## 5 Performance Evaluation
### Simulation  
In this section, we conduct simulations to verify the analysis above. Since we focus on the purely algorithmic aspect of the sensing-based SPS and our proposal to enhance it, and the problem formulation is simple, we use a homegrown simulator written in Python. We run ten instances of each simulation instance to obtain narrow enough confidence intervals for the results. We assume that UEs are static, and for the channel propagation, we assume the simple disc model where all UEs within the communication range enjoy the perfect physical channel. The only exception to the static UE locations is one experiment where the population churn, due to new UEs joining in the communication range and others leaving, is modeled and its impact is observed. Please note that our intention in this paper is to demonstrate the point that even under perfect physical-layer conditions, the current standard SPS algorithm exhibits serious shortcomings in resolving resource conflicts and providing high-reliability communication. Due to the perfect propagation model, the spatial distribution of the UEs is immaterial. Towards the end of this paper, however, we briefly discuss the expected impacts of a more realistic physical configurations in terms of the wireless channel. In particular, we consider how such imperfect physical channel will affect the use of the lookahead and the consequent performance impacts.  
For the simulation experiments, we use the following assumptions. First, the packet size is uniform at 300 bytes, and each packet requires a single-subframe resource. (For the optimization focusing on the uneven packet sizes, readers are referred to Molina-Masegosa [33].) Second, the sensing-based candidate resource selections (i.e., \(S_{A}\) and \(S_{B}\)) have been already performed, at which instant our simulation starts. Packets are periodically transmitted every \(RRI=100\) ms. Following the standard, we set the RC range at \((C1,C2)=(5,15)\). For the selection window, we set \((T1,T2)=(1,100)\). We turn off the retransmission, so each BSM is transmitted only once. For the resource pool, we assume that a 20-MHz channel at 5.9 GHz is completely dedicated to Mode 4 using adjacent PSCCH + PSSCH subchannelization. We assume that there are \(N_{subCH}=25\) subchannels of 8 RBs each are defined per subframe. The maximum number of UEs is 2500 if their RRI is 100 ms, and 500 if RRI is 20 ms. The simulation parameters are summarized in Table 1.  
For the performance metric, here we use the collision probability. Please note that it is not the collision probability of the reselection operation that we considered in the analysis above. Instead,  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Default Value** \\ \hline Retransmission & off \\ RRI & 100 ms \\ Sensing window & 1000 ms \\ BSM size & 300 bytes \\ \(RC_{LA}\) & 1 \\ \(L_{subCH}\) & 1 \\ \(N_{subCH}\) & 25 \\ \(RB/subCH\) & 8 \\ \(T1,T2\) & 1, 100 \\ \(C1,C2\) & 5, 15 \\ \(probResourceKeep\) & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters used in simulation.  
Figure 6: Collision probabilities upon reselection under SPS and SPS/LA.  
we use the fraction of the single-frame resources to which more than one packet arrived, out of the entire set of single-subframe resources generated throughout the simulation. Each simulation lasts 100 s, so the total number of single-subframe resources are 2,500,000. The presented results are averages of 10 simulation runs.