Resilient large-scale cognitive radio ad hoc networking using path-time codes

Cognitive radio ad hoc networks (CRAHNs) emerge as a spectrum efficient networking technology to enable autonomous machine-to-machine communication among massive number of IoT devices. However, spectrum sharing results in opportunistic links and CRAHN becomes a kind of opportunistic networks. To reduce latency in CRAHN and to achieve overall spectrum efficiency by avoiding tremendous feedback signaling, CRAHNs of open-loop physical layer transmission open a new avenue under massive operations. The new technology challenge associated with such new CRAHNs lies in error control with only local networking information without relying on feedback control over each opportunistic link. Path-time codes virtually realizing multi-input-multi-output over network layer have been innovated to resolve such a dilemma. However, effective multipath routing considering interference remains unclear. In this paper, be taking network topological factors and interference into account, we analytically derive SINR approximations to design power control and multi-path greedy routing. By stochastic geometry analysis, we also show that the resilient operation for large-scale CRAHNs can be facilitated with the aid of path-time codes.