ViterbiNet: A deep learning based Viterbi algorithm for symbol detection
Symbol detection plays an important role in the implementation of digital receivers. In this
work, we propose ViterbiNet, which is a data-driven symbol detector that does not require …
work, we propose ViterbiNet, which is a data-driven symbol detector that does not require …
ViterbiNet: Symbol detection using a deep learning based Viterbi algorithm
Symbol detection plays an important role in the implementation of digital receivers. One of
the most common symbol detection schemes is the Viterbi algorithm, which is capable of …
the most common symbol detection schemes is the Viterbi algorithm, which is capable of …
Generative-adversarial-network enabled signal detection for communication systems with unknown channel models
L Sun, Y Wang, AL Swindlehurst… - IEEE Journal on …, 2020 - ieeexplore.ieee.org
The Viterbi algorithm is widely adopted in digital communication systems because of its
capability of realizing maximum-likelihood signal sequence detection. However …
capability of realizing maximum-likelihood signal sequence detection. However …
Data-driven factor graphs for deep symbol detection
Many important schemes in signal processing and communications, ranging from the BCJR
algorithm to the Kalman filter, are instances of factor graph methods. This family of …
algorithm to the Kalman filter, are instances of factor graph methods. This family of …
Data-driven symbol detection via model-based machine learning
N Farsad, N Shlezinger, AJ Goldsmith… - 2021 IEEE Statistical …, 2021 - ieeexplore.ieee.org
We present a data-driven framework to symbol detection design that combines machine
learning (ML) and model-based algorithms. The resulting data-driven receivers are most …
learning (ML) and model-based algorithms. The resulting data-driven receivers are most …
Tiny machine learning (tiny-ml) for efficient channel estimation and signal detection
Machine learning has provided great potential in intelligent signal processing, eg, channel
estimation and signal detection. But still, it is difficult to deploy deep neural network (DNN) …
estimation and signal detection. But still, it is difficult to deploy deep neural network (DNN) …
DeepRx: Fully convolutional deep learning receiver
Deep learning has solved many problems that are out of reach of heuristic algorithms. It has
also been successfully applied in wireless communications, even though the current radio …
also been successfully applied in wireless communications, even though the current radio …
Deep learning-aided tabu search detection for large MIMO systems
In this study, we consider the application of deep learning (DL) to tabu search (TS) detection
in large multiple-input multiple-output (MIMO) systems. First, we propose a deep neural …
in large multiple-input multiple-output (MIMO) systems. First, we propose a deep neural …
Deep hypernetwork-based MIMO detection
Optimal symbol detection for multiple-input multiple-output (MIMO) systems is known to be
an NP-hard problem. Conventional heuristic algorithms are either too complex to be …
an NP-hard problem. Conventional heuristic algorithms are either too complex to be …
Adaptive neural signal detection for massive MIMO
Traditional symbol detection algorithms either perform poorly or are impractical to implement
for Massive Multiple-Input Multiple-Output (MIMO) systems. Recently, several learning …
for Massive Multiple-Input Multiple-Output (MIMO) systems. Recently, several learning …