no code implementations • 18 Oct 2021 • Mohamed Elsayed, Ahmad A. Aziz El-Banna, Octavia A. Dobre, Wanyi Shiu, Peiwei Wang
In contrast to the state-of-the-art NNs that employ dense or recurrent layers for SI modeling, the proposed NNs exploit, in a novel manner, a combination of different hidden layers (e. g., convolutional, recurrent, and/or dense) in order to model the SI with lower computational complexity than the polynomial and the state-of-the-art NN-based cancelers.
no code implementations • 23 Sep 2020 • Mohamed Elsayed, Ahmad A. Aziz El-Banna, Octavia A. Dobre, Wanyi Shiu, Peiwei Wang
The core idea of these two structures is to mimic the non-linearity and memory effect introduced to the SI signal in order to achieve proper SI cancellation while exhibiting low computational complexity.
