Simulation of 802.11 PHY/MAC: the Quest for Accuracy and Efficiency - - PowerPoint PPT Presentation
Research Motivation Proposed Approach First Implementation Simulation of 802.11 PHY/MAC: the Quest for Accuracy and Efficiency Michele Segata Renato Lo Cigno 9th Annual Conference on Wireless On-demand Network Systems and Services January
Research Motivation Proposed Approach First Implementation
Michele Segata Renato Lo Cigno
9th Annual Conference on Wireless On-demand Network Systems and Services January 9-11, 2012, Courmayeur, Italy
Michele Segata, Renato Lo Cigno Simulation of 802.11 PHY/MAC: the Quest for Accuracy and Efficiency
Research Motivation Proposed Approach First Implementation Realism vs. Scalability YANS PhySim Shadowing
Need of realistic and scalable simulations for VANETs ns-3 choices:
ns-3 default PHY layer (YANS)
Stochastic Scalable Lack of realism
PhySim implementation by DSN Research Group (KIT)1
Emulative Not scalable Highly realistic
Other popular simulators:
ns-2 Omnet++
None consider shadowing due to obstacles Goal: provide a scalable model accurate enough for VANET simulations
Michele Segata, Renato Lo Cigno Simulation of 802.11 PHY/MAC: the Quest for Accuracy and Efficiency
Research Motivation Proposed Approach First Implementation Realism vs. Scalability YANS PhySim Shadowing
Michele Segata, Renato Lo Cigno Simulation of 802.11 PHY/MAC: the Quest for Accuracy and Efficiency
Research Motivation Proposed Approach First Implementation Realism vs. Scalability YANS PhySim Shadowing
Chunk based with BER/PER approach
Frame 1 Frame 2 Frame 3 Signal C1 C2 C3 C4 ED threshold Energy
Frame received with probability Pr(f) =
1 − Pe(ci).
Michele Segata, Renato Lo Cigno Simulation of 802.11 PHY/MAC: the Quest for Accuracy and Efficiency
Research Motivation Proposed Approach First Implementation Realism vs. Scalability YANS PhySim Shadowing
Optimistic (recently, error rate model updated by NIST) Preamble / header decoding phases missing No capture effects Fading model (i.e., Nakagami) does not consider relative speed Frame 1 Frame 2 Signal
Michele Segata, Renato Lo Cigno Simulation of 802.11 PHY/MAC: the Quest for Accuracy and Efficiency
Research Motivation Proposed Approach First Implementation Realism vs. Scalability YANS PhySim Shadowing
Emulative - DSP oriented approach Bits -> Scrambling -> Conv. encoding -> Interleaving -> Modulation -> IFFT -> GI -> Samples Signal represented as complex time samples Channel represented through tapped delay line TDL setup using data from real traces for realistic fading Drawback: traces are relative to a fixed scenario
Michele Segata, Renato Lo Cigno Simulation of 802.11 PHY/MAC: the Quest for Accuracy and Efficiency
Research Motivation Proposed Approach First Implementation Realism vs. Scalability YANS PhySim Shadowing
Reception = reverse send procedure:
Try to detect preamble and estimate freq. offset Try to decode the PLCP header Try to decode the payload
Natural reproduction of real phenomena High realism Huge computational load
Michele Segata, Renato Lo Cigno Simulation of 802.11 PHY/MAC: the Quest for Accuracy and Efficiency
Research Motivation Proposed Approach First Implementation Realism vs. Scalability YANS PhySim Shadowing
Shadowing: additional attenuation caused by obstacles Usually modelled using random fluctuations of signal energy What about this case?
