1. Capacity Variation of Indoor Radio MIMO Systems Using a Deterministic Model A. GrennanDIT C. DowningDIT B. FoleyTCD

2. Introduction n MIM0 = MULTIPLE INPUT MULTIPLE OUTPUT n Antenna Array Implementation n Radio Channel Efficiency Improvement n Multipath Reflections Utilized

3. Structure of the presentation n Ray-tracing room model n Validation of the model n MIMO systems theory n Results of investigations n Conclusions and direction of future work

4. Why use a Ray-tracing Model? n Modeling is an inexpensive alternative to site specific measurements n Traditional statistical models have focussed on power coverage requirements n Ray-tracing provides information on specific direction of arrival of rays n Antenna parameters and other physical aspects of room may be accurately modeled n A custom tool permits easy modification of all parameters

5. Typical Ray-trace 3-D

6. Validation of Model n Developed laboratory experiment n Employed established techniques for measurements n Directly compared simulated and measured rays n Determined delay spread from measured data and compared to simulated prediction

8. Simulated and Measured Data Single Reflective Surface

9. Simulated and Measured Data (2 surfaces)

11. Simulated and Measured Delay Spread

12. High Bit Rate Radio using Multi-element Antennas (MIMO System) n System proposed by Foshini and Gans et al of Lucent Technologies n Standard radio channel capacity increases by 1 bit/sec/Hz for 3dB increase in SNR n Using multi-element antennas the capacity increases linearly with the number of elements in the array n This capacity increases without limit and is not restricted by multipath

13. Multipaths in 4 X 4 system and Channel matrix For a single channel the efficiency is C/B = log 2 (1+ |h ij | 2 *  ) where C is the bit rate, B is the channel bandwidth and  is the signal to noise ratio and where is the wavelength assuming frequency of 5.2 GHz (60 mm)

14. The gain for the array is determined by calculating the eigenvalues, i of HH*. Thus, the overall system efficiency is given by Virtual parallel channels in 4 X 4 system

15. RESULTS Random matrix versus simulated measurements n RMS delay spread and Capacity n Effect of increased element spacing and signal correlation

16. Random versus Simulated 1 S/N 18 dB /2 antenna element spacing

17. Random versus Simulated 2 S/N 18 dB 5 antenna element spacing

18. Random versus Simulated comment Random matrix does not accurately model fluctuations due to movement of arrays relative to reflective surfaces And Is only idicative of results when gain is at a maximun (center of room) or the element spacing in the array is large, thus decorrelating signal components.

19. RMS delay spread   and This is the power weighted impulse response of the channel where the first moment and second moments of the power delay profile are defined

20. Simulated RMS delay spread

21. RMS delay spread with Spectral Efficiency S/N 18 dB /2 antenna element spacing

22. RMS delay spread comment In case of small number of elements and when the spacing of the elements is small the delay spread is a good indication of the efficency fluctuation with distance but not when larger arrays are used. The traditional radio system designer would seek to position antennas so as to minimise delay spread but the opposite is required for mimo systems.

23. Comparison of array sizes S/N 18 dB /2 antenna element spacing

24. Effect of increasing element spacing S/N 18 dB /2 and 8 Element spacing

25. Signal correlations and Capacity element spacing 4x4 system Signal correl -ation Pair 1 Signal correl -ation Pair 2 Mean bits/ sec/ hz /2 0.97180.967411.7 2 0.97380.935013.0 5 0.95100.852917.9 8 0.93260.790620.9

26. Blocked line-of-sight 1 S/N 18 dB 4x4 system /2 antenna element spacing

27. Blocked line-of-sight 2 S/N 18 dB 8x8 system /2 antenna element spacing

28. Conclusions n Random matrices are limited in predicting mimo performance for indoor environment n Location of transmitter/receiver pair may have to be chosen carefully to avoid ‘nulls’ n Antenna element spacing/signal correlation is the most critical factor limiting system efficiency indoors