# Empirical Performance Models of MAC Protocols for Cooperative Platooning Applications
## 5 Conclusions  
In this work, we present empirical models that relate the number of RF neighbours that a platoon member, in a highway platoon-only scenario, observes with relevant network performance metrics. We show that, as we change scenario and communication parameters, there are underlying and recurrent relationships between the number of RF neighbours and the average rate of collisions (following an exponential curve if we increase platoon size, and a bell-shaped curve if we change the number of occupied lanes and transmit power of followers) and the busy time ratio (a logarithmic-linear relationship is observed in all cases), across a variety of MAC protocols. The universal model for the busy time ratio and the set of models characterizing collisions, described with respect to a practical and accessible metric, the number of RF neighbours, can support the design and operation of applications in highway platoon-only scenarios. As a noteworthy additional finding, we also report that CSMA/CA and PLEXE-slotted can enter in conditions of generalized and small-phase difference coherence between rounds of disparate platoons that profoundly degrade the performance of both metrics; and that this effect is particularly aggravated if platoon sizes are uniform.  
RA-TDMAp does not experience this effect due to its ability of inter-leaving the internal periodic communications of neighbouring platoons.  
Future work will involve continuing to seek models that abstract from circumstantial conditions. While a single, universal model has been achieved for the busy time ratio, this was not entirely achieved for the collisions metric due to the various described phenomena having different types of impact on this metric. The challenge also remains of determining whether protocols are operating in conditions of high coherence of rounds. A possible solution may pass by inferring concrete physical and/or scenario parameters, e.g., the platoon size, that offers a bijective relationship with the number of collisions. This can be difficult to learn if only the number of RF neighbours is available, as in some situations the relationship between the number of neighbours and the platoon size is not bijective (see Figure 5(b)). Additional information is required to identify this parameter and condition online, e.g., a machine learning mechanism that considers the time-series of the metric.  
We also plan to assess the impact of non-platooning traffic, other more complex propagation models, such as the two-ray model with obstructions, as well as the impact of different sizes of the beacon packets and different beacon frequency.  
Figure 12: Consolidating the metrics of all scenarios.  
Finally, we seek to carry out a validation of the models we proposed using actual IEEE 802.11p-enabled hardware.  
Conceptualization, F.S. and L.A.; Funding acquisition, L.A.; Investigation, P.M.S. and F.S.; Methodology, A.A., P.M.S., F.S. and L.A.; Project administration, L.A.; Software, A.A.; Supervision, F.S. and L.A.; Visualization, A.A.; Writing--original draft, A.A. and P.M.S.; Writing--review & editing, P.M.S. and L.A..  
This research was partially funded by FCT/MEC through national funds and when applicable co-funded by FEDER-PT2020 partnership agreement under the project UID/EEA/50008/2019 (IT) and UID/CEC/04234/2019 (CISTER) in Portugal.  
The authors declare no conflict of interest.