Measurement-based Assessment of Space Diversity over Indoor Fixed Wireless Channels

1. Measurement-based Assessment of Space Diversity over Indoor Fixed Wireless Channels Presented by: Chengyu Wang Supervisor: Prof. Dave Michelson Radio Science Lab University of British Columbia

2. Agenda • Introduction • How do sample length and inter-sample correlation affect the accuracy of Rician K-factor estimates obtained from time-series data? • What do narrowband CW measurements reveal about the nature of the body-shadowed indoor fixed wireless channel? • How can the results of measurement-based modeling be used in dynamic simulations of the indoor channel? • Conclusions and Future Work

3. 1. Introduction: Motivation • An increasing number of short-range wireless systems are being deployed in indoor environments, e.g., Bluetooth, ZigBee, WLAN. • Many of these systems (Bluetooth, simple ASK/FSK/BPSK systems) are effectively narrowband. • Comparatively few experimental studies of space diversity performance in indoor environments, especially fixed wireless environments. • Simulation studies (e.g., based upon ray-tracing tool) have severe limitations when used to characterize complicated and dynamic indoor environments.

4. 1. Introduction: Objective and Approach • Objective: characterize the manner in which body shadowing between transmitter and receiver affects the indoor fixed wireless channel, including fading statistics, time variation (Doppler spread) and space diversity correlation. • Approach: a narrowband measurement campaign was conducted to characterize the indoor fixed wireless channels.

5. 2. Estimation of Rician K-factor: Introduction • Where the received signal is dominated by a single fixed component, the received signal envelope often follows a Rician distribution • Rician K-factor is a key parameter to describe the ratio of the power in the fixed component to the power in the time-varying/scattered component • As K  0, the Rician distribution reduces to the Rayleigh distribution. • How many samples are required to estimate the Rician K-factor from time-series data?

6. 2. Estimation of Rician K-factor: Algorithm • We used a moment-based Rician K-factor approach proposed by Greenstein, Michelson and Erceg [1]. • Let the received signal be |x(t)|2 where x(t) = V + v(t) • A simple and rapid approach that K-factor is a function of moments estimated from measurements of received power versus time. where Ga is the time average power gain, Gv is rms fluctuation of the power gain G about Ga.

7. 2. Estimation of Rician K-factor: Simulation on the sample size and sampling rate • The accuracy of K-factor estimation increases as the total sample size increases. • The accuracy of K-factor estimation decreases as the correlation between the successive samples increases (a function of the Doppler spectrum) • Thus, we need to investigate the impact of sample size and sampling rate on the accuracy of Rician K-factor estimation. • This is a useful result for those planning measurement campaigns in indoor fixed wireless environments.

8. 2. Estimation of Rician K-factor: Simulation Structure • Used IDFT algorithm to simulate Rayleigh fading signals [2]. • Added an LOS component to obtain a Rician Fading Envelope. • Doppler filter response matches the average of the Doppler spectra estimated from measurements in 4th floor hallway of MacLeod building.

9. 2. Estimation of Rician K-factor: Results • Estimation RMSE vs. Sample Size: RMSE decreases as sample size increases RMSE vs. Sample Size as fs=2fd • Estimation RMSE vs. Sampling Rate: RMSE increases as sampling rate increases RMSE vs. Sampling Rate as K=10

10. 3. Characterization of Indoor Fixed Wireless Channels:Introduction • Estimation of CDF: comparatively less results concerning the body shadowed indoor fixed wireless environments. Need to confirm whether the received signal envelopes follow Rayleigh or Rician distribution. • Estimation of Doppler Spectrum: comparatively less results have been proposed for the environment of our interest. Need to investigate how the Doppler spectrum is affected by people moving in the vicinity of transmitter or receiver. • Estimation of Spatial Correlation for Space Diversity: few studies have been addressed for the effectiveness of space diversity in indoor fixed wireless channels. Need to characterize the spatial correlation of received signal envelopes in such cases.

11. 3. Characterization of Indoor Fixed Wireless Channels:Narrowband Measurement Platform • Transmitter • Marconi Signal Generator • Cisco Dipole Antenna • Receiver • Mock Access Point • Agilent Network Analyzer 8753E (used as dual-channel receiver) • GPIB ENET adapter • Laptop PC

12. 3. Characterization of Indoor Fixed Wireless Channels:Environment Description • After site survey in MacLeod building, MCLD 323 and 458 were chosen to conduct the measurements. • MCLD323 is a typical office environment with desks, chairs and cubicle panels. MCLD458 is a typical laboratory environment which is full of computers and electronic equipment and has over three times area of MCLD323. • People moving paths are planned to between the transmitter and receiver. The moving speed is 3 km/h (0.83m/s).

