Inter-Vehicle Cooperation Channel Estimation for IEEE802.11p V2I Communications

다국어 초록 (Multilingual Abstract)

The impact of imperfect channel state information (CSI) is particularly serious in vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) communications.~Considering the sparse characteristics of V2I channels, this paper proposes an inter-vehicle cooperation channel estimation (IVC-CE) method at the IEEE 802.11p physical layer, and the effects of outdated CSI can be mitigated by single or multiple vehicle measurements.~With these assisted measurements, we can develop an interpolation-assisted channel estimation technique to obtain accurate CSI.~To select optimum measurements from a group of channel estimates, we utilize the position and velocity of a vehicle as a priori information.~We investigate the validity of the IVC-CE technique with the traditional linear minimum mean-squared error algorithm and present a scalable data transmission scheme at the physical layer.~The performance of IVC-CE is analyzed by its mean squared error and bit error rate in a fast fading channel environment. Simulations and numerical results show the merits of the proposed techniques, which can significantly enhance estimation performance and support safety related data transmissions for vehicular ad hoc networks.

참고문헌 (Reference)

1 "WINNER II Channel Models" 2007

2 M. Médard, "The effect upon channel capacity in wireless communications of perfect and imperfect knowledge of the channel" 46 (46): 933-946, 2000

3 S. Zhou, "Subspace-based (semi-) blind channel estimation for block precoded space-time OFDM" 50 (50): 1215-1228, 2002

4 "Spatial channel model for Multiple Input Multiple Output (MIMO) simu-lations (Release 7)"

5 M. T. Heath, "Scientific Computing: An Introductory Survey" McGraw-Hill Companies 1997

6 Y. Li, "Robust channel estimation for OFDM systems with rapid dispersive fading channels" 46 (46): 902-915, 1998

7 G. Dahman, "On the cross-correlation properties of large-scale fading in distributed antenna systems" 160-165, 2014

8 J. V. Beek, "On channel estimation in OFDM systems" 815-819, 1995

9 O. Edfors, "OFDM channel estimation by singular value decomposition" 46 (46): 931-939, 1998

10 H. ¸Senol, "Nondata-aided joint channel estimation and equalization for OFDM systems in very rapidly varying mobile channels" 60 (60): 4236-4253, 2012

11 P. Hoeher, "Multi-Carrier Spread-Spectrum" 169-178, 1997

12 W. C. Jakes, "Microwave Mobile Communications" Wiley Press 1974

13 H. Jung, "Link asymmetry in virtual MISObased networks" 1322-1327, 2013

14 X. Dong, "Linear interpolation in pilot symbol assisted channel estimation for OFDM" 6 (6): 1910-1920, 2007

15 S. Rezaei, "Kalman filter-based integration of DGPS and vehicle sensors for localization" 15 (15): 1080-1088, 2007

16 N. M. Idrees, "Improving time variant channel estimation for 3GPP LTE-downlink" 2114-2119, 2012

17 P. Petrus, "Geometrical-based statistical macrocell channel model for mobile environments" 51 (51): 425-434, 2002

18 A. Paier, "First results from car-to-car and car-to-infrastructure radio channel measurements at 5.2 GHz" 1-5, 2007

19 C. Oestges, "Experimental characterization and modeling of outdoorto-indoor and indoor-to-indoor distributed channels" 59 (59): 2253-2265, 2010

20 U.S. Department of Transportation: DoT, "Crash Data Analyses for Vehicle-to-Infrastructure Communications for Safety Applications" 2012

21 P. Alexander, "Cooperative intelligent transport systems: 5.9-GHz field trials" 99 (99): 1213-1235, 2011

22 X. Cheng, "Cooperative MIMO channel modeling and multi-link spatial correlation properties" 30 (30): 388-396, 2012

23 "Consideration of the frequency bands 1,375–1,400MHz and 1,427–1,452 MHz for the mobile service – Compatibility with systems of the Earth exploration-satellite service (EESS)within the 1,400–1,427 MHz frequency band" 2014

24 W Bajwa, "Compressed channel sensing:A new approach to estimating sparse multipath channels" 98 (98): 1058-1076, 2010

25 K. Manolakis, "Channel prediction by Doppler-delay analysis and benefits for base station cooperation" 1-6, 2013

26 S. Coleri, "Channel estimation techniques based on pilot arrangement in OFDM systems" 48 (48): 223-229, 2002

27 M. K. Ozdemir, "Channel estimation for wireless OFDM systems" 9 (9): 18-48, 2007

28 L. J. Cimini, "Analysis and simulation of a digital mobile channel using orthogonal frequency division multiplexing" COM-33 : 665-675, 1985

29 D. J. Love, "An overview of limited feedback in wireless communication systems" 26 (26): 1341-1365, 2008

30 X. Cheng, "An adaptive geometry-based stochastic model for non-isotropic MIMO mobile-to-mobile channels" 8 (8): 4824-4835, 2009

31 P. Tordorovic, "An Introduction to Stochastic Processes and Their Applica-tions" Springer-Verlag 1992

32 IEEE 802 Standard Part 11, "Amendment 6: Wireless Access in Vehicular Environments"

33 Sigen Ye, "Adaptive OFDM systems with imperfect channel state information" 5 (5): 3255-3265, 2006

34 A. Abdi, "A space-time correlation model for multielement antenna systems in mobile fading channels" 20 (20): 550-560, 2002

35 A. Abdi, "A parametric model for the distribution of the angle of arrival and the associated correlation function and power spectrum at the mobile station" 61 (61): 1222-1233, 2002

36 J. Karedal, "A geometry-based stochastic MIMO model for vehicleto-vehicle communications" 8 (8): 3646-3657, 2009

37 X. Chen, "A Markov model for headway/spacing distribution of road traffic" 11 (11): 773-785, 2010