Signal Format Research Articles

Design of a Differential Chaotic Shift Keying Communication System Based on Noise Reduction with Orthogonal Double Bit Rate

In this paper, a differential chaotic shift keying communication system based on noise reduction with orthogonal double bit rate (NR-ODBR-DCSK) is proposed. The system incorporates Walsh orthogonalization at the transmitter side to orthogonalize the information signals so that two mutually orthogonal signals can be superimposed. At the receiving end, because the principle of orthogonal signals is used, it achieves the characteristic of double information transmission rate for information signal transmission while avoiding the problem of chaotic synchronization. In addition, the system employs a noise reduction transmission mechanism, which reduces the noise variance in the received signal, further reducing the BER of the system and thus improving the performance of the communication system. By analyzing the signal format of the system, the transmitter and receiver structures of the communication system are designed. Subsequently, theoretical analyses and simulations in an additive white Gaussian noise (AWGN) channel demonstrate the good performance of the system, including a low bit error rate (BER) and a good data-energy to bit-energy ratio (DBR). Finally, a simulation test of the NR-ODBR-DCSK system for a semi-physical communication system was carried out using two USRP devices to verify the experimental feasibility of the system. The simulation analysis results show that comparative analyses with conventional DCSK and SR-DCSK systems highlight the superior performance of the NR-ODBR-DCSK system.

Digitisation of electrocardiographic images enable artificial intelligence-based mortality prediction

Abstract Background Artificial intelligence-based electrocardiogram (AI-ECG) models have shown excellent performance in a vast number of diagnostic and prognostic tasks. One of such tasks, which is critical for patient risk stratification and clinical intervention, is the prediction of adverse events, including death. Although a plethora of existing AI-ECG models have shown their accuracy in mortality prediction, their development and utilization currently rely on digital ECG signals for training and application. Purpose Many hospitals around the world have a significant number of ECGs stored in image formats or printed as paper ECGs. To address this problem, we developed and tested an ECG image digitisation pipeline to extract digital traces from ECG images and compare the performance of AI-ECG models for mortality prediction, under both natively digital and digitised signal formats. Methods A secondary care dataset of over 1 million ECGs (1,163,401 ECGs; 189,539 patients) was used for this analysis, in which both a natively digital signal and a PDF image was available. The ECG digitisation pipeline comprised of coloured PDF image inputs (2200 x 1700), initially processed to extract the ECG traces and grid features. ECG signals were extracted from the traces after cropping/restoration of the images and scaled appropriately based on the detected grid (2.5-sec strips per lead, 0.5 mV per grid block). Subsequently, both digital and digitised signals were identically pre-processed in order to acquire the ECG data fed into the AI model. In particular, a previously successful convolutional neural network architecture with a discrete-time survival loss function was used for mortality prediction. The model was trained under multiple configurations, using either the digital or the digitised ECGs as training/testing datasets (Figure 1). ECGs were split by patient ID to ensure cross-subject generalization with a training/validation/testing split of 50%/10%/40% ratio, respectively. Finally, the models were evaluated by the c-index. Results A comparison of the digital and digitised ECGs on the respective 2.5-sec strips available in both formats revealed a mean absolute error of 0.014 (CI: 0.07 – 0.021) and a Pearson’s R of 0.98 (CI: 0.96 – 1.00) between the signals, demonstrating an accurate ECG digitisation (Figure 2 shows overlapping natively digital (black) and digitised (red) traces). The performance of the digitisation was also evident in the performance of the AI-ECG mortality prediction task, with the model achieving c-indices of 0.775 (CI: 0.774 – 0.776) and 0.772 (CI: 0.771 – 0.774) when trained and tested on digital and digitised signals, respectively. Conclusion We described the first digitisation-based AI-ECG model for mortality prediction. Our results demonstrate the potential for clinical settings that lack the digital ECG infrastructure, to develop and deploy AI models using ECG image formats.Figure 1Figure 2

Circular DNA enhanced amplification-free CRISPR/Cas12a assays for end-user friendly ultra-sensitive Porphyromonas gingivalis diagnosis

