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

Question

I am a master student working with localization in VANEts in this moment I am working on a trilateration method based on RSSI for Cooperative Positioning (CP). I am considering the Analogue Model : Simple Path Loss Model

But I have some doubts in how to calculate the distance correctly for a determined Phy Model. I spent some time (one day) reading some papers of Dr. Sommer about the PHY models included in veins.

Would anyone help-me with this solution? I need a way to:

1) Measure the power of an receiver when its receive a beacon (I found this in the Decider class). In the Decider802.11p the received Power can be obtained with this line in method Decider80211p::processSignalEnd(AirFrame* msg):

double recvPower_dBm = 10*log10(signal.getReceivingPower()->getValue(start));

2) Apply a formula of RSSI accordingly the phy model in order to achieve a distance estimation between transmiter and receiver.

3) Asssociate this measure (distance by RSSI) with the Wave Short Message to be delivered in AppLayer of the receiver (that is measuring the RSSI).

After read the paper "On the Applicability of Two-Ray Path Loss Models for Vehicular Network Simulation" and the paper "A Computationally Inexpensive Empirical Model of IEEE 802.11p Radio Shadowing in Urban Environments" and investigating how it works in the veins project. I noticed that each analogue model have your own path loss model with your own variables to describe the model.

For example for the SimplePathLossModel we have these variables defined on AnalogueModels folder of veins modules:

lambda = 0.051 m (wave length to IEEE 802.11p CCH center frequency of 5.890 GHz)

A constant alpha = 2 (default value used)

a distance factor given by pow(sqrDistance, -pathLossAlphaHalf) / (16.0 * M_PI * M_PI);

I found one formula for indoor environments in this link, but I am in doubt if it is applicable for vehicular environments.

Any clarification is welcome. Thanks a lot.

Answer 1

Technically, you are correct. Indeed, you could generate a simple look-up table: have one vehicle drive past another one, record distance and RSSIs, and you have a table that can map RSSI to mean distance (without knowing how the TX power, antenna gains, path loss model, fading models, etc, are configured).

In the simplest case, if you assume that antennas are omnidirectional, that path loss follows the Friis transmission equation, that no shadow fading occurs, and that fast fading is negligible, your table will be perfect.

In a more complicated case, where your simulation also includes probabilistic fast fading (say, a Nakagami model), shadow fading due to radio obstacles (buildings), etc. your table will still be roughly correct, but less so.

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