localization - Measure distance by RSSI in veins4.4 Omnet++5 SUMO0.25 -

i master student working localization in vanets in moment working on trilateration method based on rssi cooperative positioning (cp). considering analogue model : simple path loss model

but have doubts in how calculate distance correctly determined phy model. spent time (one day) reading papers of dr. sommer phy models included in veins.

would help-me solution? need way to:

1) measure power of receiver when receive beacon (i found in decider class). in decider802.11p received power can obtained line in method decider80211p::processsignalend(airframe* msg):

double recvpower_dbm = 10*log10(signal.getreceivingpower()->getvalue(start)); 

2) apply formula of rssi accordingly phy model in order achieve distance estimation between transmiter , receiver.

3) asssociate measure (distance rssi) wave short message delivered in applayer of receiver (that measuring rssi).

after read paper "on applicability of two-ray path loss models vehicular network simulation" , paper "a computationally inexpensive empirical model of ieee 802.11p radio shadowing in urban environments" , investigating how works in veins project. noticed each analogue model have own path loss model own variables describe model.

for example simplepathlossmodel have these variables defined on analoguemodels folder of veins modules:

lambda = 0.051 m (wave length ieee 802.11p cch center frequency of 5.890 ghz)

a constant alpha = 2 (default value used)

a distance factor given pow(sqrdistance, -pathlossalphahalf) / (16.0 * m_pi * m_pi);

i found 1 formula indoor environments in link, in doubt if applicable vehicular environments.

any clarification welcome. lot.

technically, correct. indeed, generate simple look-up table: have 1 vehicle drive past one, record distance , rssis, , have table can map rssi mean distance (without knowing how tx power, antenna gains, path loss model, fading models, etc, configured).

in simplest case, if assume antennas omnidirectional, path loss follows friis transmission equation, no shadow fading occurs, , fast fading negligible, table perfect.

in more complicated case, simulation includes probabilistic fast fading (say, nakagami model), shadow fading due radio obstacles (buildings), etc. table still correct, less so.

it important consider real-life application, though. consider if algorithm still works if conditions change (more reflective road surface changing reflection parameters, buildings blocking more or less power, antennas non-ideal or unknown gain characteristics, etc).