Periodontitis not only leads to tooth loss but is also associated with various systemic symptoms. Porphyromonas gingivalis (Pg) is the microorganism most closely linked to periodontitis. A rapid and accurate Pg detection method holds significant potential health benefits for patients. While several nucleic acid amplification-based diagnostic methods have been developed, there is a current lack of rapid diagnostic tools for Pg, particularly at chair-side level. Here, two detection methods for the Pg 16S gene based on the CRISPR/Cas12a system have been developed. To achieve ultra-high sensitivity in the developed CRISPR/Cas12a assays without pre-amplification, we designed a circular DNA molecule (termed as Cir-amplifier) which amplifies the signal output of the Cas12a reactions in a cascade response manner. The Cir-amplifier enhanced Fluorescence-Cas12a reaction achieved a minimum detectable limit of 0.39 CFU of Pg within 80-minute under room-temperature. In addition, the Cir-amplifier enhanced Lateral Flow Detection (LFD)-Cas12a reaction has demonstrated the ability to detect a minimum of 3.9 CFU of Pg in 90 min, with results observable by the naked eye. The potential of the two methods for Pg detection in clinical samples has been demonstrated, showing 100 % sensitivity compared to the traditional real-time PCR method. In summary, we have established two efficient detection methods for Pg that eliminate the need for pre-amplification, hence no amplicon contaminations, making them promising candidates for chair-side Pg diagnosis. Furthermore, the Cir-amplifier has been demonstrated to be adaptable to CRISPR/Cas systems producing various signal formats. The Cir-amplifier is expected to become a universal CRISPR/Cas enhancer.

Prediction of paroxysmal atrial fibrillation using a convolutional neural network and electrocardiogram signals

Atrial fibrillation (AF) is the most clinically diagnosed arrhythmia in cardiac pathology. The incidence of AF begins at a very early age and its initial state is paroxysmal atrial fibrillation (PAF). This type of heart disease can be detected and predicted by analyzing the spectrogram of a surface electrocardiogram (ECG) signal. In many studies, different ECG signal formats and convolutional neural network (CNN) architectures have been used. However, the lack of good signal preprocessing or signal adequacy may have affected the accuracy, especially on short-term ECG signals. In this study, we analyzed a preprocessed ECG signal, determined the optimal set to predict PAF, and evaluated the accuracy using ECG signals of different durations. The PAF Prediction Challenge–PhysioNet database was used to extract spectrograms in 30-sec and 5-sec windows for two classes (Normal, PAF) and 3 classes (Normal, Close-AF, Distant-AF). Then, the AlexNet architecture was used. The proposed method achieved a two-class accuracy of 99.92% with a 30-sec window and 99.42% with a 5-sec window, improving the PAF prediction performance compared with similar works. In addition, the three-class accuracies were 96.92% and 97.43% with windows of 30-sec, and 5-sec, respectively. These results prove the efficacy of the method for the early diagnosis of PAF, even based on short-term ECG signals.

Research on an Alpha Navigation Signal Detection Method Based on Multichannel Orthogonal Correlation

The Alpha navigation system is the only operating radio system based on very-low-frequency (VLF) signals that can be used to research VLF navigation, timing, and ionospheric characteristics. The detection of the Alpha navigation signal is the key step in the Alpha receiver; however, the received Alpha navigation signal is susceptible to noise and mutual interference, which deteriorates signal detection performance. This paper presents a multichannel orthogonal correlation method for Alpha navigation signal detection. Once the three frequency signals of the Alpha navigation system are obtained using a notch filter, station identification is realized using a multichannel orthogonal correlation method and signal format. The selection of key parameters and the detection performance under noise and mutual interference are analyzed. This method’s detection probability exceeds 90% when the signal-to-noise ratio (SNR) is greater than −10 dB. The influence of mutual interference on the signal correlation peak is less than 1% when the signal-to-interference ratio (SIR) of the mutual interference is greater than −28 dB. The proposed method is verified using an actual signal collected using an Alpha receiver. The results show that an Alpha signal can be detected at an extremely low SNR. This method has strong practicability and satisfies the application requirements of an Alpha receiver.

