Cyber-Physical Interference Modeling for Predictable Reliability of Inter-Vehicle Communications

Predictable inter-vehicle communication reliability is a basis for the paradigm shift from the traditional singlevehicle-oriented safety and efficiency control to networked vehicle control. The lack of predictable interference control in existing mechanisms of inter-vehicle communications, however, makes them incapable of ensuring predictable communication reliability. For predictable interference control, we propose the Cyber-Physical Scheduling (CPS) framework that leverages the PRK interference model and addresses the challenges of vehicle mobility to PRK-based scheduling. In particular, CPS leverage physical locations of vehicles to define the gPRK interference model, a geometric approximation of the PRK model, for effective interference relation estimation, and CPS leverages cyber-physical structures of vehicle traffic flows (particularly, spatiotemporal interference correlation as well as macro- and micro-scopic vehicle dynamics) for effective use of the gPRK model. Through experimental analysis with high-fidelity ns-3 and SUMO simulation, we observe that CPS enables predictable reliability while achieving high throughput and low delay in communication. To the best of our knowledge, CPS is the first field deployable method that ensures predictable interference control and thus reliability in inter-vehicle communications.

💡 Research Summary

This paper addresses the critical challenge of ensuring predictable communication reliability for inter-vehicle communications (V2V), which is a fundamental requirement for safety-critical networked vehicle control systems. The authors identify the lack of predictable interference control in existing mechanisms, such as those based on IEEE 802.11p, as the root cause of unreliable and unpredictable V2V links.

The core contribution is the Cyber-Physical Scheduling (CPS) framework. CPS builds upon the Physical-Ratio-K (PRK) interference model, which is known to enable predictable reliability in static networks by transforming non-local interference problems into local ones using a parameter K. However, direct application of PRK-based scheduling to V2V networks is infeasible due to high vehicle mobility, which makes maintaining accurate signal strength maps between vehicles prohibitively costly.

To overcome the mobility challenge, CPS introduces two key innovations. First, it proposes the geometric PRK (gPRK) model, a geometric approximation of the PRK model. Instead of relying on measured signal strengths, gPRK leverages readily available physical vehicle locations (from GPS, etc.) along with known transmission powers and path-loss models to estimate interference relationships. This allows vehicles to determine if they can transmit concurrently using low-overhead control messaging.

Second, CPS systematically leverages the cyber-physical structures of vehicular traffic flows to enable effective and agile use of the gPRK model in dynamic environments. This involves:

1. Spatiotemporal Interference Correlation: Exploiting the fact that nearby links experience similar interference, allowing for quick initialization of gPRK parameters for new links based on learned parameters from neighboring links.
2. Macroscopic Vehicle Dynamics: Utilizing traffic-level patterns (e.g., density, average speed) to inform the initial estimation of gPRK parameters.
3. Microscopic Vehicle Dynamics: Using car-following models and relative kinematics to predict short-term vehicle positions, enabling more accurate and timely tracking of changing interference relationships than relying on measurements alone.

Integrated into a distributed framework, CPS operates in timeslots. Vehicles exchange control packets for neighbor discovery and location sharing. Based on feedback from data packet delivery successes/failures, each link adapts its gPRK K parameter online using a control-theoretic approach to meet its target Packet Delivery Ratio (PDR). In every timeslot (e.g., 2.5ms), each vehicle uses the estimated locations and current K parameters to determine its interference relationships with nearby vehicles. It then executes a distributed TDMA scheduling algorithm (ONAMA) to select a maximal set of non-interfering transmitters for concurrent data transmission.

Through comprehensive, high-fidelity simulation integrating ns-3 (network) and SUMO (traffic), the paper demonstrates that CPS successfully ensures predictable, application-specified communication reliability across various traffic scenarios. Simultaneously, it achieves significantly higher network throughput and lower latency compared to conventional 802.11p-based schemes. The authors position CPS as the first field-deployable solution that ensures predictable interference control and thus reliable V2V communication, providing a vital networking foundation for the future of cooperative automated driving and networked vehicle control systems.