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 9-11, 2012, Courmayeur, Italy Michele Segata, Renato Lo Cigno Simulation of 802.11 PHY/MAC: the Quest for Accuracy and Efficiency
Realism vs. Scalability Research Motivation YANS Proposed Approach PhySim First Implementation Shadowing Reasons Of This Work 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
Realism vs. Scalability Research Motivation YANS Proposed Approach PhySim First Implementation Shadowing ns-3 Models’ Description Michele Segata, Renato Lo Cigno Simulation of 802.11 PHY/MAC: the Quest for Accuracy and Efficiency
Realism vs. Scalability Research Motivation YANS Proposed Approach PhySim First Implementation Shadowing YANS - Stochastic model Chunk based with BER/PER approach Energy Frame 1 Frame 2 Frame 3 Signal C1 C2 C3 C4 ED threshold Frame received with probability � P r ( f ) = 1 − P e ( c i ) . c i ∈ f Michele Segata, Renato Lo Cigno Simulation of 802.11 PHY/MAC: the Quest for Accuracy and Efficiency
Realism vs. Scalability Research Motivation YANS Proposed Approach PhySim First Implementation Shadowing YANS - Stochastic model 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
Realism vs. Scalability Research Motivation YANS Proposed Approach PhySim First Implementation Shadowing PhySim - Emulative model 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
Realism vs. Scalability Research Motivation YANS Proposed Approach PhySim First Implementation Shadowing PhySim - Emulative model 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
Realism vs. Scalability Research Motivation YANS Proposed Approach PhySim First Implementation Shadowing A note on shadowing Shadowing: additional attenuation caused by obstacles Usually modelled using random fluctuations of signal energy What about this case? B A A single truck can cause 20 dB of attenuation (Meireles et. al., "Experimental study on the impact of vehicular obstructions in VANETs", VNC 2010) Michele Segata, Renato Lo Cigno Simulation of 802.11 PHY/MAC: the Quest for Accuracy and Efficiency
Research Motivation Proposed Approach First Implementation Proposed Approach Michele Segata, Renato Lo Cigno Simulation of 802.11 PHY/MAC: the Quest for Accuracy and Efficiency
Research Motivation Proposed Approach First Implementation Idea: Markov Decision Process Create a MDP for the PHY receive procedure Tune it with results obtained through PhySim Important parameters: Current reception phase: R P = { Preamble , Header , payLoad } Vector of interfering frames � I F ∈ � I = ( t s , t e , PW , B , ∆ f , MC , ∆ v ) Frame under reception (described as any other frame F ) The state S of the MDP is S = { F S ; F R ; F D ; ( R P ,� I ) , E } where F S = initial state, F R / F D = 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 MDP Graphical Representation Michele Segata, Renato Lo Cigno Simulation of 802.11 PHY/MAC: the Quest for Accuracy and Efficiency
Research Motivation Captures Proposed Approach Shadowing First Implementation Fading First implementation 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 Captures Proposed Approach Shadowing First Implementation Fading Fraction of frames generating a capture, 5 dB thr. Percentage of PHY captures, 8 lanes, 802.11p, 6Mbps, 500 Bytes,10Hz, 5dB thr. Percentage of PHY captures, 8 lanes, 802.11p, 6Mbps, 500 Bytes,10Hz, 5dB thr. 10 10 Total Total Preamble Preamble Header Header Payload Payload 8 8 Percentage of capture frames (%) Percentage of capture frames (%) 6 6 4 4 2 2 0 0 0 100 200 300 400 500 0 100 200 300 400 500 Number of vehicles (#) Number of vehicles (#) 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 Captures Proposed Approach Shadowing First Implementation Fading Impact of trucks on frame reception B A 100 Percentage of frames received (%) 80 Without shadowing Shadowing 20 dB, linear 60 Shadowing 20 dB, geometric Shadowing 10 dB, linear Shadowing 10 dB, geometric 40 20 0 0 1 2 3 4 5 6 7 8 9 Number of obstructing trucks (#) 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 Captures Proposed Approach Shadowing First Implementation Fading Impact of relative speed Work in progress. Can take 1 hour to process 100-200 frames 1 0.8 Fraction of frames received 0.6 0.4 0.2 NIST model ∆ v 14 m/s ∆ v 28 m/s ∆ v 50 m/s ∆ v 72 m/s 0 0 5 10 15 20 25 30 SINR (dB) 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 Conclusion 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 That’s all! Thanks for listening! Questions? Contacts: {msegata,locigno}@disi.unitn.it Michele Segata, Renato Lo Cigno Simulation of 802.11 PHY/MAC: the Quest for Accuracy and Efficiency
Conclusion Frequency Offset The End PHY layer behavior Appendix ∆ f effects on preamble, header and payload Fraction of preamble drops (#) 1 1 Fraction of header drops (#) 0.8 0.8 0.6 0.6 0.4 0.4 0.2 -5 0.2 -5 0 0 0 5 0 5 -80 -80 -60 -60 10 SINR (dB) 10 SINR (dB) -40 -40 -20 -20 0 0 15 15 20 20 40 40 Frequency offset (ppm) 60 20 Frequency offset (ppm) 60 20 80 80 1 Fraction of payload drops (#) 0.8 0.6 0.4 0.2 -5 0 0 5 -80 -60 -40 10 SINR (dB) -20 0 15 20 40 60 Frequency offset (ppm) 20 80 Michele Segata, Renato Lo Cigno Simulation of 802.11 PHY/MAC: the Quest for Accuracy and Efficiency
Conclusion Frequency Offset The End PHY layer behavior Appendix PHY layer behavior – Noise floor only 1 0.8 Fraction of frames (#) 0.6 Successfully received Drop at preamble Drop at header Drop at payload 0.4 0.2 0 -5 0 5 10 15 20 SINR (dB) Michele Segata, Renato Lo Cigno Simulation of 802.11 PHY/MAC: the Quest for Accuracy and Efficiency
Recommend
More recommend
