2 research outputs found
Machine learning techniques for self-interference cancellation in full-duplex systems
Full-duplex (FD), enabling remote parties to transfer information simultaneously in
both directions and in the same bandwidth, has been envisioned as an important
technology for the next-generation wireless networks. This is due to the ability to
leverage both time and frequency resources and theoretically double the spectral efficiency. Enabling the FD communications is, however, highly challenging due to the
self-interference (SI), a leakage signal from the FD transmitter (Tx) to its own receiver
(Rx). The power of the SI is significantly higher when compared with the signal of
interest (SoI) from a remote node due to the proximity of the Tx to its co-located Rx.
The SI signal is thus swamping the SoI and degrading the FD system's performance.
Traditional self-interference cancellation (SIC) approaches, spanning the propagation,
analog, and/or digital domains, have been explored to cancel the SI in FD
transceivers. Particularly, digital domain cancellation is typically performed using
model-driven approaches, which have proven to be effective for SIC; however, they
could impose additional cost, hardware, memory, and/or computational requirements.
Motivated by the aforementioned, this thesis aims to apply data-driven machine
learning (ML)-assisted SIC approaches to cancel the SI in FD transceivers|in the digital
domain|and address the extra requirements imposed by the traditional methods.
Specifically, in Chapter 2, two grid-based neural network (NN) structures, referred
to as ladder-wise grid structure and moving-window grid structure, are proposed to
model the SI in FD transceivers with lower memory and computational requirements
than the literature benchmarks. Further reduction in the computational complexity
is provided in Chapter 3, where two hybrid-layers NN structures, referred to as
hybrid-convolutional recurrent NN and hybrid-convolutional recurrent dense NN, are
proposed to model the FD SI. The proposed hybrid NN structures exhibit lower computational
requirements than the grid-based structures and without degradation in the
SIC performance. In Chapter 4, an output-feedback NN structure, referred to as the
dual neurons-` hidden layers NN, is designed to model the SI in FD transceivers with
less memory and computational requirements than the grid-based and hybrid-layers
NN structures and without any additional deterioration to the SIC performance.
In Chapter 5, support vector regressors (SVRs), variants of support vector machines,
are proposed to cancel the SI in FD transceivers. A case study to assess the
performance of SVR-based approaches compared to the classical and other ML-based
approaches, using different performance metrics and two different test setups, is also
provided in this chapter. The SVR-based SIC approaches are able to reduce the training
time compared to the NN-based approaches, which are, contrarily, shown to be
more efficient in terms of SIC, especially when high transmit power levels are utilized.
To further enhance the performance/complexity of the ML approaches provided
in Chapter 5, two learning techniques are investigated in Chapters 6 and 7. Specifically,
in Chapter 6, the concept of residual learning is exploited to develop an NN
structure, referred to as residual real-valued time-delay NN, to model the FD SI with
lower computational requirements than the benchmarks of Chapter 5. In Chapter 7,
a fast and accurate learning algorithm, namely extreme learning machine, is proposed
to suppress the SI in FD transceivers with a higher SIC performance and lower training
overhead than the benchmarks of Chapter 5. Finally, in Chapter 8, the thesis
conclusions are provided and the directions for future research are highlighted