8 research outputs found
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
Cooperative OFDM-IM relays network with partial relay selection under imperfect CSI
In this paper, we investigate the performance of cooperative orthogonal frequency division multiplexing with index modulation (OFDM-IM) with the low complexity greedy detection (GD). In particular, we propose a novel partial relay selection scheme whose search criteria are designed to exploit the IM subcarriers. To provide low-complexity receiver, we further examine the energy-sensing based GD design for the cooperative OFDM-IM. For the performance analysis, we derive novel upper bound and approximate closed form solutions for both the average index error probability and the average symbol error probability over Nakagami-m fading channels with imperfect channel state information (CSI) at the relays and destination. Unlike the information theoretical works, in presence of positive detection error in the relays, the derived expressions provide a useful insight into the error performance of cooperative OFDM-IM under various fading conditions. The numerical and simulation results clearly present that the proposed scheme harmonizing partially selected relays and their IM subcarriers with GD can outperform the benchmark schemes, under uncertain CSI, at reduced complexity
Impact of CSI Uncertainty on the MCIK-OFDM Performance: Tight, Closed-Form Symbol Error Probability Analysis
This paper proposes a novel framework to analyze the symbol error probability (SEP) for multicarrier index keying orthogonal frequency-division multiplexing (MCIK-OFDM) systems. Considering two different types of detections such as the maximum likelihood (ML) and low-complexity greedy detectors (GD), we derive tight closed-form expressions for the average SEPs of MCIK-OFDM in the presence of channel state information (CSI) uncertainty. We undertake an asymptotic performance analysis with respect to three CSI conditions, which ensures to provide a comprehensive insight into the achievable diversity and coding gains as well as the impact of various CSI uncertainties on the SEP performance. The SEP performance comparison between the ML and GD is obtained under different CSI uncertainties. This interestingly reveals that the GD can achieve nearly optimal error performance as the M-ary modulation size is large or even outperforms the ML under certain CSI conditions. Finally, the theoretical and asymptotic analysis are verified via simulation results, obtaining the high accuracy of the derived SEP
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