11 research outputs found
Review of Recent Trends
This work was partially supported by the European Regional Development Fund (FEDER), through the Regional Operational Programme of Centre (CENTRO 2020) of the Portugal 2020 framework, through projects SOCA (CENTRO-01-0145-FEDER-000010) and ORCIP (CENTRO-01-0145-FEDER-022141). Fernando P. Guiomar acknowledges a fellowship from “la Caixa” Foundation (ID100010434), code LCF/BQ/PR20/11770015. Houda Harkat acknowledges the financial support of the Programmatic Financing of the CTS R&D Unit (UIDP/00066/2020).MIMO-OFDM is a key technology and a strong candidate for 5G telecommunication systems. In the literature, there is no convenient survey study that rounds up all the necessary points to be investigated concerning such systems. The current deeper review paper inspects and interprets the state of the art and addresses several research axes related to MIMO-OFDM systems. Two topics have received special attention: MIMO waveforms and MIMO-OFDM channel estimation. The existing MIMO hardware and software innovations, in addition to the MIMO-OFDM equalization techniques, are discussed concisely. In the literature, only a few authors have discussed the MIMO channel estimation and modeling problems for a variety of MIMO systems. However, to the best of our knowledge, there has been until now no review paper specifically discussing the recent works concerning channel estimation and the equalization process for MIMO-OFDM systems. Hence, the current work focuses on analyzing the recently used algorithms in the field, which could be a rich reference for researchers. Moreover, some research perspectives are identified.publishersversionpublishe
Learning-based communication system design – autoencoder for (differential) block coded modulation designs and path loss predictions
Shannon’s channel coding theorem states the existence of long random codes that can
make the error probability arbitrarily small. Recently, advanced error-correcting codes
such as turbo and low-density parity-check codes have almost reached the theoretical
Shannon limit for binary additive white Gaussian noise channels. However, designing
optimal high-rate short-block codes with automatic bit-labeling for various wireless networks is still an unsolved problem.
Deep-learning-based autoencoders (AE) have appeared as a potential near-optimal
solution for designing wireless communications systems. We take a holistic approach that
jointly optimizes all the components of the communication networks by performing data-driven end-to-end learning of the neural network-based transmitter and receiver together.
Specifically, to tackle the fading channels, we show that AE frameworks can perform
near-optimal block coded-modulation (BCM) and differential BCM (d-BCM) designs in
the presence and absence of the channel state information knowledge. Moreover, we
focus on AE-based designing of high-rate short block codes with automatic bit-labeling
that are capable of outperforming conventional networks with larger margins as the rate
R increases. We also investigate the BCM and d-BCM from an information-theoretic
perspective.
With the advent of internet-of-things (IoT) networks and the widespread use of small
devices, we face the challenge of limited available bandwidth. Therefore, novel techniques need to be utilized, such as full-duplex (FD) mode transmission reception at the
base station for the full utilization of the spectrum, and non-orthogonal multiple access
(NOMA) at the user-end for serving multiple IoT devices while fulfilling their quality-of-service requirement. Furthermore, the deployment of relay nodes will play a pivotal
role in improving network coverage, reliability, and spectral efficiency for the future 5G
networks. Thus, we design and develop novel end-to-end-learning-based AE frameworks
for BCM and d-BCM in various scenarios such as amplify-and-forward and decode-and-forward relaying networks, FD relaying networks, and multi-user downlink networks.
We focus on interpretability and understand the AE-based BCM and d-BCM from an
information-theoretic perspective, such as the AE’s estimated mutual information, convergence, loss optimization, and training principles. We also determine the distinct properties of AE-based (differential) coded-modulation designs in higher-dimensional space.
Moreover, we also studied the reproducibility of the trained AE framework.
In contrast, large bandwidth and worldwide spectrum availability at mm-wave bands
have also shown a great potential for 5G and beyond, but the high path loss (PL) and
significant scattering/absorption loss make the signal propagation challenging. Highly
accurate PL prediction is fundamental for mm-wave network planning and optimization,
whereas existing methods such as slope-intercept models and ray tracing fall short in
capturing the large street-by-street variation seen in urban cities. We also exploited the
potential benefits of AE framework-based compression capabilities in mm-wave PL prediction. Specifically, we employ extensive 28 GHz measurements from Manhattan Street
canyons and model the street clutters via a LiDAR point cloud dataset and 3D-buildings
by a mesh-grid building dataset. We aggressively compress 3D-building shape information using convolutional-AE frameworks to reduce overfitting and propose a machine
learning (ML)-based PL prediction model for mm-wave propagation.EPSRC-UKRI fundin