Design of Universal Code Generator for Multi-Constellation Multi-Frequency GNSS Receiver

A multi-constellation, multi-frequency Global Navigation Satellite System (GNSS) receiver is capable of simultaneously receiving signals from multiple satellite constellations across various frequency bands. This allows for increased observations, thereby enhancing navigation accuracy, continuity, effectiveness, and reliability. The spread spectrum code structures used in satellite navigation signals differ among systems. Compatible code generators are employed in multi-constellation, multi-frequency GNSS receivers to support tasks such as signal acquisition and tracking. There are three main types of spread spectrum code structures: Linear Feedback Shift Register (LFSR), Legendre sequences, and Memory codes. The Indian Regional Navigation Satellite System (IRNSS) released the L1-SPS (Standard Positioning Service) signal format in August 2023, which utilizes the Interleaved Z4-linear ranging code (IZ4 code) as its spread spectrum code. Currently, there is no universal code generator design compatible with the IZ4 code. In this paper, a proposed universal code generator is based on the hardware structure of the IRNSS IZ4 code generator. It achieves compatibility with all LFSR-based spread spectrum codes and enables parallel generation of multiple sets of GNSS signal spread spectrum codes, thereby improving hardware utilization efficiency. The proposed structure is implemented and validated using FPGA design, and resource consumption is provided as part of the validation results.

Intelligent non-cooperative optical networks: Leveraging scattering neural networks with small training data

Artificial intelligence (AI) is enabling intelligent communications where learning based signal classification simplifies optical network signal allocation and shifts signal processing pressure to each network edge. This work proposes a non-orthogonal signal waveform framework that leverages its unique spectral compression characteristic as a user address for efficiently forwarding messages to target users. The primary focus of this work lies in the physical layer intelligent receiver design, which can automatically identify different received signal formats without preamble notification in a non-cooperative communication approach. Traditional signal classification methods, such as convolutional neural network (CNN), rely on extensive training, resulting in a heavy dependency on large training datasets. To overcome this limitation, this work designs a specific two-layer scattering neural network that can accurately separate signals even when the training data is limited, leading to reduced training complexity. Its performance remains robust in diverse transmission conditions. Furthermore, the scattering neural network is interpretable because features are extracted based on deterministic wavelet filters rather than training based filters.

Deep-CNN based knowledge learning with Beluga Whale optimization using chaogram transformation using intelligent sensors for speech emotion recognition

In recent times, there has been a lot of focus placed on speech emotion recognition, often known as SER. SER is a fundamental approach to emotional human-machine interaction. Utilising DCNNs has resulted in significant advancements being made, notably with regard to the learning of high-level characteristics for SER. However, one of the primary challenges that arises when applying deep neural networks to SER is the problem of overfitting, which occurs commonly when too many parameters are chosen using insufficiently large datasets. The majority of the research that has been done to attempt to solve this problem has focused on translating the audio input into a picture and employing transfer learning methods. According to research, the patterns generated in this region include significant emotional aspects of the speaker. A new speech signal format called Chaogram was designed to project these patterns, which would result in three channels like RGB images, in order to give an input that works with VGG-based Deep-CNNs. The original phase space is then used to reconstruct the voice samples in a three-dimensional space. In the subsequent stage, the Chaogram photos' finer details were accentuated by image enhancing techniques. To learn Chaogram's high-level features and emotion classifications, the Visual Geometry Group (VGG) deep convolutional neural network (DCNN) is employed after being pre-trained on the massive ImageNet dataset using intelligent sensors. We next apply transfer learning to our datasets to further improve the provided model, which we combine with the proposed model. To enhance the hyper-parameter layout of architecture-determined CNNs, a new Deep-CNN with BWO (Beluga Whale Optimization) is introduced. In order to apply Deep–CNN–KL and to the field of SER, thispropose a new method of representing speech signals, which we call Chaograms, by mapping the signal onto a 2D image. On two publicly available emotion datasets, these findings demonstrate the ability of the proposed approach, which has the potential to significantly improve SER applications: EMO-DB and eNTERFACE05. Image enhancement methods that place more emphasis on such features may lead to more precise classification in the performance analysis. Classification accuracy can be considerably improved by adapting these images to work with the pre-trained Deep-CNN inputs.

Satellite Navigation Message Authentication in GNSS: Research on Message Scheduler for SBAS L1.

