  Random access schemes are widely used in IoT wireless access networks to
accommodate simplicity and power consumption constraints. As a result, the
interference arising from overlapping IoT transmissions is a significant issue
in such networks. Traditional signal detection methods are based on the
well-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 IoT
transmissions, deep learning approaches are being considered as a better
alternative. In this paper, we present a hybrid framework, HybNet, that
switches between deep learning and match filter pathways based on the detected
interference level. This helps the detector work in a broader range of
conditions, optimally leveraging the matched filter and deep learning
robustness. We compare the performance of several possible data modalities and
detection architectures concerning the interference-to-noise ratio,
demonstrating that the proposed HybNet surpasses the complex conjugate matched
filter performance under interference-limited scenarios.
