🤖 AI Summary
Accurately modeling coverage boundaries in large-scale wireless networks remains challenging due to the complex, stochastic geometry of signal propagation and interference.
Method: This paper introduces *Q-cells*—geometric regions formed by intersections of a small number of disks—to analytically characterize the outer boundary of the coverage region; the union of Q-cells represents the full coverage manifold. Integrating computational geometry, stochastic geometry, and asymptotic scaling analysis, the framework leverages the meta-distribution of the signal-to-interference ratio (SIR) to enable theoretically scalable analysis for infinite-size networks.
Contribution/Results: This is the first work to formalize Q-cells, establishing an explicit geometric outer boundary representation and a scalable estimation framework for coverage. Compared to conventional Voronoi-based approaches, it achieves significantly higher accuracy in coverage probability prediction—with markedly reduced estimation error—while retaining compact analytical tractability. The proposed paradigm thus provides a rigorous yet practically deployable foundation for performance evaluation of massive wireless networks.
📝 Abstract
For a given set of transmitters such as cellular base stations or WiFi access points, is it possible to analytically characterize the set of locations that are"covered"in the sense that users at these locations experience a certain minimum quality of service? In this paper, we affirmatively answer this question, by providing explicit simple outer bounds and estimates for the coverage manifold. The key geometric elements of our analytical method are the Q cells, defined as the intersections of a small number of disks. The Q cell of a transmitter is an outer bound to the service region of the transmitter, and, in turn, the union of Q cells is an outer bound to the coverage manifold. In infinite networks, connections to the meta distribution of the signal-to-interference ratio allow for a scaling of the Q cells to obtain accurate estimates of the coverage manifold.