# Latency Analysis for Sequential Detection in Low-Complexity Binary Radio Systems

## TL;DR

This paper develops sequential detection algorithms for binary radio systems with minimal digitization, leveraging spatio-temporal correlations to analyze latency and improve decision speed in spectrum monitoring applications.

## Contribution

It introduces adaptive approximations for likelihood ratios in binary data, enabling efficient sequential detection analysis in low-complexity radio systems.

## Key findings

- Derived detection latency as a function of oversampling and antenna count
- Validated algorithms through Monte-Carlo simulations
- Characterized the impact of spatio-temporal correlation on detection performance

## Abstract

We consider the problem of making a quick decision in favor of one of two possible physical signal 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. To handle the intractable multivariate binary data model, we first consider sequential tests for exponential family distributions. Within this generic probabilistic framework, we identify adaptive 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 spatio-temporal correlation structure of the analog sensor signals engraved into the binary measurements. As an application, we consider the specification of binary sensing architectures for cognitive radio and GNSS spectrum monitoring where our results characterize the sequential 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.