148 research outputs found
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
Physical Layer Communications System Design Over-the-Air Using Adversarial Networks
This paper presents a novel method for synthesizing new physical layer
modulation and coding schemes for communications systems using a learning-based
approach which does not require an analytic model of the impairments in the
channel. It extends prior work published on the channel autoencoder to consider
the case where the channel response is not known or can not be easily modeled
in a closed form analytic expression. By adopting an adversarial approach for
channel response approximation and information encoding, we can jointly learn a
good solution to both tasks over a wide range of channel environments. We
describe the operation of the proposed adversarial system, share results for
its training and validation over-the-air, and discuss implications and future
work in the area
A Generalized Data Representation and Training-Performance Analysis for Deep Learning-Based Communications Systems
Deep learning (DL)-based autoencoder is a potential architecture to implement
end-to-end communication systems. In this letter, we first give a brief
introduction to the autoencoder-represented communication system. Then, we
propose a novel generalized data representation (GDR) aiming to improve the
data rate of DL-based communication systems. Finally, simulation results show
that the proposed GDR scheme has lower training complexity, comparable block
error rate performance and higher channel capacity than the conventional
one-hot vector scheme. Furthermore, we investigate the effect of
signal-to-noise ratio (SNR) in DL-based communication systems and prove that
training at a high SNR could produce a good training performance for
autoencoder
Channel Agnostic End-to-End Learning based Communication Systems with Conditional GAN
In this article, we use deep neural networks (DNNs) to develop a wireless
end-to-end communication system, in which DNNs are employed for all
signal-related functionalities, such as encoding, decoding, modulation, and
equalization. However, accurate instantaneous channel transfer function,
\emph{i.e.}, the channel state information (CSI), is necessary to compute the
gradient of the DNN representing. In many communication systems, the channel
transfer function is hard to obtain in advance and varies with time and
location. In this article, this constraint is released by developing a channel
agnostic end-to-end system that does not rely on any prior information about
the channel. We use a conditional generative adversarial net (GAN) to represent
the channel effects, where the encoded signal of the transmitter will serve as
the conditioning information. In addition, in order to deal with the
time-varying channel, the received signal corresponding to the pilot data can
also be added as a part of the conditioning information. From the simulation
results, the proposed method is effective on additive white Gaussian noise
(AWGN) and Rayleigh fading channels, which opens a new door for building
data-driven communication systems
Beamforming Design for Large-Scale Antenna Arrays Using Deep Learning
Beamforming (BF) design for large-scale antenna arrays with limited radio
frequency chains and the phase-shifter-based analog BF architecture, has been
recognized as a key issue in millimeter wave communication systems. It becomes
more challenging with imperfect channel state information (CSI). In this
letter, we propose a deep learning based BF design approach and develop a BF
neural network (BFNN) which can be trained to learn how to optimize the
beamformer for maximizing the spectral efficiency with hardware limitation and
imperfect CSI. Simulation results show that the proposed BFNN achieves
significant performance improvement and strong robustness to imperfect CSI over
the conventional BF algorithms.Comment: The codes are available in
https://github.com/TianLin0509/BF-design-with-D
Decentralized Deep Scheduling for Interference Channels
In this paper, we study the problem of decentralized scheduling in
Interference Channels (IC). In this setting, each Transmitter (TX) receives an
arbitrary amount of feedback regarding the global multi-user channel state
based on which it decides whether to transmit or to stay silent without any
form of communication with the other TXs. While many methods have been proposed
to tackle the problem of link scheduling in the presence of reliable Channel
State Information (CSI), finding the optimally robust transmission strategy in
the presence of arbitrary channel uncertainties at each TX has remained elusive
for the past years. In this work, we recast the link scheduling problem as a
decentralized classification problem and we propose the use of Collaborative
Deep Neural Networks (C-DNNs) to solve this problem. After adequate training,
the scheduling obtained using the C-DNNs flexibly adapts to the decentralized
CSI configuration to outperform other scheduling algorithms.Comment: Submitted to the 2018 IEEE International Conference on Communications
(ICC
Deep Learning-Based Channel Estimation for High-Dimensional Signals
We propose a novel deep learning-based channel estimation technique for
high-dimensional communication signals that does not require any training. Our
method is broadly applicable to channel estimation for multicarrier signals
with any number of antennas, and has low enough complexity to be used in a
mobile station. The proposed deep channel estimator can outperform LS
estimation with nearly the same complexity, and approach MMSE estimation
performance to within 1 dB without knowing the second order statistics. The
only complexity increase with respect to LS estimator lies in fitting the
parameters of a deep neural network (DNN) periodically on the order of the
channel coherence time. We empirically show that the main benefit of this
method accrues from the ability of this specially designed DNN to exploit
correlations in the time-frequency grid. The proposed estimator can also reduce
the number of pilot tones needed in an OFDM time-frequency grid, e.g. in an LTE
scenario by 98% (68%) when the channel coherence time interval is 73ms (4.5ms)
A Deep Learning Wireless Transceiver with Fully Learned Modulation and Synchronization
In this paper, we present a deep learning based wireless transceiver. We
describe in detail the corresponding artificial neural network architecture,
the training process, and report on excessive over-the-air measurement results.
We employ the end-to-end training approach with an autoencoder model that
includes a channel model in the middle layers as previously proposed in the
literature. In contrast to other state-of-the-art results, our architecture
supports learning time synchronization without any manually designed signal
processing operations. Moreover, the neural transceiver has been tested over
the air with an implementation in software defined radio. Our experimental
results for the implemented single antenna system demonstrate a raw bit-rate of
0.5 million bits per second. This exceeds results from comparable systems
presented in the literature and suggests the feasibility of high throughput
deep learning transceivers.Comment: Presented at ICC 2019- 2nd Workshop on Machine Learning in Wireless
Communications (ML4COM
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 the Gaussian Wiretap Channel
End-to-end learning of communication systems with neural networks and
particularly autoencoders is an emerging research direction which gained
popularity in the last year. In this approach, neural networks learn to
simultaneously optimize encoding and decoding functions to establish reliable
message transmission. In this paper, this line of thinking is extended to
communication scenarios in which an eavesdropper must further be kept ignorant
about the communication. The secrecy of the transmission is achieved by
utilizing a modified secure loss function based on cross-entropy which can be
implemented with state-of-the-art machine-learning libraries. This secure loss
function approach is applied in a Gaussian wiretap channel setup, for which it
is shown that the neural network learns a trade-off between reliable
communication and information secrecy by clustering learned constellations. As
a result, an eavesdropper with higher noise cannot distinguish between the
symbols anymore.Comment: 6 pages, 11 figure
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