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Random access schemes are widely used in IoT wireless access networks toaccommodate simplicity and power consumption constraints. As a result, theinterference arising from overlapping IoT transmissions is a significant issuein such networks. Traditional signal detection methods are based on thewell-established matched filter using the complex conjugate of the signal,which is proven as the optimal filter under additive white Gaussian noise.However, with the colored interference arising from the overlapping IoTtransmissions, deep learning approaches are being considered as a betteralternative. In this paper, we present a hybrid framework, HybNet, thatswitches between deep learning and match filter pathways based on the detectedinterference level. This helps the detector work in a broader range ofconditions, optimally leveraging the matched filter and deep learningrobustness. We compare the performance of several possible data modalities anddetection architectures concerning the interference-to-noise ratio,demonstrating that the proposed HybNet surpasses the complex conjugate matchedfilter performance under interference-limited scenarios.

HybNet: A Hybrid Deep Learning -- Matched Filter Approach for IoT Signal Detection