  Narrowband internet-of-things (NB-IoT) is a competitive 5G technology for
massive machine-type communication scenarios, but meanwhile introduces
narrowband interference (NBI) to existing broadband transmission such as the
long term evolution (LTE) systems in enhanced mobile broadband (eMBB)
scenarios. In order to facilitate the harmonic and fair coexistence in wireless
heterogeneous networks, it is important to eliminate NB-IoT interference to LTE
systems. In this paper, a novel sparse machine learning based framework and a
sparse combinatorial optimization problem is formulated for accurate NBI
recovery, which can be efficiently solved using the proposed iterative sparse
learning algorithm called sparse cross-entropy minimization (SCEM). To further
improve the recovery accuracy and convergence rate, regularization is
introduced to the loss function in the enhanced algorithm called regularized
SCEM. Moreover, exploiting the spatial correlation of NBI, the framework is
extended to multiple-input multiple-output systems. Simulation results
demonstrate that the proposed methods are effective in eliminating NB-IoT
interference to LTE systems, and significantly outperform the state-of-the-art
methods.
