Multiplexing Signals Research Articles

Non-Contact Acquisition of PPG Signal using Chest Movement-Modulated Radio Signals

We present for the first time a novel method that utilizes the chest movement-modulated radio signals for non-contact acquisition of the photoplethysmography (PPG) signal. Under the proposed method, a software-defined radio (SDR) exposes the chest of a subject sitting nearby to an orthogonal frequency division multiplexing signal with 64 sub-carriers at a center frequency 5.24 GHz, while another SDR in the close vicinity collects the modulated radio signal reflected of the chest. This way, we construct a custom dataset by collecting 160 minutes of labeled data (both raw radio data as well as the reference PPG signal) from 16 healthy young subjects. With this, we first utilize principal component analysis for dimensionality reduction of the radio data. Next, we denoise the radio signal and reference PPG signal using wavelet technique, followed by segmentation and Z-score normalization. We then synchronize the radio and PPG segments using cross-correlation method. Finally, we proceed to the waveform translation (regression) task, whereby we first convert the radio and PPG segments into frequency domain using discrete cosine transform (DCT), and then learn the non-linear regression between them. Eventually, we reconstruct the synthetic PPG signal by taking inverse DCT of the output of regression block, with a mean absolute error of 8.1294. The synthetic PPG waveform has a great clinical significance as it could be used for non-contact performance assessment of cardiovascular and respiratory systems of patients suffering from infectious diseases, e.g., covid19.

4.096 Tbit/s multidimensional multiplexing signals transmission over 1000 km few mode fiber

4.096 Tbit/s multidimensional multiplexing signals transmission over 1000 km few mode fiber

Parallel implement of real-time delta-sigma modulation for digital mobile fronthaul.

We propose and experimentally demonstrate a novel FPGA-based parallel architecture for delta-sigma modulation (DSM) for digital mobile fronthaul employing the DSM interface. This architecture breaks the limitations of the feedback loop in DSM, is not constrained by critical paths, and supports fully parallel processing, so it can deal with high sampling rates at low hardware operating speeds. In contrast to other parallel schemes, the proposed bit-by-bit quantization DSM avoid significant storage resources requirements for buffering. A real-time experimental system using Xilinx Kintex Ultrascale FPGA was implemented to validate the feasibility of the proposed architecture. 14 carrier aggregated orthogonal frequency division multiplexing (OFDM) signals are digitized by DSM into a 5Gb/s PAM4 signal and transmitted over a 20 km single-mode fiber (SMF). As a waveform-agnostic digitization interface, we also experimentally demonstrated the DSM with 14 carrier aggregated filter-bank-multicarrier (FBMC) signals, which can achieve better EVM performance.

Abstract B008: Machine-learning enabled quantification of colocalized multiplex IHC signals with spectral overlap

Abstract Background: Immunohistochemistry (IHC) is the gold standard for detecting protein biomarkers in cancer. In breast cancer, several nuclear biomarkers are relevant for guiding treatment decisions, including estrogen receptor (ER) and progesterone receptor (PR), indicative of response to endocrine therapy, and the proliferation marker Ki67, indicative of response to CDK inhibition1. However, these nuclear markers are detected using chromogens that colocalize and spectrally overlap with hematoxylin (HTX), the standard nuclear counterstain for IHC. Thus, the accurate detection and quantification of nuclear antigens is challenging. To overcome this limitation of IHC-based biomarker assays, we developed a method to identify colocalized signals after staining with chromophores with spectral overlap. Methods: Multiplex IHC (mIHC) using primary antibodies against Ki67, PR, and ER was performed on a breast cancer tissue microarray (N=65 cores). Antigens were detected by dabsyl-, tamra-, and Cy5-conjugated secondary antibodies, respectively, and slides were counterstained with HTX. The cores were scanned with brightfield imaging (Aperio AT2 and 3DHistech microscopes). The same cores were imaged on a custom brightfield microscope using an LED light source and 10 nm wide band-pass filters at 405, 450, 520, 550, 589, 632 and 660 nm. Images were spectrally unmixed using absorption spectra of the different chromophores and the background estimated based on the images themselves and aligned with the brightfield images. Pathologist-trained individuals collected exhaustive manual annotations in regions of interest for each marker and HTX using unmixed channels. A convolutional neural network (CNN) was trained using the annotations for each marker and HTX and applied to the WSI. Predicted cell locations were aggregated across the four channels to obtain an exhaustive “super-annotation” co-expression map of the WSI. Super-annotations were then used to train a CNN to detect ER/PR/Ki67/HTX colocalization in the aligned RBG image of the same slide. Predictions from the super-annotation model were compared to ER/PR/Ki67/HTX detection done via HALO (Indica Labs). Results: Our multi-spectral imaging pipeline allowed for comprehensive annotations of all nuclei positive for at least one of the three markers or HTX. Using our super-annotation model, the count error of nuclei that are positive for HTX and one, two, or three markers was reduced by 7.8%, 6.4%, and 0.03%, respectively, compared to HALO. Conclusions: We have developed a method to identify colocalized antigens after mIHC using chromogens with spectral overlap. Related work to extend this approach to membranous biomarkers is ongoing and will enable additional novel analyses (e.g., describing the precise spatial organization of PD-L1+ tumor cells relative to CD8+ tumor-infiltrating lymphocytes). The approach that we describe here has great potential to facilitate the utilization of mIHC for the clinical detection of biomarkers in oncology.

