7 research outputs found
Deep Energy Autoencoder for Noncoherent Multicarrier MU-SIMO Systems
We propose a novel deep energy autoencoder (EA) for noncoherent multicarrier
multiuser single-input multipleoutput (MU-SIMO) systems under fading channels.
In particular, a single-user noncoherent EA-based (NC-EA) system, based on the
multicarrier SIMO framework, is first proposed, where both the transmitter and
receiver are represented by deep neural networks (DNNs), known as the encoder
and decoder of an EA. Unlike existing systems, the decoder of the NC-EA is fed
only with the energy combined from all receive antennas, while its encoder
outputs a real-valued vector whose elements stand for the subcarrier power
levels. Using the NC-EA, we then develop two novel DNN structures for both
uplink and downlink NC-EA multiple access (NC-EAMA) schemes, based on the
multicarrier MUSIMO framework. Note that NC-EAMA allows multiple users to share
the same sub-carriers, thus enables to achieve higher performance gains than
noncoherent orthogonal counterparts. By properly training, the proposed NC-EA
and NC-EAMA can efficiently recover the transmitted data without any channel
state information estimation. Simulation results clearly show the superiority
of our schemes in terms of reliability, flexibility and complexity over
baseline schemes.Comment: Accepted, IEEE TW
Deep Learning-Based Signal Detection for Dual-Mode Index Modulation 3D-OFDM
In this paper, we propose a deep learning-based signal detector called
DuaIM-3DNet for dual-mode index modulation-based three-dimensional (3D)
orthogonal frequency division multiplexing (DM-IM-3D-OFDM). Herein, DM-IM-3D-
OFDM is a subcarrier index modulation scheme which conveys data bits via both
dual-mode 3D constellation symbols and indices of active subcarriers. Thus,
this scheme obtains better error performance than the existing IM schemes when
using the conventional maximum likelihood (ML) detector, which, however,
suffers from high computational complexity, especially when the system
parameters increase. In order to address this fundamental issue, we propose the
usage of a deep neural network (DNN) at the receiver to jointly and reliably
detect both symbols and index bits of DM-IM-3D-OFDM under Rayleigh fading
channels in a data-driven manner. Simulation results demonstrate that our
proposed DNN detector achieves near-optimal performance at significantly lower
runtime complexity compared to the ML detector
Deep Neural Network-Based Detector for Single-Carrier Index Modulation NOMA
In this paper, a deep neural network (DNN)-based detector for an uplink
single-carrier index modulation nonorthogonal multiple access (SC-IM-NOMA)
system is proposed, where SC-IM-NOMA allows users to use the same set of
subcarriers for transmitting their data modulated by the sub-carrier index
modulation technique. More particularly, users of SC-IMNOMA simultaneously
transmit their SC-IM data at different power levels which are then exploited by
their receivers to perform successive interference cancellation (SIC)
multi-user detection. The existing detectors designed for SC-IM-NOMA, such as
the joint maximum-likelihood (JML) detector and the maximum likelihood
SIC-based (ML-SIC) detector, suffer from high computational complexity. To
address this issue, we propose a DNN-based detector whose structure relies on
the model-based SIC for jointly detecting both M-ary symbols and index bits of
all users after trained with sufficient simulated data. The simulation results
demonstrate that the proposed DNN-based detector attains near-optimal error
performance and significantly reduced runtime complexity in comparison with the
existing hand-crafted detectors
Transformer-Based Deep Learning Detector for Dual-Mode Index Modulation 3D-OFDM
In this paper, we propose a deep learning-based signal detector called
TransD3D-IM, which employs the Transformer framework for signal detection in
the Dual-mode index modulation-aided three-dimensional (3D) orthogonal
frequency division multiplexing (DM-IM-3D-OFDM) system. In this system, the
data bits are conveyed using dual-mode 3D constellation symbols and active
subcarrier indices. As a result, this method exhibits significantly higher
transmission reliability than current IM-based models with traditional maximum
likelihood (ML) detection. Nevertheless, the ML detector suffers from high
computational complexity, particularly when the parameters of the system are
large. Even the complexity of the Log-Likelihood Ratio algorithm, known as a
low-complexity detector for signal detection in the DM-IM-3D-OFDM system, is
also not impressive enough. To overcome this limitation, our proposal applies a
deep neural network at the receiver, utilizing the Transformer framework for
signal detection of DM-IM-3D-OFDM system in Rayleigh fading channel. Simulation
results demonstrate that our detector attains to approach performance compared
to the model-based receiver. Furthermore, TransD3D-IM exhibits more robustness
than the existing deep learning-based detector while considerably reducing
runtime complexity in comparison with the benchmarks
Non-Coherent Massive MIMO-OFDM Down-Link Based on Differential Modulation
Orthogonal frequency division multiplexing (OFDM) and multiple-input multiple-output (MIMO) are wireless radio technologies adopted by the new Fifth Generation (5G) of mobile communications. A very large number of antennas (massive MIMO) is used to perform the beam-forming of the transmitted signal, either to reduce the multi-user interference (MUI), when spatially multiplexing several users, or to compensate the path-loss when higher frequencies than microwave are used, such as the millimeter-waves (mm-Waves). Usually, a coherent demodulation scheme (CDS) is used in order to exploit MIMO-OFDM, where the channel estimation and the pre/post-equalization processes are complex and time consuming operations, which require a considerable pilot overhead and also increase the latency of the system. As an alternative, non-coherent techniques based on a differential modulation scheme have been proposed for the up-link (UL). However, it is not straightforward to extend these proposals to the down-link (DL) due to the (usually) reduced number of antennas at the receiver side. In this paper we overcome this problem, and assuming that each user equipment (UE) is only equipped with one single antenna, we propose the combination of beam-forming with a differential modulation scheme for the DL, enhanced by the frequency diversity. The new transmission and reception schemes are described, and the signal-to-interference-plus-noise ratio (SINR) and the complexity are analysed. The numerical results verify the accuracy of the analysis and show that our proposal outperforms the existing CDS with a significant lower complexity.This work was supported by project TERESA-ADA (TEC2017-90093-C3-2-R) (MINECO/AEI/FEDER, UE)