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We present a method for separating collided signals from multiple users inthe presence of strong and wideband interference/jamming signal. Morespecifically, we consider a massive connectivity setup where few, out of alarge number of users, equipped with spreading codes, synchronously transmitsymbols. The received signal is a noisy mixture of symbols transmitted throughusers' flat fading channels, impaired by fast frequency hopping jamming signalof relatively large power. In the absence of any conventional techniquesuitable for the considered setup, we propose a "model-driven" deep learningmethod, based on convolution neural network, to suppress jamming signal fromthe received signal, and detect active users together with their transmittedsymbols. A numerical study of the proposed method confirms its effectiveness inscenarios where classical techniques fail. As such, in a two user scenario withwideband jamming signal of power $20$ dB above the power any active user, theproposed algorithm achieves error rates $10^{-2}$ for a wide range of AWGNvariances.

Model-Driven Deep Learning Method for Jammer Suppression in Massive Connectivity Systems