Presentation given by Andrei Stoica on 19th June 206 in Gothenburg, Sweden, at CCP workshop co-located with IEEE Intelligent Vehicles Conference

1. On Prototyping IEEE802.11p Channel
Estimators in Real-World Environments
Using GNURadio
Razvan-Andrei Stoica, Stefano Severi, Giuseppe Abreu
r.stoica@jacobs-university.de
Focus Area Mobility - Jacobs University Bremen (GERMANY)
June 19, 2016

2. Vehicular Ad-hoc Networks in ITS
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3. Wireless Channel in VANETs: Relevance
ITS Applications require robust wireless communications.
⇓
Knowledge of the wireless channel is fundamental!
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4. Wireless Channel in VANETs: Relevance
ITS Applications require robust wireless communications.
⇓
Knowledge of the wireless channel is fundamental!
⇓
Ranging may be refined and improved using Channel
State Information (CSI).
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5. Wireless Channel in VANETs: Characteristics
Vehicular WiFi [2] ↔ IEEE 802.11p [3].
Key Challenges:
Non-stationary channel distributions,
Short coherence times,
Short / medium link availability,
Frequency selective channels,
Fast fading and shadowing.
[2] ETSI EN 302 665 V1.1.1, European Standard (Telecommunications series): Intelligent Transport Systems (ITS); Communications Architecture, September 2010.
[3] IEEE Std. 802.11p-2010: “IEEE Standard for Information Technology - Telecommunications and Information Exchange Between Systems - Local and Metropolitan
Area Networks - Specific Requirements, Part 11: Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) Specifications, Amendment 6: Wireless
Access in Vehicular Environments” , July 2010.
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6. Proposal Overview
Objective
Design a new practical channel estimation scheme and
prototype its real-world deployment with Software Defined
Radio (SDR).
Strategy
Implement the SOF algorithm[4] in GNURadio and
contribute to an open-source community of engineers and
enthusiats.
[4] R. A. Stoica, S. Severi, and G. T. F. de Abreu, “Learning the vehicular channel through the self-organization of frequencies,” in Vehicular Networking Conference
(VNC), 2015 IEEE, Dec 2015, pp. 68-75.
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7. GNURadio in a Nutshell
What is it?
Open-source community driven framework for SDRs[5].
“Linux” for Software Defined Radios and Digital Signal
Processing.
[5] GNU Radio Website, accessed June 2016. [Online]. Available at: http://www.gnuradio.org
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8. GNURadio in a Nutshell
Why use GNURadio?
open-source and free
large tutorial pool and thorough documentation
flowchart based modeling
many signal processing blocks implementated by default
C++ / Python based block programming
rapid prototyping through software
modular packaging of custom solutions
easy blocks and code reuse
SDR hardware prices go down
hardware reuse for different projects
[5] GNU Radio Website, accessed June 2016. [Online]. Available at: http://www.gnuradio.org
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9. GNURadio IEEE 802.11p Rx Prototyping
How to use GNURadio in given context?
Start from existent module of IEEE 801.11p transceiver
[6],[7], integrate and test researched channel estimation
solution on the Rx side.
[6] B. Bloessl, M. Segata, C. Sommer, and F. Dressler, “Towards an open source IEEE 802.11p stack: A full SDR-based transceiver in GNURadio,” in Vehicular
Networking Conference (VNC), 2013 IEEE, Dec 2013, pp. 143-149.
[7] B. Bloessl, gr-ieee802-11 GNURadio module, accessed June 2016. [Online]. Available at: https://github.com/bastibl/gr-ieee802-11.
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10. GNURadio IEEE 802.11p Rx Prototyping
How to use GNURadio in given context?
Start from existent module of IEEE 801.11p transceiver
[6],[7], integrate and test researched channel estimation
solution on the Rx side.
[6] B. Bloessl, M. Segata, C. Sommer, and F. Dressler, “Towards an open source IEEE 802.11p stack: A full SDR-based transceiver in GNURadio,” in Vehicular
Networking Conference (VNC), 2013 IEEE, Dec 2013, pp. 143-149.
[7] B. Bloessl, gr-ieee802-11 GNURadio module, accessed June 2016. [Online]. Available at: https://github.com/bastibl/gr-ieee802-11.
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11. The SOF Algorithm - Overview
What is the SOF ?
Improved channel estimator compliant with IEEE 802.11p
OFDM PHY specifications and packet format [3].
Why use SOF ?
Superior performance than typical LS estimation and SotA
sequential schemes (e.g. STA [8], CDP [9]) and lower
complexity and latency than iterative schemes (e.g.
soft-input soft-output turbo decoder [10]) [4].
[8] J. A. Fernandez, K. Borries, L. Cheng, B. Vijaya Kumar, D. D. Stancil, and F. Bai, “Performance of the 802.11p physical layer in vehicle-to-vehicle environments,”
Vehicular Technology, IEEE Transactions on, vol. 61, no. 1, pp. 3-14, 2012.
[9] Z. Zhao, X. Cheng, M. Wen, L. Yang, and B. Jiao, “Constructed Data Pilot-Assisted Channel Estimators for Mobile Environments,” IEEE Transactions on Intelligent
Transportation Systems, vol. 16, no. 2, pp. 947–957, April 2015.
[10] P. Alexander, D. Haley, and A. Grant, “Cooperative intelligent transport systems: 5.9-ghz field trials,” Proceedings of the IEEE, vol. 99, no. 7, pp. 1213-1235, July
2011.
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12. The SOF Algorithm - How does it work ?
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13. The SOF Algorithm - Practical Amendments
Prototyping with GNURadio revealed two “issues”:
high SNR performance improvement possible by adaptive
MA filtering
constellation drifting due to sequential estimation and low
resolution comb pilots
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14. The SOF Algorithm - Practical Amendments
MA Adaptive Filtering Optimization
Problem
MA filtering refines LS estimates for low and medium
SNRs, but decays performance in high SNR regimes.
Solution
Linearly adapt the MA filtering window. Use a wide
window (max. 11 taps) for low SNRs and decrease the
width up to just one tap (no filtering) for high SNRs.
Adapt filtering window by thresholding — up to 30 dB allow
MA and then turn it off upon results in [4].
σ(ˆρ)=