Performance optimization and parameters estimation for MIMO-OFDM dual-functional communication-radar systems

Dual-function communication radar systems use common Radio Frequency (RF) signals are used for both communication and detection. For better compatibility with existing communication systems, we adopt Multiple-Input Multiple-Output (MIMO) Orthogonal Frequency Division Multiplexing (OFDM) signals as integrated signals and investigate the estimation performance of MIMO-OFDM signals. First, we analyze the Cramer-Rao Lower Bound (CRLB) of parameter estimation. Then, the transmit powers over different subcarriers are optimized to achieve the best tradeoff between the transmission rate and the estimation performance. Finally, we propose a more accurate estimation method that uses Canonical Polyadic Decomposition (CPD) of the third-order tensor to obtain the parameter matrices. Due to the characteristic of the column structure of the parameter matrices, we only need to use DFT / IDFT to recover the parameters of multiple targets. The simulation results show that tensor-based estimation method can achieve a performance close to CRLB, and the estimation performance can be improved by optimizing the transmit powers.

A novel PAPR reduction scheme based on time-delay neural network for CO-OFDM systems

In this paper, we propose a low-complexity peak-to-average power ratio (PAPR) reduction scheme based on time-delay neural network (TDNN) for coherent optical orthogonal frequency division multiplexing (CO-OFDM) systems to reduce the limitation of PAPR on the total power of laser diodes and the impact of fiber nonlinear effects. The proposed scheme is jointly trained on traditional iterative clipping and filtering (ICF) scheme and simplified clipping and filtering (SCF) scheme, and changes the objective function by adjusting the given training weight α. Compared to the original orthogonal frequency division multiplexing (OFDM) signal of existing system, our method achieves an adjustable PAPR reduction of 5.31 dB to 6.12 dB for 10−4 complementary cumulative distribution function (CCDF). When bit error rate (BER) Pe=10−3, the proposed scheme (α=0.5) achieves additional transmission distances of 220 km, 55 km, 105 km, and 100 km, compared to the original OFDM signal, partial transmission sequence (PTS) scheme, ICF scheme, and feedforward neural network (FNN)-based ICF scheme, respectively. Besides, our method achieves 86.3% and 80.0% floating-point operations (FLOPs) reductions, compared with PTS scheme and ICF scheme, respectively, which effectively reduces the complexity of PAPR reduction module, saving the system computational time.

