1 research outputs found
Model-Driven Deep Learning Method for Jammer Suppression in Massive Connectivity Systems
We present a method for separating collided signals from multiple users in
the presence of strong and wideband interference/jamming signal. More
specifically, we consider a massive connectivity setup where few, out of a
large number of users, equipped with spreading codes, synchronously transmit
symbols. The received signal is a noisy mixture of symbols transmitted through
users' flat fading channels, impaired by fast frequency hopping jamming signal
of relatively large power. In the absence of any conventional technique
suitable for the considered setup, we propose a "model-driven" deep learning
method, based on convolution neural network, to suppress jamming signal from
the received signal, and detect active users together with their transmitted
symbols. A numerical study of the proposed method confirms its effectiveness in
scenarios where classical techniques fail. As such, in a two user scenario with
wideband jamming signal of power dB above the power any active user, the
proposed algorithm achieves error rates for a wide range of AWGN
variances.Comment: 5 pages, 5 figure