A B
A single truck can cause 20 dB of attenuation (Meireles et. al., "Experimental study on the impact of vehicular
Michele Segata, Renato Lo Cigno Simulation of 802.11 PHY/MAC: the Quest for Accuracy and Efficiency
Research Motivation Proposed Approach First Implementation
Michele Segata, Renato Lo Cigno Simulation of 802.11 PHY/MAC: the Quest for Accuracy and Efficiency
Research Motivation Proposed Approach First Implementation
Create a MDP for the PHY receive procedure Tune it with results obtained through PhySim Important parameters:
Current reception phase: RP = {Preamble, Header, payLoad} Vector of interfering frames I F ∈ I = (ts, te, PW, B, ∆f, MC, ∆v) Frame under reception (described as any other frame F)
The state S of the MDP is S = {FS; FR; FD; (RP, I), E} where FS = initial state, FR/FD = absorbing states for receive/discard decision, E = environment
Michele Segata, Renato Lo Cigno Simulation of 802.11 PHY/MAC: the Quest for Accuracy and Efficiency
Research Motivation Proposed Approach First Implementation
Michele Segata, Renato Lo Cigno Simulation of 802.11 PHY/MAC: the Quest for Accuracy and Efficiency
Research Motivation Proposed Approach First Implementation Captures Shadowing Fading
Features: PHY state machine with captures Simple environment description (cars and trucks) for shadowing effects Uses the NIST BER model
Michele Segata, Renato Lo Cigno Simulation of 802.11 PHY/MAC: the Quest for Accuracy and Efficiency
Research Motivation Proposed Approach First Implementation Captures Shadowing Fading
2 4 6 8 10 100 200 300 400 500 Percentage of capture frames (%) Number of vehicles (#) Percentage of PHY captures, 8 lanes, 802.11p, 6Mbps, 500 Bytes,10Hz, 5dB thr. Total Preamble Header Payload 2 4 6 8 10 100 200 300 400 500 Percentage of capture frames (%) Number of vehicles (#) Percentage of PHY captures, 8 lanes, 802.11p, 6Mbps, 500 Bytes,10Hz, 5dB thr. Total Preamble Header Payload
ED thr. = -104 dBm, Preamble BUSY over -65 dBm ED thr. = -85 dBm, Preamble BUSY over -85 dBm Michele Segata, Renato Lo Cigno Simulation of 802.11 PHY/MAC: the Quest for Accuracy and Efficiency
Research Motivation Proposed Approach First Implementation Captures Shadowing Fading
A B
20 40 60 80 100 1 2 3 4 5 6 7 8 9 Percentage of frames received (%) Number of obstructing trucks (#) Without shadowing Shadowing 20 dB, linear Shadowing 20 dB, geometric Shadowing 10 dB, linear Shadowing 10 dB, geometric
Figure: Payload 500 bytes, data rate 6 Mbps
Michele Segata, Renato Lo Cigno Simulation of 802.11 PHY/MAC: the Quest for Accuracy and Efficiency
Research Motivation Proposed Approach First Implementation Captures Shadowing Fading
Work in progress. Can take 1 hour to process 100-200 frames
0.2 0.4 0.6 0.8 1 5 10 15 20 25 30 Fraction of frames received SINR (dB) NIST model ∆v 14 m/s ∆v 28 m/s ∆v 50 m/s ∆v 72 m/s
Figure: Payload 500 bytes, data rate 6 Mbps
Michele Segata, Renato Lo Cigno Simulation of 802.11 PHY/MAC: the Quest for Accuracy and Efficiency
Conclusion The End Appendix Conclusion
Currently available stochastic models are not precise enough for VANETs (PHY, fading, shadowing) A DSP-like approach harms scalability, but is useful for understanding and model derivation An MDP-like approach with enough information can improve precision
Michele Segata, Renato Lo Cigno Simulation of 802.11 PHY/MAC: the Quest for Accuracy and Efficiency
Conclusion The End Appendix
Contacts: {msegata,locigno}@disi.unitn.it
Michele Segata, Renato Lo Cigno Simulation of 802.11 PHY/MAC: the Quest for Accuracy and Efficiency
Conclusion The End Appendix Frequency Offset PHY layer behavior
5 10 15 20 SINR (dB)
20 40 60 80 Frequency offset (ppm) 0.2 0.4 0.6 0.8 1 Fraction of preamble drops (#)
5 10 15 20 SINR (dB)
20 40 60 80 Frequency offset (ppm) 0.2 0.4 0.6 0.8 1 Fraction of header drops (#)
5 10 15 20 SINR (dB)
20 40 60 80 Frequency offset (ppm) 0.2 0.4 0.6 0.8 1 Fraction of payload drops (#)
Michele Segata, Renato Lo Cigno Simulation of 802.11 PHY/MAC: the Quest for Accuracy and Efficiency
Conclusion The End Appendix Frequency Offset PHY layer behavior
0.2 0.4 0.6 0.8 1
5 10 15 20 Fraction of frames (#) SINR (dB) Successfully received Drop at preamble Drop at header Drop at payload
Michele Segata, Renato Lo Cigno Simulation of 802.11 PHY/MAC: the Quest for Accuracy and Efficiency