Incorporating dynamic network topologies and user mobility patterns into the SG-based ISAC modeling framework can significantly enhance the accuracy and applicability of the analysis. One approach to extend the framework is by integrating time-varying parameters into the existing models. For dynamic network topologies, the point process distributions can be modified to account for the movement of devices over time. Instead of assuming static device locations, the framework can incorporate mobility models such as random waypoint or random walk to simulate the changing positions of communication and sensing devices.
Moreover, the channel models can be adapted to capture the time-varying nature of wireless channels in dynamic networks. By incorporating fading models that consider mobility-induced effects like Doppler shifts and path loss variations, the framework can provide more realistic channel predictions. Additionally, the association strategies between devices can be optimized dynamically based on real-time network conditions, ensuring efficient resource allocation and performance enhancement in dynamic environments.
Furthermore, leveraging machine learning algorithms to predict user mobility patterns and network topology changes can enhance the predictive capabilities of the SG-based ISAC modeling framework. By training models on historical data and continuously updating them with real-time information, the framework can adapt to evolving network dynamics and provide more accurate performance evaluations and optimization strategies.