Proc Mag 30(1):40–60

    Google Scholar 

  48. Oyman Ö , Nabar RU, Bölcskei H, Paulraj AJ (2003) Characterizing the statistical properties of mutual information in MIMO channels. IEEE Trans Signal Process 51(11):2784– 2795

    MathSciNet  MATH  Google Scholar 

  49. Oyman Ö , Nabar RU, Bölcskei H, Paulraj AJ (2002) Tight lower bounds on the ergodic capacity of Rayleigh fading MIMO channels. In: Proceedings of IEEE Global Telecommunications Conference, pp 1172–1176

  50. Yuan J, Matthaiou M, Jin S, Gao F (2017) Tightness of Jensen’s bounds and applications to MIMO communications. IEEE Trans Commun 65(2):579–593

    Google Scholar 

  51. Zhang QT, Cui XW, Li XM (2005) Very tight capacity bounds for MIMO-correlated Rayleigh-fading channels. IEEE Trans Wirel Commun 4(2):681–688

    Google Scholar 

  52. Chi K, Zhu YH, Li Y, Huang L, Xia M (2017) Minimization of transmission completion time in wireless powered communication networks. IEEE Internet J 4(5):1671–1683

    Google Scholar 

  53. Zeng D, Gu L, Lian L, Guo S, Yao H, Hu J (2016) On cost-efficient sensor placement for contaminant detection in water distribution systems. IEEE Trans Ind Inf 12(6):2177–2185

    Google Scholar 

  54. Clerckx B, Oestges C (2013) MIMO Wireless networks (Second Edition): Channels, techniques and standards for multi-antenna, multi-user and multi-cell systems. Academic Press , Cambridge

    Google Scholar 

  55. Qian D, Zheng D, Zhang J, Shroff NB, Joo C (2013) Distributed CSMA algorithms for link scheduling in multihop MIMO networks under SINR model. IEEE/ACM Trans Netw 21(3):746–759

    Google Scholar 

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (61771374, 61771373, 61801360, and 61601357), in part by China 111 Project (B16037), and in part by the Fundamental Research Fund for the Central Universities (3102019PY005, JB181506, JB181507, and JB181508).

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  1. School of Cyberspace Security, Northwestern Polytechnical University, Shaanxi, China

    Shangwei Zhang & Jiajia Liu

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  1. Shangwei Zhang
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Correspondence to Jiajia Liu.

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Zhang, S., Liu, J. Ergodic Capacity Analysis on MIMO Communications in Internet of Vehicles. Mobile Netw Appl 26, 923–939 (2021). https://doi.org/10.1007/s11036-019-01352-1

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