2 research outputs found
An End-to-End Block Autoencoder For Physical Layer Based On Neural Networks
Deep Learning has been widely applied in the area of image processing and
natural language processing. In this paper, we propose an end-to-end
communication structure based on autoencoder where the transceiver can be
optimized jointly. A neural network roles as a combination of channel encoder
and modulator. In order to deal with input sequences parallelly, we introduce
block scheme, which means that the autoencoder divides the input sequence into
a series of blocks. Each block contains fixed number of bits for encoding and
modulating operation. Through training, the proposed system is able to produce
the modulated constellation diagram of each block. The simulation results show
that our autoencoder performs better than other autoencoder-based systems under
additive Gaussian white noise (AWGN) and fading channels. We also prove that
the bit error rate (BER) of proposed system can achieve an acceptable range
with increasing the number of symbols.Comment: 4 pages, 15 figures, submitted to IEEE Wireless Communications
Letter
Blind interactive learning of modulation schemes: Multi-agent cooperation without co-design
We examine the problem of learning to cooperate in the context of wireless
communication. In our setting, two agents must learn modulation schemes that
enable them to communicate across a power-constrained additive white Gaussian
noise channel. We investigate whether learning is possible under different
levels of information sharing between distributed agents which are not
necessarily co-designed. We employ the "Echo" protocol, a "blind" interactive
learning protocol where an agent hears, understands, and repeats (echoes) back
the message received from another agent, simultaneously training itself to
communicate. To capture the idea of cooperation between "not necessarily
co-designed" agents we use two different populations of function approximators
- neural networks and polynomials. We also include interactions between
learning agents and non-learning agents with fixed modulation protocols such as
QPSK and 16QAM. We verify the universality of the Echo learning approach,
showing it succeeds independent of the inner workings of the agents. In
addition to matching the communication expectations of others, we show that two
learning agents can collaboratively invent a successful communication approach
from independent random initializations. We complement our simulations with an
implementation of the Echo protocol in software-defined radios. To explore the
continuum of co-design, we study how learning is impacted by different levels
of information sharing between agents, including sharing training symbols,
losses, and full gradients. We find that co-design (increased information
sharing) accelerates learning. Learning higher order modulation schemes is a
more difficult task, and the beneficial effect of co-design becomes more
pronounced as the task becomes harder.Comment: 33 pages, 25 figures, code can be found at
https://github.com/ml4wireless/echo, accepted for publication in IEEE Acces