Name: WESLEY DA SILVA COSTA
Publication date: 20/04/2023
Examining board:
Name |
Role |
|---|---|
| ALEXANDRE DE ALMEIDA PRADO POHL | Examinador Externo |
| HELDER ROBERTO DE OLIVEIRA ROCHA | Coorientador |
| JAIR ADRIANO LIMA SILVA | Presidente |
| MARCELO EDUARDO VIEIRA SEGATTO | Examinador Interno |
| MARIA JOSE PONTES | Examinador Interno |
Pages
Summary: In recent years, the increasing applications of Internet of Things (IoT) and smart devices have
accelerated the demand for signal bandwidth. However, radiofrequency (RF) wireless systems
cannot meet this upcoming need because of spectrum congestion in urban areas and insufficient
bandwidth, mainly in indoor environments. These facts pave the way for alternatives to reduce
the pressure of the RF spectrum in such conditions and also ensure high data rates, low latency,
reliability, and low cost. The advance of Light-Emitting Diode (LED) technology provided high
energy efficiency lighting with a high-speed light intensity switching. These facts, along with
the possible spectrum crunch, have given rise to research interests in Visible Light Communication
(VLC), through which data are transmitted using the existing lighting infrastructure. VLC
offers a complementary alternative to radio-based systems, with an unlicensed optical spectrum
(approximately 400 THz), security at the physical layer, low power, high speed, and immunity
to RF electromagnetic interference.
A high data rate can be achieved by combining the broadband VLC channel and multicarrier
modulation schemes. Orthogonal Frequency Division Multiplexing (OFDM) is largely studied
because of its spectral efficiency promotion and capacity to deal with multipath fading.
However, the nonlinearity introduced by the LED offers a challenge to the OFDM parameters
settings, due to its high Peak-to-Average Power Ratio (PAPR). This work tackles the challenge
of conveying OFDM signal considering the Intermodulation Distortion (IMD) produced by the
LED nonlinearity.
This Thesis addresses the nonlinear LED effects, VLC parameters, and its performance for
conventional and constant-envelope OFDM. The VLC system is modeled and optimization algorithms
are evaluated to achieve parameters that provide maximum power and spectral efficiencies,
constrained by modulation figure of metrics: bit error rate and error vector magnitude.
This work also presents the application of artificial neural networks in the physical layer of
VLC systems. The Long Short-Term Memory (LSTM) neural network is applied to predict
future positions, as well as channel gain, and also forecast optimized parameters. Additionally,
an investigation of the OFDM equalization using deep learning architectures for a multipath
single-input single-output VLC channel is proposed. Convolutional Neural Network (CNN)
architectures are applied in a direct OFDM mapped symbols equalization, without channel estimation,
interpolation, nor element-wised division, denominated Convolutional Neural Network
Direct-Equalizer (CNN-DE).
Results show that the optimization based on meta-heuristics was capable to determine VLC parameters in order to satisfy the communication constraints. Additionally, they emphasize
the trade-off between power and spectral efficiency in VLC: higher spectral efficiency is
achieved with the increase of offset current (power) to deal with the IMD; in contrast, to achieve
higher power efficiency, a lower spectral efficiency is obtained. The optimization results using
constant-envelope outperforms the conventional OFDM with the proper choice of the phase
modulation index. The LSTM showed as a powerful tool for routing forecasting and assessing
the optimized parameters. The CNN-DE equalizer (regression) was capable of detecting the
correct symbol for lower SNR ( 10 dB). Additionally, the classification version of the CNNDE
was able to predict and classify the mapped symbols similarly to the least-square-based
equalization.
