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Validation of Radio Channel Models using an Anechoic Chamber Yuhao Zheng, David M. Nicol University of Illinois at Urbana-Champaign 1.

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Presentation on theme: "Validation of Radio Channel Models using an Anechoic Chamber Yuhao Zheng, David M. Nicol University of Illinois at Urbana-Champaign 1."— Presentation transcript:

1 Validation of Radio Channel Models using an Anechoic Chamber Yuhao Zheng, David M. Nicol University of Illinois at Urbana-Champaign 1

2 Outline Introduction & anechoic chamber Experimental framework Radio channel models Experiment results Conclusions & future works 2

3 Introduction Wireless network simulation is popular Fidelity is a problem – Especially for radio channel model – Higher layers depend on physical layer Tradeoff: accuracy ↔ computational cost – Simple models: free space, two ray – Complex models: raytracing, Transmission Line Matrix (TLM) 3

4 Our Focus Complex models: Raytracing, TLM – Received signal strength Sensitivity experiments – Small changes in environment – How does a model reflect this? Problems – Need accurate measured value for validation – Anechoic chamber 4 Tx Rx

5 Anechoic Chamber Illinois Wireless Wind Tunnel (iWWT) Characteristics – No outside interferences – No inside reflections Ideal wireless testbed – “Free space” inside 5

6 record RSS transmit pkts Experimental Framework 6 chamber wall Soekris Engineering net4521 wireless node attenuator (directional) reflector (material varies) 20 ft 11 ft experiment measured model predicted compare & validate

7 Simple Raytracing Model 7 Wireless node  single point – Assumption: omnidirectional antenna Attenuator  fixed pathloss coefficient – Depends on direction Reflector  line – Material-dependent reflection rate, tuned offline N aiai aeae didi dede n points Contribution of this single reflection path:  a series of points

8 Advanced Raytracing Model 8 direct path reflected path Im Re More general radio model – Single point  point matrix N aiai aeae didi dede n points

9 Transmission Line Matrix Model Even-based Transmission Line Matrix [Nutaro’06] Space  cells displacement  state A cell can change state when – External event: from adjacent cells – Internal event: when not at equilibrium position Implementation details – Grid size = λ/D, D is tunable – Source: sinusoidal – RSS: average over time 9

10 Experimental Results 10 20 ft 11 ft large-scale movement small-scale movement direction A direction B

11 Results – Large-scale Movement 11 direction A  direction B  can capture the peak but not exact shape ~2dB error can capture the peak but not exact shape ~2dB error

12 Results – Small-scale Movement 12 direction A  direction B  cannot capture the shape ~2dB error cannot capture the shape ~2dB error

13 Results – Radio Beamform 13 wireless box @ rotating table spectrum analyzer

14 Results – Radio Beamform 14 up to 10dB variation!

15 Results – Resolution of Raytracing 15 converged, n=9 is good

16 Results – Resolution of TLM 16 not converged, D=8 is the best

17 Conclusions & Future Works Conclusions – 2dB error of both raytracing & TLM – Model uncertainty > error eliminated by chamber – Validation outside the chamber may be okay Future works – Quantify the speed of different models – Consider the beamform of antenna 17

18 Backup Slides 18

19 Result – Antenna Shape 19

20 Title text 20

21 Title text 21


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