Latency Analysis for Sequential Detection in Low-Complexity Binary Radio Systems
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We consider the problem of making a quick decision in favor of one of two possible physical models while the numerical measurements are acquired by sensing devices featuring minimal digitization complexity. Therefore, the digital data streams available for statistical processing are binary and exhibit temporal and spatial dependencies. Such a setting is of relevance when designing radio systems that have to perform time-critical monitoring tasks in noisy environments with low energy consumption. We assess the latency penalty in sequential decision-making resulting from using analog-to-binary instead of high-resolution analog-to-digital conversion at the front-ends. To handle the intractable multivariate binary data model, we first consider sequential tests for exponential family distributions. Within this generic probabilistic framework, we identify approximations for the log-likelihood ratio and the Kullback-Leibler divergence. The results allow designing sequential detectors for binary radio systems and analyzing their average run-time along classical arguments of Wald. In particular, the derived tests exploit the structure of the spatio-temporal correlation of the analog sensor signals engraved into the binary radio data stream. As an application, we consider the specification of binary sensing architectures in the context of cognitive radio and satellite-based synchronization where our results characterize the detection latency as a function of the temporal oversampling and the number of antennas. Finally, we evaluate the efficiency of the proposed algorithms and illustrate the accuracy of our analysis via Monte-Carlo simulations.
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