# Hybrid-Layers Neural Network Architectures for Modeling the Self-Interference in Full-Duplex Systems

arXiv: https://arxiv.org/abs/2110.09997 · HTML: https://mlsanity.com/paper/2110.09997/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 40 references that are in the map.
- First posted on arXiv 18 Oct 2021.

## Builds on (most cited first, 13 of 13)

- [An Introduction to Deep Learning for the Physical Layer](https://mlsanity.com/paper/1702.00832/) (2017)
- [A Vision of 6G Wireless Systems: Applications, Trends, Technologies, and Open Research Problems](https://mlsanity.com/paper/1902.10265/) (2019)
- [Towards 6G Networks: Use Cases and Technologies](https://mlsanity.com/paper/1903.12216/) (2019)
- [Nonlinear Interference Mitigation via Deep Neural Networks](https://mlsanity.com/paper/1710.06234/) (2017)
- [Non-Linear Digital Self-Interference Cancellation for In-Band Full-Duplex Radios Using Neural Networks](https://mlsanity.com/paper/1711.00379/) (2017)
- [Passive Self-Interference Suppression for Full-Duplex Infrastructure Nodes](https://mlsanity.com/paper/1302.2185/) (2013)
- [Low Complexity Neural Network Structures for Self-Interference Cancellation in Full-Duplex Radio](https://mlsanity.com/paper/2009.11361/) (2020)
- [Design and Implementation of a Neural Network Aided Self-Interference Cancellation Scheme for Full-Duplex Radios](https://mlsanity.com/paper/1812.00449/) (2018)
- [Hardware Implementation of Neural Self-Interference Cancellation](https://mlsanity.com/paper/2001.04543/) (2020)
- [Simultaneous Transmission and Reception: Algorithm, Design and System Level Performance](https://mlsanity.com/paper/1309.5546/) (2013)
- [Joint Blind Identification of the Number of Transmit Antennas and MIMO Schemes Using Gerschgorin Radii and FNN](https://mlsanity.com/paper/1803.10849/) (2018)
- [On the Spectral and Energy Efficiencies of Full-Duplex Cell-Free Massive MIMO](https://mlsanity.com/paper/1910.01294/) (2019)
- [Advanced Machine Learning Techniques for Self-Interference Cancellation in Full-Duplex Radios](https://mlsanity.com/paper/1912.06818/) (2019)

## Built on by (most cited first, 3 of 3)

- [Deep-Learning Channel Estimation for IRS-Assisted Integrated Sensing and Communication System](https://mlsanity.com/paper/2402.09441/) (2024)
- [Extreme Learning Machine-based Channel Estimation in IRS-Assisted Multi-User ISAC System](https://mlsanity.com/paper/2402.09440/) (2024)
- [Autoencoder for Position-Assisted Beam Prediction in mmWave ISAC Systems](https://mlsanity.com/paper/2511.18594/) (2025)

## Abstract (arXiv)

Full-duplex (FD) systems have been introduced to provide high data rates for beyond fifth-generation wireless networks through simultaneous transmission of information over the same frequency resources. However, the operation of FD systems is practically limited by the self-interference (SI), and efficient SI cancelers are sought to make the FD systems realizable. Typically, polynomial-based cancelers are employed to mitigate the SI; nevertheless, they suffer from high complexity. This article proposes two novel hybrid-layers neural network (NN) architectures to cancel the SI with low complexity. The first architecture is referred to as hybrid-convolutional recurrent NN (HCRNN), whereas the second is termed as hybrid-convolutional recurrent dense NN (HCRDNN). 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. The key idea behind using hybrid layers is to build an NN model, which makes use of the characteristics of the different layers employed in its architecture. More specifically, in the HCRNN, a convolutional layer is employed to extract the input data features using a reduced network scale. Moreover, a recurrent layer is then applied to assist in learning the temporal behavior of the input signal from the localized feature map of the convolutional layer. In the HCRDNN, an additional dense layer is exploited to add another degree of freedom for adapting the NN settings in order to achieve the best compromise between the cancellation performance and computational complexity. Complexity analysis and numerical simulations are provided to prove the superiority of the proposed architectures.