13. 3. Characterization of Indoor Fixed Wireless Channels:Results - CDF An example of CDF for MacLeod 323 An example of CDF for MacLeod 458 • The CDFs of all the 160 measured data sets were compared to the corresponding Rayleigh and Rician CDFs. In all cases, a Rician distribution with appropriate K-factor offers a far better fit than a Rayleigh distribution, as shown in the plots above.

14. 3. Characterization of Indoor Fixed Wireless Channels:Results - Doppler Spectrum • The resulting Doppler spectra were estimated from the measured power samples by the approach in [3]. • The average of estimated Doppler spectra in MacLeod 323 shows that the spectrum width for levels of -5, -10 and -25 dB are 0.7, 1.5 and 12 Hz. These results are consistent with previous investigations for fixed wireless environment. • The average of estimated Doppler spectra in MacLeod 458 shows that the spectrum width for levels of -5, -10 and -25 dB are 0.2, 1.2 and 14 Hz. A sideband effect is observed at around 6.5 Hz, which corresponds to a velocity of 3 km/h.

15. 3. Characterization of Indoor Fixed Wireless ChannelsResults – Spatial Correlation • By examining the average spatial correlations as a function of antenna spacing, all the correlations are found to below 0.7, which is typically considered to be low enough for effective diversity reception. • By comparing the average spatial correlations with idealized theoretical results, the correlation trends show a reasonable agreement. The results give us confidence that the measurement campaign is suitable for use in more ambitious measurement campaigns in the future.

16. 4. Measurement-based Model for Space Diversity Assessment: Introduction • Given the estimated CDF, Doppler spectra and spatial correlation, we show how these characteristics can be incorporated into the simulation of space diversity assessment. • The diversity gains were estimated based on the simplifying assumption that the mean signal strength and K-factor on both branches are identical.

17. 4. Measurement-based Model for Space Diversity Assessment: Envelope Validation • The CDF and Doppler spectrum of the single branch of measured signal envelope and simulated signal envelope were compared for validation purposes and show a good agreement.

18. 4. Measurement-based Model for Space Diversity Assessment: Diversity Gain Validation • The measured and simulated diversity gains were also compared; the results show that, although there exist some discrepancies in CDFs, the diversity gain estimates are reasonably accurate.

19. 4. Measurement-based Model for Space Diversity Assessment: Results Estimation of Diversity Gain for MacLeod 323 with 90, 95 and 99% Reliability levels • Rician K-factor equal 5 is typically considered to be a medium K-factor. The table above gives us how diversity gain would be in such a scenario. • The estimated diversity gains (with K=0) were compared with the classical theoretical analysis, small variations (within 2%) were observed due to the limited sample size of simulation.

20. 5. Conclusions • How sample length and inter-sample correlation (expressed in the form of the Doppler spectrum) affect the accuracy of Rician K-factor estimates obtained from time-series data: • Point 1 • Point 2 • What narrowband CW measurements reveal about the nature of the body-shadowed indoor fixed wireless channel: • Received signal envelope: Rician • Doppler spectrum: “peaky” shape with small sidebands due to body motion • Space diversity correlation: Very low (< 0.7) for small antenna spacings

21. 5. Conclusions • How the results of measurement-based modeling be used in dynamic simulations of the indoor channel: • Example: implementation of a simulation tool for dual-branch narrowband diversity channel based upon previous work • Example: estimation of space-diversity gain • These results give us confidence that our measurement system is suitable for use in more ambitious measurement campaigns in the future.

22. 5. Recommendations for Future Work • Determine the extent to which novel diversity schemes, including various types of three-way diversity, provide improvement in indoor and microcell environments • Lee’s three-way diversity • Tripole antenna • Modify the simulation methodology employed in [4] to account for the sidebands observed in the Doppler spectra presented in Chapter 3 • Determine how the number of samples required to estimate Rician K factor is affected by the shape of the Doppler spectrum, e.g., classical U-shape, central peaky shape and the modulated Doppler spectrum described in Chapter 3.

23. References [1]. L. J. Greenstein, D. G. Michelson, and V. Erceg, “Moment-method estimation of the Ricean K-factor,” IEEE Commun. Lett., vol. 3, pp. 175–176, June 1999. [2]. D. J. Young and N. C. Beaulieu, “The generation of correlated Rayleigh random variate by inverse discrete Fourier transform,” IEEE Trans. Commun., vol. 48, no. 7, pp. 1114–1127, July 2000. [3]. A. Domazetovic, L. J. Greenstein, N. B. Mandayam, and I. Seskar, “Estimating the Doppler spectrum of a short-range fixed wireless channel,” IEEE Commun. Lett., vol. 7, no. 5, pp. 227–229, May 2003. [4]. S. Thoen, L. V. D. Perre, and M. Engels, “Modeling the channel time-variance for fixed wireless communications,” IEEE Commun. Lett., vol. 6, pp. 331–333, Aug. 2002.

24. End of presentation

25. 3. Characterization of Indoor Fixed Wireless Channels:Measurement Parameter Setting • Receiver Parameters • Transmitter Parameters