Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: RCNet: Incorporating Structural Information into Deep RNN for MIMO-OFDM
  Symbol Detection with Limited Training   In this paper, we investigate learning-based MIMO-OFDM symbol detection
strategies focusing on a special recurrent neural network (RNN) -- reservoir
computing (RC). We first introduce the Time-Frequency RC to take advantage of
the structural information inherent in OFDM signals. Using the time domain RC
and the time-frequency RC as the building blocks, we provide two extensions of
the shallow RC to RCNet: 1) Stacking multiple time domain RCs; 2) Stacking
multiple time-frequency RCs into a deep structure. The combination of RNN
dynamics, the time-frequency structure of MIMO-OFDM signals, and the deep
network enables RCNet to handle the interference and nonlinear distortion of
MIMO-OFDM signals to outperform existing methods. Unlike most existing NN-based
detection strategies, RCNet is also shown to provide a good generalization
performance even with a limited training set (i.e, similar amount of reference
signals/training as standard model-based approaches). Numerical experiments
demonstrate that the introduced RCNet can offer a faster learning convergence
and as much as 20% gain in bit error rate over a shallow RC structure by
compensating for the nonlinear distortion of the MIMO-OFDM signal, such as due
to power amplifier compression in the transmitter or due to finite quantization
resolution in the receiver.

8
The categories of this paper are: hep-ex
30
The categories of this paper are: q-alg
2
The categories of this paper are: math
28
The categories of this paper are: alg-geom
18
The categories of this paper are: econ
9
The categories of this paper are: nlin
4
The categories of this paper are: cond-mat
5
The categories of this paper are: gr-qc
17
The categories of this paper are: q-fin
37
The categories of this paper are: supr-con
22
The categories of this paper are: chao-dyn
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The categories of this paper are: cmp-lg
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The categories of this paper are: mtrl-th
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The categories of this paper are: astro-ph
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The categories of this paper are: bayes-an
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The categories of this paper are: nucl-th