A Bayesian Approach to D2D Proximity Estimation using Radio CSI Measurements

Abstract

Channel State Information (CSI) refers to a set of measurements used to characterize a radio communication link. Radio infrastructure collects CSI and derives useful metrics that indicate changes to modulation and coding to be made to improve the link performance (e.g. throughput, reliability). The CSI, however, has a wider potential use. It contains an environment-specific signature that can be used to extract information about users’ position and activity.

In our work, we explore the problem of proximity estimation, which consists of identifying how close a pair of devices are to each other. By assuming that Cellular Base Stations (BSs) are distributed spatially according to a Poisson Point Process (PPP), and that the channel is under Rayleigh fading, we were able to probabilistically model radio measurements and use Bayesian inference to estimate the separation between two devices given their measurements only.

We first explore a shadowless channel model, then we investigate how spatially-correlated shadowing can prove useful for estimation. For both cases, Bayesian estimators are proposed and tested through simulations. We also perform experiments and evaluate how well the estimators fit to actual data.