# Toward Intelligent Roads: Uniting Sensing and Communication in Mobile Networks
## 4 Conclusions and Future Perspectives  
We have provided a perspective on JSC, reviewing various setups--namely, monostatic, bistatic, multistatic, and multiple monostatic--while focusing on the dual functionality of communication and sensing, as well as the strengths and limitations of each configuration. We have emphasized the urgent need for a standardized JSC channel that incorporates multipath components and accommodates extended targets, particularly in complex and highly dynamic scenarios such as vehicular environments. Target classification has been addressed as a key feature to enable target-specific tracking and optimize resource allocation parameters. Additionally, we explored the intricate domain of resource management, considering the allocation of power, frequency, time, and the number of sensors. Finally, to demonstrate the benefits of synergy between resource allocation strategies, cooperative sensing, and target recognition, we presented numerical results in a realistic vehicular case study. The numerical results demonstrate that cooperation among three BSs enables the localization of both extended and point-like targets, with an error of less than 80 cm, while maintaining a downlink capacity exceeding 10 Gbit/s for a UE experiencing an signal-to-noise ratio (SNR) of 8 dB when considering \(\rho_{\rm p}=0.4\), \(\rho_{\rm f}=0.6\), and \(\rho_{\rm t}=0.5\). While a large body of literature overlooks the role of tracking in JSC systems, our numerical results reveal significant improvements when tracking is incorporated, highlighting its critical contribution to the overall system performance. Additionally, the integration of the CNN for target recognition demonstrates its effectiveness in identifying target categories as either car or pedestrian, with its accuracy exceeding 0.8 for two cooperating BSs and surpassing 0.9 when three BSs cooperate and \(\rho_{\rm f}\geq 0.4\). While this work addressed key challenges in cooperative sensing and data fusion for JSC systems, several promising avenues remain for future exploration. The data fusion technique proposed in this study, which enables cooperative sensing among monostatic JSC BSs via soft map exchange at a fusion center, could be further optimized through advanced beamforming strategies [37]. Specifically, integrating multicell coordination and distributed MIMO radar functionalities could balance sensing and communication objectives, enhancing waveform diversity and improving resolution in complex urban  
Figure 7: Localization performance (OSPA) over time for the PHD (**top**) and MBM (**bottom**). Target-type information is utilized by the clustering algorithms to adapt their measurement selection gates. The green dashed curves indicate a fixed tight gate, while the yellow dashed curves represent a fixed large gate. The blue solid curves correspond to adaptive gating based on target classification. Finally, the gray dashed curves represent the results obtained when tracking is not applied. scenarios like the one evaluated in this work. Additionally, the use of reconfigurable intelligent surfaces (RISs) offers a complementary approach to overcome the propagation challenges highlighted in this study, such as multipath and occlusions [38]. By strategically deploying RISs, it would be possible to enhance the detection and tracking of shadowed or dim targets, including pedestrians, while simultaneously improving localization precision for extended targets like vehicles. Furthermore, the analysis of resource trade-offs in this study highlights the importance of exploring distributed sensing in cell-free massive MIMO systems. Deploying dense antenna arrays across a wide area could improve spatial resolution and enable fine-grained detection, especially for weak or extended targets. Scaling antenna deployments dynamically to match specific sensing needs could make such systems highly adaptable to a variety of applications, from traffic monitoring to cooperative autonomous driving. Conceptualization, W.X.