2.79, ˆρ ≤ 0 dB
−2.33
30 ˆρ + 2.79, ˆρ ∈ (0, 30] dB
0.46, ˆρ > 30 dB
. (1)
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15. The SOF Algorithm - Practical Amendments
Constellation Drifting Fix
Problem
IEEE 802.11p comb pilots cannot lock and correct the
phase offset of the symbols constellation → errors.
Solution
Correlate previous CFR estimate with current one to
estimate and correct the phase offset.
ˆφcorr(i) = ∠
Nsc−1
k=0
ˆHSOF,i(k) ˆH∗
SOF,i−1(k)
Nsc · | Nsc−1
k=0
ˆHSOF,i(k) ˆH∗
SOF,i−1(k)|
, (2)
ˆHcorr
SOF,i(k) ˆHSOF,i(k) · exp(−j ˆφcorr(i)). (3)
Works due to initial training block → fixed reference locked.
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16. The SOF Algorithm - Pseudocode
1: Compute initial LS estimate from training symbols: ˆH0
2: Estimate SNR: ˆρ
3: Compute filter window: MA(·)
4: Filter initial estimate: ˆHSOF,0 = MA( ˆH0)
5: for symbol i = 1 to F do
6: Get OFDM symbol estimate: ˆXi = Yi/ ˆHSOF,i−1
7: Equalize OFDM Tx symbol: ˆXTX,i −→ ˆXi
8: Estimate current symbol SNR: ˆρ
9: Compute filter window: MA(·)
10: Filter intermediate estimate: ˜Hi = MA( ˆHi)
11: Update SOF channel estimate:
ˆHSOF,i = ˆHSOF,i−1 + γ( ˜Hi − ˆHSOF,i−1)
12: Correct phase rotation:
ˆφcorr(i), ˆHSOF,i ← ˆHSOF,i · exp(−j ˆφcorr(i))
13: end for
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17. Testing the Implementation
Strategy
Perform unit tests and system tests.
unit tests ↔ loopback Tx-Rx chain targeting channel
estimation algorithms.
system tests ↔ real-world Tx-Rx communication focusing
on packet delivery and general operation.
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18. Unit Testing
loopback GNURadio interfaces (does not require RF HW);
builds on top of IEEE 802.11p transceiver [7];
similar to MATLAB simulations;
computes FER at different SNRs on emulated channels;
implements 2 vehicular channels [11].
Channel v[km/h] Doppler [Hz] Max. τs[µs]
V2V
express-way
oncoming
300-400 m
210 1000-1200 0.3
V2I
urban canyon
100 m
32-48 300 0.5
[11] G. Acosta-Marum and M. A. Ingram, “Six Time-and Frequency-selective empirical channel models for vehicular wireless LANs,” Vehicular Technology Magazine,
IEEE, vol. 2, no. 4, pp. 4-11, 2007.
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19. Performance Evaluation - FER 100B PSDU
V2V(top), V2I(bottom) @ QPSK 1/2
0 5 10 15 20 25 30 35
10
−2
10
−1
10
0
Es/N0 dB
FER
LS
CDP
SOF
0 5 10 15 20 25 30 35
10
−2
10
−1
10
0
Es/N0 dB
FER
LS
CDP
SOF
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20. Performance Evaluation - FER 300B PSDU
V2V(top), V2I(bottom) @ QPSK 1/2
0 5 10 15 20 25 30 35
10
−2
10
−1
10
0
Es/N0 dB
FER
LS
CDP
SOF
0 5 10 15 20 25 30 35
10
−2
10
−1
10
0
Es/N0 dB
FER
LS
CDP
SOF
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21. System Testing
runs real-time communication (requires RF HW [12],[13]);
uses implemented IEEE 802.11p Rx;
computes FER based on short-term statistics;
displays results in real-time GUI for evaluation and debug;
tested only indoor (for the moment).
[11] Cohda Wireless, accessed June 2016. [Online]. Available at: http://cohdawireless.com/Portals/0/MK5_OBU_10122015.pdf
[12] Ettus Research, accessed June 2016. [Online]. Available at:
https://www.ettus.com/content/files/07495_Ettus_N200-210_DS_Flyer_HR_1.pdf.
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22. Performance Evaluation - Indoor Testing
Real-Time OFDM Equalized Symbol Snapshot
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23. Conclusions and Future Work
Take Home Points
SDRs: future of modular multi-purpose radio design
GNURadio: easy-to-use “Linux” of SDRs
CSI Knowledge: key of robust communication and ranging
SOF: IEEE 802.11 p(/a/g)-compliant, performant and
low-complexity channel estimation algorithm
Next Steps
SOF Optimization: antenna diversity and
deparameterization by channel correlations
Field Trials: setup outdoor V2X test framework of SDR vs.
reference devices (e.g. Cohda Mk5)
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24. Thank you!