Deep learning based channel equalization for MIMO ISI channels
Author(s)
Advisor
Duman, Tolga MeteDate
2022-09Publisher
Bilkent University
Language
English
Type
ThesisItem Usage Stats
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Abstract
Future wireless communications is expected to bring significant changes along
with a number of emerging technologies such as 5G, virtual reality, edge computing,
and IoT. These developments pose unprecedented demands in terms of
capacity, coverage, latency, efficiency, flexibility, compatibility, and quality of experience
on wireless communication systems. Machine Learning (ML) techniques
are considered as a promising tool to tackle this challenge due to their ability to
manage big data, powerful nonlinear mapping, and distributed computing capabilities.
There have been many research results addressing different aspects of
ML algorithms and their connections to wireless communications; however, there
are still various challenges that need to be addressed. In particular, their use for
communication systems with memory, is not fully investigated. With this motivation,
this thesis considers an application of ML, in particular, deep learning (DL),
techniques for communications over intersymbol interference (ISI) channels.
In this thesis, we propose DL-based channel equalization algorithms for channels
with ISI. We introduce three different DL-based ISI detectors, namely sliding
bidirectional long short term memory (Sli-BiLSTM), sliding multi layer perceptron
(Sli-MLP), and sliding iterative (Sli-Iterative), and demonstrate that they
are computationally efficient and capable of performing equalization under a variety
of channel conditions with the knowledge of the channel state information.
We also employ sliding bidirectional gated recurrent unit (Sli-BiGRU) and Sli-
MLP, which are more suitable for use with fixed ISI channels. As an extension,
we also examine DL-based equalization techniques for multiple-input multipleoutput
(MIMO) ISI channels. Numerical results show that proposed models are
well suited for equalization of ISI channels with perfect as well as noisy CSI for
a broad range of signal-to-noise ratio (SNR) levels as long as the ISI length is
very close to the optimal solution, namely, the maximum likelihood sequence estimation,
implemented through the Viterbi Algorithm while having considerably
less complexity, and they have superior performance compared to MMSE-based
channel equalization.
Keywords
Deep learningNeural networks
Intersymbol interference
MIMO detection
Equalization
Wireless communications