4.2 Element spacing effect
4.3.2 Angle Spread Effect
From Table-3 we realize the fact that capacity and azimuth spread of AOA present a positive correlation shown as Fig 4-23, but indifferent with azimuth spread of AOD. Azimuth spread of AOA means the angle dispersion caused by multipaths propagating in the channel owing to ground reflection wave, corner diffracted wave, scattered wave etc. These mulitpaths can disturb the propagation channel of signals resulting in the transmitted signal received by array less correlated, even uncorrelated.
But angular spread of AOD means the angular dispersionness from transmitter; it does not yet propagate through MIMO channel to interference the capacity, hence the azimuth spread of AOD does not present obvious correlation with capacity. The condition hold for site2, site3 and site4, we note that the capacity of LOS in site1 is smaller than that of site2, site3 and site4, since scatters existed in the environment and site1 is LOS with open-area, short distance but for the site2, site3 and site4 cases, we can resolve the scattered wave in terms of time resolution and azimuth resolution to
observe how the channel interfered by the scatters shown Fig 4-24. Figure 4-24 provides the Delay-Azimuth Spectrum of measurement data and the resolved scattered wave (*) on the time and azimuth resolution and CDF of capacity for subchannels and eigenvalue distribution of measurement for site2 (a), site3 (b) and site4 (c) since we have realized the impact of propagation on capacity. While we compute the number of scatter waves, an assumption of single bounce is made. From the number resolved, Site2-28, Site3-59, Site4-89, we realize the scattered waves impinged on transmitter more, the propagation channel decorrelated more, hence the capacity will have a significant improvement.
Table-3 the mean capacity, standard deviation of capacity, standard deviation of rms of azimuth spread of AOA and standard deviation of rms of azimuth spread of AOD
for all measurement sites.
Site3
Fig 4-26 (a) capacity versus rms azimuth spread of AOA for route no.1
Fig 4-26 (b) capacity versus rms azimuth spread of AOA for route no.2
Fig 4-26 (c) capacity versus rms azimuth spread of AOA for route no.3
Fig 4-26 (d) capacity versus rms azimuth spread of AOA for route no.4
Fig 4-27 (a)
Fig 4-27 (b)
Fig 4-27 (c)
Figure 4-27 the Delay-Azimuth Spectrum of measurement data and the resolved scattered wave (*) on the time and azimuth resolution and CDF of capacity for subchannels and eigenvalue distribution of measurement for (a) route no.2 (b) route
no.3 and (c) route no.4.
Chapter 5 Conclusion distance, bandwidth and angular spread are also considered. The measurement using the RUSK channel sounder was carried in the National Chiao-Tung University campus.
As far as propagation conditions are concerned, the capacity in the NLOS condition ensures a high probability to be larger than the LOS and OLOS conditions due to larger angular dispersion. As for MIMO system with the same element spacing located at different routes, capacity increases due to transmitted signals disturbed by the local scatter so that the spatial correlation of receiving signals decreases.
Environment must be complex enough to disturb the spatial correlation of receiving signals so that capacity becomes larger.
For the different element spacing effect, capacity of MIMO system under particular site increases due to the resolution of multipaths seen from the receive end array increases. From hybrid model, we know that local scatters surrounded by the transmit end array will cause large capacity fluctuation.
For the bandwidth effect, MIMO capacity will increase as the signal bandwidth increases. This phenomenon is hold for each route under this measurement.
angular spread of AOA increases since the multipaths propagated within larger rms angular spread of AOA probably experienced complex channel so that disturb the spatial correlation of signals. Finally, we evaluate the complexity of the channel influenced by local scatters using the number of resolved scatter waves based on temporal and angular resolution.
References
References
[1] G. J. Foschini and M. J. Gans, On limits of Wireless Communications in a Fading Environment When Using Multiple Antennas, Wireless Personal Communications, vol. 6, No. 3, pp. 311-335, March 1998
[2] A. Lozano, F. R. Farrokhl and R. A. Valenzuela, Lifting the Limits on High-Speed Wireless Data Access Using Antenna Arrays, IEEE Communications Magazine, pp. 156-162, September 2001.
[3] Jean Philippe Kermoal, Preben E. Mogensen, Soren HH. Jensen, Jorgen B.
Andersen, Frank Frederiksen, Troels B. Sorensen and Klaus I. Pedersen,
§ Experi ment al I nvesti gati on of Mulipath Richness for Multi-Element Transmit and Receive Antenna Arrays , IEEE Vehicular Technology Conference VTC 2000 Spring, Tokyo, Japan, vol.3, pp. 2004-2008, May 2000
[4] Da-shan Shiu, Gerard J. Foschini, Michael J. Gans and Joseph M. Kahn, §Fadi ng Correlation and Its Effect on the Capacity of Multielement Antenna Systems , IEEE Transactions on Communications, vol. 48, no.3, pp. 502-513 March 2000.
[5] Daniel W. Bliss, Keith W. Forsythe, Alfred O. Hero and Ali F. Yegulalp,
§ Envir on ment Iss ues f or MI MO Capacit , IEEE Transactions on Signal Processing, vol.50, no.9, pp.2128-2141 September 2002.
[6] D. Gesbert, H. Boleskei, D. A. Gore and A. J. Paulraj, § Out door MI MO Wir el ess Channels-Models and Performance Prediction , IEEE Transactions on Communications, pp.1-21 Aug. 2000,
[7] Jorgen Bach Andersen, Array Gain and Capacity for Known Random Channels with Multiple Element Arrays at Both Ends, IEEE Journal on Selected Areas in Communications, vol. 18, no. 11, pp. 2171-2178, November 2000
[8] Thomas M. Cover and Joy A. Thomas, Elements of Information Theory, Wiley, 1991
[9] Andreas F. Molisch, A generic model for MIMO wireless propagation channels, Communications, 2002. ICC 2002. IEEE International Conference on, vol. 1, 28 April-2 May 2002 , pp. 277 282
[10] Reiner S. Thoma, Dirk Hampicke, Andreas Richter, Gerd Sommerkorn, Axel Schneider, Uwe Trautwein and Walter Wirnitzer, §I dentifi cati on of Ti m-Variant Directional Mobile Radio Channels , IEEE Transactions on Instruction and Measurement, vol.49, no.2 pp.357-364 April 2000
[11] Martin Haardt, and Josef A. Nossek, Unitary ESPRIT: How to Obtain Increased Estimation Accuracy with a Reduced Computational Burden , IEEE Transactions on Signal Processing, vol.43, no.5, pp.1232-1243 May 1995.
[12] Yu-Jiun Ren and Jenn-Hwan Tarng, A Hybrid Spatio-Temporal Radio Channel
[13] R. G. Gallager, Information Theory and Reliable Communication, Wiley, 1968 [14] T. S. Rappaport, Wireless Communications Principles and Practice, Prentice
Hall, 1996.