# Resource Management for Collaborative 5G-NR-V2X RSUs to Enhance V2I/N Link Reliability
## 3 System Model of Cellular V2X Network
### Abstraction of the Physical Layer  
In this section, we obtain the packet error rate (PER) by computing VUE throughput using specific modulation and coding levels for abstraction in the physical layer (PHY). 5G-NR uses LDPC coding rather than turbo coding used in LTE. The 15-channel quality indicator (CQI) indexes using the modulation coding scheme (MCS) that provides up to 256 QAM, achieving different spectral efficiencies at various MCS levels, represent 5G-NR. In these PHY calculations, we must assume that urban environment VUE reception signals have a perfect time and frequency synchronization; otherwise, obtaining PERs through system-level simulation (SLS) and performing measurements considering all transmission and reception architectures for signal processing is expensive and time-consuming. Therefore, it is necessary to simplify and apply limited assumptions and systems for simulation analysis, using link-level simulation (LLS) to evaluate analysis results under realistic conditions. For LLS utilization, we implement and apply the LLS curve of the additive white Gaussian noise (AWGN) channel, using the Vienna 5G Link Level Simulator [24]. The simulation results shown in Figure 2. Compare with the simulation results in LTE, 5G NR, and the analysis performed on the AWGN channel with a CQI value of 1 to 15 [25]. Simulation results provide calibration values for effective SINR mapping in feedback calculations, along with comparison of LLS performance [24]. Therefore, we conduct link-to-system (L2S) mapping based on the results using mutual information effective SNR mapping (MIESM). Effective SNR mapping (ESM) algorithms provide the important link-layer abstraction for SLS. Thus, for MIESM application in this study, we compute based on nonlinear mapping relationships from SINR to mutual information to compute an effective SINR, utilizing (4) and (5) to provide a higher accuracy than other ESM methods.  
\[SINR_{eff}=\beta\cdot I^{-1}\Bigg{(}\frac{1}{N}\sum\limits_{n=1}^{N}I\Big{(} \frac{SINR_{n}}{\beta}\Big{)}\Bigg{)} \tag{4}\]  
\[\begin{split} I_{mp}(x)=m_{p}-E_{y}\Bigg{\{}\frac{1}{2^{\text{m}p }}\sum\limits_{i=1}^{mp}\sum\limits_{b=0}^{1}\sum\limits_{\begin{subarray}{ c}\varepsilon_{0}\leq X_{b}^{\dagger}\end{subarray}}log\frac{\sum\limits_{ \begin{subarray}{c}\varepsilon_{0}\leq X_{b}^{\dagger}\end{subarray}}\exp(A) }{\sum\limits_{\begin{subarray}{c}\varepsilon_{0}\leq X_{b}^{\dagger}\end{subarray}} \exp(A)}\Bigg{\}}\\ A=-\big{|}Y-\sqrt{x/\beta}\big{(}\tilde{x}-z\big{)}\big{|}^{2}\end{split} \tag{5}\]  
Figure 2: SNR vs. PER curve comparing LLS results when using turbo coding in the LTE environment, and LLS results when using LDPC coding in the 5G\(-\)NR environment.  
Here, \(\beta\) is the calibration coefficient selected to minimize the mean square error between the effective SNR derived from the Rayleigh channel and the fixed SNR [26], and such measurements are based on the mutual information function, \(I(SINR_{n})\). Furthermore, \(I_{mp}\) in Equation (5) represents the number of bits per symbol for the chosen modulation scheme, where \(X\) is the symbol set and \(Y\) is a zero-mean complex Gaussian variable with unit variance [27].