The BER Analysis of MRC-aided Greedy Detection for OFDM-IM in Presence of Uncertain CSI

This letter investigates the bit error rate (BER) performance of orthogonal frequency division multiplexing index modulation, employing the maximal ratio combining-based low-complexity greedy detector (MRC-GD) and the PSK modulation. For performance analysis, we derive tight expressions for both index error probability (IEP) and BER, taking into account channel state information (CSI) uncertainty. This provides insight into various impacts of CSI uncertainty on the diversity gain and error floor of the IEP and the BER, respectively. We clearly show that under imperfect CSI, the MRC-aided GD can perform like the MRC-maximum likelihood detector, at lower complexity. Finally, simulation results are presented to verify the accuracy of derived expressions and the theoretical analysis

Spread OFDM-IM with precoding matrix and low-complexity detection designs

We propose a new spread orthogonal frequency division multiplexing with index modulation (S-OFDM-IM), which employs precoding matrices such as Walsh-Hadamard (WH) and Zadoff-Chu (ZC) to spread both non-zero data symbols of active sub-carriers and their indices, and then compress them into all available sub-carriers. This aims to increase the transmit diversity, exploiting both multipath and index diversities. As for the performance analysis, we derive the bit error probability (BEP) to provide an insight into the diversity and coding gains, and especially impacts of selecting various spreading matrices on these gains. This interestingly reveals an opportunity of using rotated versions of original WH and ZC matrices to further improve the BEP performance. More specifically, rotated matrices can enable S-OFDM-IM to harvest the maximum diversity gain, which is the number of sub-carriers, while benchmark schemes have diversity gains limited by two. Moreover, we propose three low-complexity detectors, namely minimum mean square error log-likelihood ratio, index pattern MMSE (IP-MMSE), and enhanced IP-MMSE, which achieve different levels of complexity and reliability. Simulation results are presented to prove the superiority of S-OFDM-IM over the benchmarks

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

After-Fatigue Condition: A Novel Analysis Based on Surface EMG Signals

This study introduces a novel muscle activation analysis based on surface electromyography (sEMG) signals to assess the muscle's after-fatigue condition. Previous studies have mainly focused on the before-fatigue and fatigue conditions. However, a comprehensive analysis of the after-fatigue condition has been overlooked. The proposed method analyzes muscle fatigue indicators at various maximal voluntary contraction (MVC) levels to compare the before-fatigue, fatigue, and after-fatigue conditions using amplitude-based, spectral-based, and muscle fiber conduction velocity (CV) parameters. In addition, the contraction time of each MVC level is also analyzed with the same indicators. The results show that in the after-fatigue condition, the muscle activation changes significantly in the ways such as higher CV, power spectral density shifting to the right, and longer contraction time until exhaustion compared to the before-fatigue and fatigue conditions. The results can provide a comprehensive and objective evaluation of muscle fatigue and recovery, which can be helpful in clinical diagnosis, rehabilitation, and sports performance

Repeated MCIK-OFDM with Enhanced Transmit Diversity under CSI Uncertainty

This paper investigates the opportunity for a repetition coded multi-carrier index keying-orthogonal frequency division multiplexing (MCIK-OFDM), termed repeated MCIK-OFDM (ReMO), which can provide superior performance over existing schemes at the same spectral efficiency. Unlike the classical scheme, the proposed scheme activates a subset of subcarriers and modulates them with the same M-ary data symbol, while additional information is conveyed by the active sub-carrier indices. This approach not only provides the frequency diversity gains in the M-ary symbol detection but also improves the index detection, leading to considerable improvement in the transmit diversity. For performance analysis, we derive tight closed-form expressions for the symbol error probability and the bit error rate, under both perfect and imperfect channel state information (CSI). These expressions provide insight into the achievable performance gains, system designs, and impacts of various CSI conditions. Finally, simulation results are given to illustrate the superior performance achieved by our scheme over existing schemes under different CSI uncertainties

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

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 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 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