Learning the Wireless Interference Graph via Local Probes

Due to the densification and ambitious spectral efficiency targets, wireless networks are becoming increasingly interference-limited. Effectively managing the interference and allocation of communication resources strongly hinges on knowing the interference signals. Acquiring such information, however, is often infeasible as the scale and complexity of the wireless network grow. This paper proposes an algorithm for learning the interference signals by aggregating the minimal data collected by individual users. Specifically, each user has a binary quantization of the interference it observes by each of the other users. The proposed algorithm aggregates these local binary data to form an estimate of the interference network. Sufficient conditions for optimal inference are delineated, and the proposed algorithm is demonstrated to enjoy certain optimality guarantees.