# Toward Intelligent Roads: Uniting Sensing and Communication in Mobile Networks
## 3 Cooperative Data Fusion in JSC Network for Vehicular Scenarios
### Resource Allocation Strategies
In our case study, the OSPA decreased from over 2 m in the non-cooperative case (i.e., with \(n_{\rm s}=1\) BS) to 0.6 m when \(n_{\rm s}=3\) BSs were employed (see right-hand side plot in Figure 5). Clearly, allocating more BSs exclusively for communication purposes increases the overall network capacity. Again with reference to Figure 5, it is worth noting that the vertical dotted lines indicate the values beyond which allocating additional resources no longer provides benefits for sensing and only results in a loss of resources for communication. Overall, the integration of the tracking algorithm, along with target recognition, led to significant performance improvements. For instance, in the scenario with \(\rho_{\rm P}=0.4\), \(\rho_{\rm t}=1\), \(\rho_{\rm f}=0.6\), and \(n_{\rm s}=3\), the OSPA metric improved markedly, reducing from 1.5 m without tracking to 0.5 m with tracking. This highlights the critical role of tracking in enhancing system performance, particularly in challenging scenarios involving closely spaced or extended targets. Moreover, in Figure 6, the system's target recognition capability was evaluated by varying the fraction of subcarriers and the number of monostatic BSs cooperating to classify the target. Classification performance is measured through accuracy, which is defined as the ratio of correctly classified targets by the CNN to the total number of objects detected by the tracking algorithms. The CNN was trained to recognize two types of targets: car or pedestrian. It can be observed that for both PHD and MBM, the CNN successfully identified the target category when the number of BSs was \(n_{\rm s}\geq 2\), with its accuracy exceeding 0.8. For example, with \(n_{\rm s}=3\) and \(\rho_{\rm f}\geq 0.4\), the accuracy rose above 0.9. Finally, in Figure 7, the localization performance of cooperative sensing was evaluated over time by varying the clustering strategy and tracking algorithm. A significant performance improvement, indicated by stable OSPA values over time, was observed when the system's target recognition capability was used to adapt the clustering gate for both tracking algorithms (blue curves compared to the green and yellow ones). This confirms the effectiveness of dynamically adjusting system parameters based on the target type. \begin{table}
\begin{tabular}{l l l l} \hline \(f_{\text{c}}\) [GHz] & 28 & Beamwidth [deg] & 2.4 \\ \(B\) [MHz] & 400 & Number of BS \(n_{\text{s}}\) & 3 \\ Active subcarriers \(K\) & 3168 & EIRP [dBm] & 30 \\ \(\Delta f=B/K\) [kHz] & 120 & Surveillance area [m\({}^{2}\)] & \(50\times 50\) \\ OFDM symbols direc. \(M_{\text{s}}\) & 112 & Number of pedestrians & 0 to 2 \\ Number of antennas & 50 & Number of cars & 2 to 3 \\ \hline \end{tabular}
\end{table} 
Table 1: JSC system and scenario parameters. Figure 5: Sensing and communication capabilities versus the fraction of power \(\rho_{\text{p}}\), the fraction of subcarriers \(\rho_{\text{r}}\), the fraction of time slots \(\rho_{\text{t}}\), and the number of sensors \(\text{n}_{\text{s}}\). The PHD and MBM tracking algorithms (continuous lines) are compared to the case without tracking using OSPA (see the numerical scale on the left in meters). The average downlink aggregate capacity of the BSs is represented in blue (see the numerical scale on the right in bit/s). Figure 6: Target recognition accuracy versus the fraction of subcarriers \(\rho_{\text{f}}\) fixing the fraction of power \(\rho_{\text{P}}=0.4\) and the fraction of time slots \(\rho_{\text{t}}=1\). Solid curves are generated considering \(n_{\text{s}}=3\), dashed with \(n_{\text{s}}=2\), and dotted using \(n_{\text{s}}=1\). Green curves represent the PHD performance, while yellow stands for MBM accuracy.