SBAS is mainly used in civil aviation and navigation, and will be applied to autonomous driving in the future. Given the open signal format of the Satellite Based Augmentation System (SBAS), which exposes security threats such as spoofing attacks, the utilization of SBAS navigation message authentication technology can improve the SBAS anti-spoofing ability from the system side. SBAS message authentication technology has become the future direction of SBAS system development. However, during the initial design of SBAS on L1, message authentication technology was not considered, and the addition of authentication messages will lead to further strain on existing message bandwidth resources. Therefore, in response to the issue of insufficient bandwidth resources after adding authentication messages to SBAS L1, a study on message scheduler for SBAS L1 authentication was conducted. A fixed time sequence dynamic message scheduler for incorporating authentication messages was proposed. This scheduler reduces the frequency of broadcasting clock error parameters to mitigate the impact of adding authentication messages. Furthermore, an optimized fixed time sequence dynamic message scheduler based on SBAS clock error messages was introduced. The results show that the message scheduler can not only improve the flexibility of SBAS message broadcasting, but also shorten the update interval of various types of messages under the premise of meeting the maximum update interval requirement. With minimal impact on the maximum message update interval, it improves the integrity, authenticity, and availability of messages. This approach can increase the effective message ratio in SBAS to over 91%, and the optimal solution reduces the initial user positioning time to 26 s.

A Sequence-to-Sequence Model for Online Signal Detection and Format Recognition

A Sequence-to-Sequence Model for Online Signal Detection and Format Recognition

Reverse polarity optical Orthogonal frequency Division Multiplexing for High-Speed visible light communications system

The radio frequency spectrum, which has been steadily getting smaller, is under pressure from growing cellular data traffic. A more dependable communication channel is required. To setup the communication medium, VLC uses LED illumination. In order to increase network throughput, the goal of this study is to analyze and model signal conditioning techniques in the visible spectrum. VLC technology offers synchronized illumination of LED lamps as well as wireless broadband connectivity. Using the real-valued O-OFDM baseband signal, optical orthogonal frequency division multiplexing (O-OFDM) offers a viable modulation option for very-low-bit-rate (VLC) systems to modulate the optical carrier's instantaneous power to attain gigabit data rates. However, one important design difficulty preventing VLC from being commercialized is how to combine industry-favored pulse width modulation (PWM) dimming technology while retaining a dependable, broadband connection. In this paper, Reverse Polarity optical orthogonal frequency division multiplexing (RPO-OFDM) is proposed as a new signal format for combining the fast O-OFDM contact signal with the comparatively slow PWM dimming signal, as both signals contribute to the illuminative brightness of the LED. The average SNR, additional testing demonstrates that the suggested method outperforms the Asymmetrically clipped optical OFDM (ACO), Optimized Flipped Optical (OFO) and DC biased optical OFDM (DCO), and algorithms in similar operating conditions.

Free-space transmission of picosecond-level, high-speed optical pulse streams in the 3 µm band.

The utilization of mid-infrared (mid-IR) light spanning the 3-5 µm range presents notable merits over the 1.5 µm band when operating in adverse atmospheric conditions. Consequently, it emerges as a promising prospect for serving as optical carriers in free-space communication (FSO) through atmospheric channels. However, due to the insufficient performance level of devices in the mid-IR band, the capability of mid-IR communication is hindered in terms of transmission capacity and signal format. In this study, we conduct experimental investigations on the transmission of time-domain multiplexed ultra-short optical pulse streams, with a pulse width of 1.8 ps and a data rate of up to 40 Gbps at 3.6 µm, based on the difference frequency generation (DFG) effect. The mid-IR transmitter realizes an effective wavelength conversion of optical time division multiplexing (OTDM) signals from 1.5 µm to 3.6 µm, and the obtained power of the 40 Gbps mid-IR OTDM signal at the optimum temperature of 54.8 °C is 7.4 dBm. The mid-IR receiver successfully achieves the regeneration of the 40 Gbps 1.5 µm OTDM signal, and the corresponding regenerated power at the optimum temperature of 51.5 °C is -30.56 dBm. Detailed results pertaining to the demodulation of regeneration 1.5 µm OTDM signal have been acquired, encompassing parameters such as pulse waveform diagram, bit error rate (BER), and Q factor. The estimated power penalty of the 40 Gbps mid-IR OTDM transmission is 2.4 dB at a BER of 1E-6, compared with the back-to-back (BTB) transmission. Moreover, it is feasible by using chirped PPLN crystals with wider bandwidth to increase the data rate to the order of one hundred gigabits.

