The dynamics in the temporal domain, such as data traffic for communication and status updates for sensing, can have a significant impact on the JCAS SIR meta distribution in the following ways:
Traffic Load Variation: Fluctuations in data traffic levels can lead to changes in interference patterns and signal strengths in the network. High data traffic periods may result in increased interference, affecting the SIR levels for both communication and sensing links. The JCAS SIR meta distribution would need to account for these variations to provide accurate performance insights.
Sensing Update Rates: In sensing applications, the frequency of status updates and radar measurements can influence the reliability and timeliness of information. Rapid updates may introduce additional interference or congestion in the network, impacting the SIR distribution. Analyzing how different sensing update rates affect the JCAS SIR meta distribution can reveal trade-offs between sensing accuracy and communication performance.
Temporal Correlation: Temporal correlations in data traffic and sensing updates can introduce dependencies between consecutive measurements. This correlation can affect the statistical properties of the SIR distribution, leading to variations in coverage probabilities and reliability levels. Understanding and modeling these temporal dependencies are essential for capturing the dynamic nature of JCAS networks.
By considering the dynamics in the temporal domain, the JCAS SIR meta distribution can provide insights into the time-varying performance of joint communication and sensing networks, enabling optimized resource allocation and network management strategies.