Inference in Linear Observations with Multiple Signal Sources: Analysis of Approximate Message Passing and Applications to Unsourced Random Access in Cell-Free Systems

Here we consider a problem of multiple measurement vector (MMV) compressed sensing with multiple signal sources. The observation model is motivated by the application of {\em unsourced random access} in wireless cell-free MIMO (multiple-input-multiple-output) networks. We present a novel (and rigorous) high-dimensional analysis of the AMP (approximate message passing) algorithm devised for the model. As the system dimensions in the order, say $\mathcal O(L)$, tend to infinity, we show that the empirical dynamical order parameters -- describing the dynamics of the AMP -- converge to deterministic limits (described by a state-evolution equation) with the convergence rate $\mathcal O(L^{-\frac 1 2})$. Furthermore, we have shown the asymptotic consistency of the AMP analysis with the replica-symmetric calculation of the static problem. In addition, we provide some interesting aspects on the unsourced random access (or initial access) for cell-free systems, which is the application motivating the algorithm

Collaborative Ad Transparency: Promises and Limitations

International audienceSeveral targeted advertising platforms offer transparency mechanisms, but researchers and civil societies repeatedly showed that those have major limitations. In this paper, we propose a collaborative ad transparency method to infer, without the cooperation of ad platforms, the targeting parameters used by advertisers to target their ads. Our idea is to ask users to donate data about their attributes and the ads they receive and to use this data to infer the targeting attributes of an ad campaign. We propose a Maximum Likelihood Estimator based on a simplified Bernoulli ad delivery model. We first test our inference method through controlled ad experiments on Facebook. Then, to further investigate the potential and limitations of collaborative ad transparency, we propose a simulation framework that allows varying key parameters. We validate that our framework gives accuracies consistent with real-world observations such that the insights from our simulations are transferable to the real world. We then perform an extensive simulation study for ad campaigns that target a combination of two attributes. Our results show that we can obtain good accuracy whenever at least ten monitored users receive an ad. This usually requires a few thousand monitored users, regardless of population size. Our simulation framework is based on a new method to generate a synthetic population with statistical properties resembling the actual population, which may be of independent interest

Collaborative Ad Transparency: Promises and Limitations

International audienceSeveral targeted advertising platforms offer transparency mechanisms, but researchers and civil societies repeatedly showed that those have major limitations. In this paper, we propose a collaborative ad transparency method to infer, without the cooperation of ad platforms, the targeting parameters used by advertisers to target their ads. Our idea is to ask users to donate data about their attributes and the ads they receive and to use this data to infer the targeting attributes of an ad campaign. We propose a Maximum Likelihood Estimator based on a simplified Bernoulli ad delivery model. We first test our inference method through controlled ad experiments on Facebook. Then, to further investigate the potential and limitations of collaborative ad transparency, we propose a simulation framework that allows varying key parameters. We validate that our framework gives accuracies consistent with real-world observations such that the insights from our simulations are transferable to the real world. We then perform an extensive simulation study for ad campaigns that target a combination of two attributes. Our results show that we can obtain good accuracy whenever at least ten monitored users receive an ad. This usually requires a few thousand monitored users, regardless of population size. Our simulation framework is based on a new method to generate a synthetic population with statistical properties resembling the actual population, which may be of independent interest