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

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

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

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

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

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

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

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

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

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

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

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

Results – Radio Beamform 13 wireless rotating table spectrum analyzer

Results – Radio Beamform 14 up to 10dB variation!

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

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

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

Backup Slides 18

Result – Antenna Shape 19

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