All-Optical MIMO Demultiplexing Using Silicon-Photonic Dual-Polarization Optical Unitary Processor

Dual-polarization (DP) arbitrary optical unitary processor (OUP) is a critical device to realize an energy-efficient multi-input-multi-output (MIMO) process of mode-division multiplexed (MDM) systems in the optical domain. In this paper, a 6-port OUP with polarization-splitter-rotators is realized on a compact silicon photonic chip based on the multi-plane light conversion (MPLC) concept. All-optical MIMO demultiplexing of 300-Gbps 3-mode DP quadrature phase-shift-keying (QPSK) signal is demonstrated at 1540, 1550, and 1560-nm wavelengths with an energy consumption of around 1.5 pJ/bit. Since all-optical MIMO is transparent to the signal format, the energy cost (J/bit) would decrease inversely as we increase the baudrate, making it an attractive approach in reducing the power consumption of the future ultra-high-capacity MDM systems.

DETECTION AND CLASSIFICATION OF COVID-19 CASES FROM OTHER CARDIOVASCULAR CLASSES FROM ELECTROCARDIOGRAPHY SIGNALS USING DEEP LEARNING AND ResNet NETWORK

Nowadays, the world confronts a highly infectious pandemic called coronavirus (COVID-19) and over 4 million people worldwide have now died from this illness. So, early detection of COVID-19 outbreak and distinguishing it from other diseases with the same physical symptoms can give enough time for treatment with true positive results and prevent coma or death. For early recognition of COVID-19, several methods for each modality are proposed. Although there are some modalities for COVID-19 detection, electrocardiography (ECG) is one of the fastest, the most accessible, the cheapest and the safest one. This paper proposed a new method for classifying COVID-19 patients from other cardiovascular disease by ECG signals. In the proposed method, ResNet50v2 which is a kind of convolutional neural network, is used for classification. In this paper because of image format of data, first data with image format are applied to the network and then for comparison, ECG images are changed to signal format and classification is done. These two strategies are used for COVID-19 classification from other cardiac abnormalities with different filter sizes and the results of strategies are compared with each other and other methods in this field. As it can be concluded from the results, signal-based data give better accuracy than image classification at best performance and it is better to change the image format to signals for classification. The second result can be found by comparing with other methods in this field, the proposed method of this paper gives better performance with high accuracy in COVID-19 classification.

Modeling and Analysis of Device Orientation, Analog and Digital Performance of Electrode Design for High Speed Electro-Optic Modulator

Electro-optic modulators (EOMs) are crucial devices for modern communication enabling high bandwidth optical communication links. Traveling wave electrodes are used to obtain high-speed modulation in these EOMs. We present the electrode design and analysis along with the study of effects of changing orientation on device performance for a thin-film lithium niobate tunable Mach–Zehnder interferometer (MZI) that offers sub-THz bandwidth operations. High velocity and impedance matching with low RF attenuation, high third-order SFDR (∼121 dB/Hz2/3) and a low half-wave voltage length product (1.74 V.cm) have been achieved for a bandwidth of 136 GHz. High-speed digital modulation using multi-level signal formats (PAM-2, QAM-4 and QAM-16) with low BER for 400 Gbps data has been demonstrated to assess the digital performance of the device.

An Inductorless 20-Gbaud Overshoot Suppression Modulator Driver Using Interleave Frequency Variant Feedback in 90-nm CMOS

An inductorless modulator driver with effective overshoot suppression and a large swing for high-speed optical communications is presented. A novel interleave frequency variant feedback (IFVF) technique is proposed, which employs active feedback loops with the RC degeneration. The <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$RC$ </tex-math></inline-formula> degeneration allows place the pole in the overall transfer function to shape the waveform for overshoot and ringing suppression. Using a 90-nm complementary metal oxide semiconductor (CMOS) technology, the driver achieves a maximum 3.4 Vpp differential output voltage swing. Clear eye diagrams for non-return to zero (NRZ) and pulse amplitude modulation 4-level (PAM4) signal formats can be obtained up to 32 Gb/s and 20 Gbaud, respectively. The circuit consumes a total power of 560 mW and occupies a core area of only 0.092 mm2.

