17,329 research outputs found
Performance Evaluation of Channel Decoding With Deep Neural Networks
With the demand of high data rate and low latency in fifth generation (5G),
deep neural network decoder (NND) has become a promising candidate due to its
capability of one-shot decoding and parallel computing. In this paper, three
types of NND, i.e., multi-layer perceptron (MLP), convolution neural network
(CNN) and recurrent neural network (RNN), are proposed with the same parameter
magnitude. The performance of these deep neural networks are evaluated through
extensive simulation. Numerical results show that RNN has the best decoding
performance, yet at the price of the highest computational overhead. Moreover,
we find there exists a saturation length for each type of neural network, which
is caused by their restricted learning abilities.Comment: 6 pages, 11 figures, Latex; typos corrected; IEEE ICC 2018 to appea
Efficiency and power of minimally nonlinear irreversible heat engines with broken time-reversal symmetry
We study the minimally nonlinear irreversible heat engines in which the
time-reversal symmetry for the systems may b e broken. The expressions for the
power and the efficiency are derived, in which the effects of the nonlinear
terms due to dissipations are included. We show that, as within the linear
responses, the minimally nonlinear irreversible heat engines enable attainment
of Carnot efficiency at positive power. We also find that the Curzon-Ahlborn
limit imposed on the efficiency at maximum power can be overcomed if the
time-reversal symmetry is broken
- …