1,236 research outputs found
LEARN Codes: Inventing Low-latency Codes via Recurrent Neural Networks
Designing channel codes under low-latency constraints is one of the most
demanding requirements in 5G standards. However, a sharp characterization of
the performance of traditional codes is available only in the large
block-length limit. Guided by such asymptotic analysis, code designs require
large block lengths as well as latency to achieve the desired error rate.
Tail-biting convolutional codes and other recent state-of-the-art short block
codes, while promising reduced latency, are neither robust to channel-mismatch
nor adaptive to varying channel conditions. When the codes designed for one
channel (e.g.,~Additive White Gaussian Noise (AWGN) channel) are used for
another (e.g.,~non-AWGN channels), heuristics are necessary to achieve
non-trivial performance.
In this paper, we first propose an end-to-end learned neural code, obtained
by jointly designing a Recurrent Neural Network (RNN) based encoder and
decoder. This code outperforms canonical convolutional code under block
settings. We then leverage this experience to propose a new class of codes
under low-latency constraints, which we call Low-latency Efficient Adaptive
Robust Neural (LEARN) codes. These codes outperform state-of-the-art
low-latency codes and exhibit robustness and adaptivity properties. LEARN codes
show the potential to design new versatile and universal codes for future
communications via tools of modern deep learning coupled with communication
engineering insights
Geometric Constellation Shaping for Fiber Optic Communication Systems via End-to-end Learning
In this paper, an unsupervised machine learning method for geometric
constellation shaping is investigated. By embedding a differentiable fiber
channel model within two neural networks, the learning algorithm is optimizing
for a geometric constellation shape. The learned constellations yield improved
performance to state-of-the-art geometrically shaped constellations, and
include an implicit trade-off between amplification noise and nonlinear
effects. Further, the method allows joint optimization of system parameters,
such as the optimal launch power, simultaneously with the constellation shape.
An experimental demonstration validates the findings. Improved performances are
reported, up to 0.13 bit/4D in simulation and experimentally up to 0.12 bit/4D
Lightweight Convolutional Neural Networks for CSI Feedback in Massive MIMO
In frequency division duplex mode of massive multiple-input multiple-output
systems, the downlink channel state information (CSI) must be sent to the base
station (BS) through a feedback link. However, transmitting CSI to the BS is
costly due to the bandwidth limitation of the feedback link. Deep learning (DL)
has recently achieved remarkable success in CSI feedback. Realizing
high-performance and low-complexity CSI feedback is a challenge in DL based
communication. We develop a DL based CSI feedback network in this study to
complete the feedback of CSI effectively. However, this network cannot be
effectively applied to the mobile terminal because of the excessive numbers of
parameters. Therefore, we further propose a new lightweight CSI feedback
network based on the developed network. Simulation results show that the
proposed CSI network exhibits better reconstruction performance than that of
other CsiNet-related works. Moreover, the lightweight network maintains a few
parameters and parameter complexity while ensuring satisfactory reconstruction
performance. These findings suggest the feasibility and potential of the
proposed techniques.Comment: 5 pages, 2 figures, 2 table
Joint Transceiver Optimization for Wireless Communication PHY with Convolutional Neural Network
Deep Learning has a wide application in the area of natural language
processing and image processing due to its strong ability of generalization. In
this paper, we propose a novel neural network structure for jointly optimizing
the transmitter and receiver in communication physical layer under fading
channels. We build up a convolutional autoencoder to simultaneously conduct the
role of modulation, equalization and demodulation. The proposed system is able
to design different mapping scheme from input bit sequences of arbitrary length
to constellation symbols according to different channel environments. The
simulation results show that the performance of neural network based system is
superior to traditional modulation and equalization methods in terms of time
complexity and bit error rate (BER) under fading channels. The proposed system
can also be combined with other coding techniques to further improve the
performance. Furthermore, the proposed system network is more robust to channel
variation than traditional communication methods
Deep Learning for Wireless Communications
Existing communication systems exhibit inherent limitations in translating
theory to practice when handling the complexity of optimization for emerging
wireless applications with high degrees of freedom. Deep learning has a strong
potential to overcome this challenge via data-driven solutions and improve the
performance of wireless systems in utilizing limited spectrum resources. In
this chapter, we first describe how deep learning is used to design an
end-to-end communication system using autoencoders. This flexible design
effectively captures channel impairments and optimizes transmitter and receiver
operations jointly in single-antenna, multiple-antenna, and multiuser
communications. Next, we present the benefits of deep learning in spectrum
situation awareness ranging from channel modeling and estimation to signal
detection and classification tasks. Deep learning improves the performance when
the model-based methods fail. Finally, we discuss how deep learning applies to
wireless communication security. In this context, adversarial machine learning
provides novel means to launch and defend against wireless attacks. These
applications demonstrate the power of deep learning in providing novel means to
design, optimize, adapt, and secure wireless communications
Machine Learning for Wireless Communications in the Internet of Things: A Comprehensive Survey
The Internet of Things (IoT) is expected to require more effective and
efficient wireless communications than ever before. For this reason, techniques
such as spectrum sharing, dynamic spectrum access, extraction of signal
intelligence and optimized routing will soon become essential components of the
IoT wireless communication paradigm. Given that the majority of the IoT will be
composed of tiny, mobile, and energy-constrained devices, traditional
techniques based on a priori network optimization may not be suitable, since
(i) an accurate model of the environment may not be readily available in
practical scenarios; (ii) the computational requirements of traditional
optimization techniques may prove unbearable for IoT devices. To address the
above challenges, much research has been devoted to exploring the use of
machine learning to address problems in the IoT wireless communications domain.