Broadband physical layer cognitive radio with an integrated photonic processor for blind source separation

The expansion of telecommunications incurs increasingly severe crosstalk and interference, and a physical layer cognitive method, called blind source separation (BSS), can effectively address these issues. BSS requires minimal prior knowledge to recover signals from their mixtures, agnostic to the carrier frequency, signal format, and channel conditions. However, previous electronic implementations did not fulfil this versatility due to the inherently narrow bandwidth of radio-frequency (RF) components, the high energy consumption of digital signal processors (DSP), and their shared weaknesses of low scalability. Here, we report a photonic BSS approach that inherits the advantages of optical devices and fully fulfils its “blindness” aspect. Using a microring weight bank integrated on a photonic chip, we demonstrate energy-efficient, wavelength-division multiplexing (WDM) scalable BSS across 19.2 GHz processing bandwidth. Our system also has a high (9-bit) resolution for signal demixing thanks to a recently developed dithering control method, resulting in higher signal-to-interference ratios (SIR) even for ill-conditioned mixtures.

A Digital Self-Interference Cancellation Scheme for In-Band Full-Duplex-Applied 5G System and Its Software-Defined Radio Implementation

The development of mobile communication technology toward 6G is difficult because the required spectrum resources have already been allocated to existing systems. Therefore, new communication systems must have high spectral efficiency to effectively utilize the available resources. This article proposes a digital self-interference (SI) canceller that can be applied to full-duplex cellular systems based on the current 5G signal format (5G-FDC) to introduce in-band full-duplex systems beyond 5G. Specifically, we reorganize the 5G demodulation reference signal (DMRS) and define a novel DMRS configuration, in which the DMRSs for uplink and downlink communications do not interfere with each other. The proposed DMRS configuration effectively improves the accuracy of the SI channel estimation. Moreover, we propose a channel extrapolation scheme that suppresses the channel estimation degradation, which deteriorates the block error rate performance. Additionally, we propose a noise estimation method to improve the low-density parity check decoding performance of the desired signal in 5G-FDC systems. The effectiveness of the proposed method is demonstrated through computer simulations and experimentally by using our developed and implemented software-defined radio-based 5G-FDC prototype. Digital SI cancellation performances of 50 dB and 30.6 dB are observed in the simulation and experimental evaluations, respectively.

Sub-column pixel neural network scheme for modulation format shifting based optical camera communications.

In this Letter, a sub-column pixel neural network (SCPNN) scheme is proposed to label the pixel patterns in modulation format shifting based optical camera communication (MFS-OCC) systems. The MFS scheme, by identifying on-off keying (OOK) and Manchester signal formats, can additionally harvest one-bit information without sacrificing the pixel width per bit, resulting in a higher transmission rate for OCC without multi-level modulation. Compared with the conventional multi-threshold decision (MTD) scheme, the proposed SCPNN can effectively mitigate the impact of inter-symbol interference (ISI) and noise on signal decision by making full use of all pixels in one-bit period. The experimental results show that, in the case of the illuminance as low as 300 lux, the SCPNN scheme can attain a data rate of 8.16 Kbps with an average bit-error-rate (BER) of 2.2 × 10-3 below the HD-FEC limitation of 3.8 × 10-3. Meanwhile, the SCPNN scheme achieves remarkable BER performance improvement compared to both MFS-OCC with MTD scheme and conventional OOK-OCC system.

Index Modulation Pattern Design for Non-Orthogonal Multicarrier Signal Waveforms

Spectral efficiency improvement is a key focus in most wireless communication systems and achieved by various means such as using large antenna arrays and/or advanced modulation schemes and signal formats. This work proposes to further improve spectral efficiency through combining non-orthogonal spectrally efficient frequency division multiplexing (SEFDM) systems with index modulation (IM), which can efficiently make use of the indices of activated subcarriers as communication information. Recent research has verified that IM may be used with SEFDM to alleviate inter-carrier interference (ICI) and improve error performance. This work proposes new SEFDM signal formats based on novel activation pattern designs, which limit the locations of activated subcarriers and enable a variable number of activated subcarriers in each SEFDM subblock. SEFDM-IM system designs are developed by jointly considering activation patterns, modulation schemes and signal waveform formats, with a set of solutions evaluated under different spectral efficiency scenarios. Detailed modelling of coded systems and simulation studies reveal that the proposed designs not only lead to better bit error rate (BER) but also lower peak-to-average power ratio (PAPR) and reduced computational complexity relative to other reported index-modulated systems.