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

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

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

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

Deep Learning-Based Detector for OFDM-IM

This letter presents the first attempt of exploiting deep learning (DL) in the signal detection of orthogonal frequency division multiplexing with index modulation (OFDM-IM) systems. Particularly, we propose a novel DL-based detector termed as DeepIM, which employs a deep neural network with fully connected layers to recover data bits in an OFDM-IM system. To enhance the performance of DeepIM, the received signal and channel vectors are pre-processed based on the domain knowledge before entering the network. Using datasets collected by simulations, DeepIM is first trained offline to minimize the bit error rate (BER) and then the trained model is deployed for the online signal detection of OFDM-IM. Simulation results show that DeepIM can achieve a near-optimal BER with a lower runtime than existing hand-crafted detectors

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