This work provides a comprehensive survey of the state of the art in the
application of machine learning techniques to address key problems in IoT
wireless communications with an emphasis on its ad hoc networking aspect.
First, we present extensive background notions of machine learning techniques.
Then, by adopting a bottom-up approach, we examine existing work on machine
learning for the IoT at the physical, data-link and network layer of the
protocol stack. Thereafter, we discuss directions taken by the community
towards hardware implementation to ensure the feasibility of these techniques.
Additionally, before concluding, we also provide a brief discussion of the
application of machine learning in IoT beyond wireless communication. Finally,
each of these discussions is accompanied by a detailed analysis of the related
open problems and challenges.Comment: Ad Hoc Networks Journa
Blind Channel Equalization using Variational Autoencoders
A new maximum likelihood estimation approach for blind channel equalization,
using variational autoencoders (VAEs), is introduced. Significant and
consistent improvements in the error rate of the reconstructed symbols,
compared to constant modulus equalizers, are demonstrated. In fact, for the
channels that were examined, the performance of the new VAE blind channel
equalizer was close to the performance of a nonblind adaptive linear minimum
mean square error equalizer. The new equalization method enables a
significantly lower latency channel acquisition compared to the constant
modulus algorithm (CMA). The VAE uses a convolutional neural network with two
layers and a very small number of free parameters. Although the computational
complexity of the new equalizer is higher compared to CMA, it is still
reasonable, and the number of free parameters to estimate is small.Comment: Accepted to ICC workshop, Promises and Challenges of Machine Learning
in Communication Networks (ML4COM), 201
Deep Learning Based MIMO Communications
We introduce a novel physical layer scheme for single user Multiple-Input
Multiple-Output (MIMO) communications based on unsupervised deep learning using
an autoencoder. This method extends prior work on the joint optimization of
physical layer representation and encoding and decoding processes as a single
end-to-end task by expanding transmitter and receivers to the multi-antenna
case. We introduce a widely used domain appropriate wireless channel impairment
model (Rayleigh fading channel), into the autoencoder optimization problem in
order to directly learn a system which optimizes for it. We considered both
spatial diversity and spatial multiplexing techniques in our implementation.
Our deep learning-based approach demonstrates significant potential for
learning schemes which approach and exceed the performance of the methods which
are widely used in existing wireless MIMO systems. We discuss how the proposed
scheme can be easily adapted for open-loop and closed-loop operation in spatial
diversity and multiplexing modes and extended use with only compact binary
channel state information (CSI) as feedback.Comment: under journal submissio
Deep Learning-Based Communication Over the Air
End-to-end learning of communications systems is a fascinating novel concept
that has so far only been validated by simulations for block-based
transmissions. It allows learning of transmitter and receiver implementations
as deep neural networks (NNs) that are optimized for an arbitrary
differentiable end-to-end performance metric, e.g., block error rate (BLER). In
this paper, we demonstrate that over-the-air transmissions are possible: We
build, train, and run a complete communications system solely composed of NNs
using unsynchronized off-the-shelf software-defined radios (SDRs) and
open-source deep learning (DL) software libraries. We extend the existing ideas
towards continuous data transmission which eases their current restriction to
short block lengths but also entails the issue of receiver synchronization. We
overcome this problem by introducing a frame synchronization module based on
another NN. A comparison of the BLER performance of the "learned" system with
that of a practical baseline shows competitive performance close to 1 dB, even
without extensive hyperparameter tuning. We identify several practical
challenges of training such a system over actual channels, in particular the
missing channel gradient, and propose a two-step learning procedure based on
the idea of transfer learning that circumvents this issue
One-Bit OFDM Receivers via Deep Learning
This paper develops novel deep learning-based architectures and design
methodologies for an orthogonal frequency division multiplexing (OFDM) receiver
under the constraint of one-bit complex quantization. Single bit quantization
greatly reduces complexity and power consumption, but makes accurate channel
estimation and data detection difficult. This is particularly true for
multicarrier waveforms, which have high peak-to-average ratio in the time
domain and fragile subcarrier orthogonality in the frequency domain. The severe
distortion for one-bit quantization typically results in an error floor even at
moderately low signal-to-noise-ratio (SNR) such as 5 dB. For channel estimation
(using pilots), we design a novel generative supervised deep neural network
(DNN) that can be trained with a reasonable number of pilots. After channel
estimation, a neural network-based receiver -- specifically, an autoencoder --
jointly learns a precoder and decoder for data symbol detection. Since
quantization prevents end-to-end training, we propose a two-step sequential
training policy for this model. With synthetic data, our deep learning-based
channel estimation can outperform least squares (LS) channel estimation for
unquantized (full-resolution) OFDM at average SNRs up to 14 dB. For data
detection, our proposed design achieves lower bit error rate (BER) in fading
than unquantized OFDM at average SNRs up to 10 dB
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