Dendrogram-based Heterogeneous Learners for Automatic Modulation Classification in DSTBC-OFDM Systems

Software-Defined Radios (SDRs) have a crucial role in dynamic spectrum sensing and sharing as they can quickly adapt to changes in their environment. Digital Modulation Recognition (DMR) enables this process by giving SDR the ability to recognize the received signal modulation format to make intelligent and autonomous decisions to switch on the optimal modulation scheme. However, with the deployment of the Distributed Space-Time Block Coding Orthogonal Frequency Division Multiplexing (DSTBC-OFDM) signals that support MIMO systems, that provides spatio-temporel multiplexing, new and challenging signal identification problems have emerged.This paper proposes a robust DMR method for relaying Space-Time Block Coding (STBC) Orthogonal Frequency Division Multiplexing (OFDM) systems. The proposed classifier is built using heterogeneous base classifiers namely, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Random Forest (RFC), based on a Dendrogram or decision tree structure.The Dendrogram-based Heterogeneous Learners (DHL) classifier were obtained via Ascendant Hierarchical Clustering (AHC) and by employing Higher-Order Moment (HOMs) and Higher-Order Cumulants (HOCs) as features. Moreover, we propose a grid search-based hyperparameter tuning for DHL to produce the best structure and optimize the performance.

Computationally efficient composite triple beat cancellation for DML-based DA-RoF fronthaul.

The digital-analog radio-over-fiber (DA-RoF) scheme offers a high-fidelity and spectrally efficient solution for future mobile fronthaul. However, to be implemented in the low-cost directly modulated laser with direct detection (DML-DD) link, both the digital and analog parts in DA-RoF modulation would suffer from the composite second-order (CSO) and composite triple beat (CTB) caused by the chirp-dispersion interaction. In this Letter, we propose and experimentally demonstrate a computationally efficient composite triple beat cancellation (CTB-C) algorithm for DA-RoF fronthaul in the dispersion-uncompensated C-band DML-DD link. The CSO and CTB are suppressed at the receiver-side DSP based on the theoretical model of these nonlinear distortions. In the proof-of-concept experiment, a 1.2-dB improvement in the recovered signal-to-noise ratio (SNR) is obtained with 5.5-GHz 1024-QAM orthogonal frequency division multiplexing (OFDM) signal after 10-km standard single-mode fiber (SSMF) transmission. The proposed CTB-C technique does not require the training process and performs close to the Volterra-based feed-forward equalizer (VFE) under the complexity constraint.

A 24-30GHz Power Amplifier with >20-dBm Psat and <0.1-dB AM-AM Distortion for 5G Applications in 130-nm SiGe BiCMOS

This paper presents a power amplifier (PA) designed as a part of a transceiver front-end fabricated in 130-nm SiGe BiCMOS. The PA shares its output antenna port with a low noise amplifier using a low-loss transmission/reception switch. The output matching network of the PA is designed to provide high output power, low AM-AM distortion, and uniform performance over frequencies in the range of 24.25-29.5 GHz. Measurements of the front-end in TX mode demonstrate peak S21 of 30.3 dB at 26.7 GHz, S21 3-dB bandwidth of 9.8 GHz from 22.2 to 32.0 GHz, and saturated output power (Psat) above 20 dBm with power-added efficiency (PAE) above 22% from 24 to 30 GHz. For a 64-QAM 400 MHz bandwidth orthogonal frequency division multiplexing (OFDM) signal, -25 dBc error vector magnitude (EVM) is measured at an average output power of 12.3 dBm and average PAE of 8.8%. The PA achieves a competitive ITRS FoM of 92.9.

High-security three-dimensional optical transmission mechanism utilizing time-frequency-space interleaving disruption.

In this paper, we propose a high-security three-dimensional optical transmission system utilizing time-frequency-space interleaving chaos, which simultaneously enhances the reliability and security of the system. The four-wing 3D chaos model encrypts the time-frequency space interleaved modulation domain of a orthogonal time-frequency space (OTFS) modulation signal and the modulated phase information simultaneously, improving the system's security. We also experimentally validate the proposed high-security 3D-OTFS method, utilizing the hexadecimal modulation technique. The modulated OTFS signal achieves a transmission rate of 34.1 Gb/s over a 2-km seven-core fiber link, with the OTFS signal exhibiting a maximum of 1.31 dB receiver sensitivity gain compared to orthogonal frequency division multiplexing (OFDM) signals under the forward error correction threshold of the bit error rate. The achieved keyspace is equal to 5 × 1048. The findings demonstrate that the proposed high-security three-dimensional optical transmission mechanism, based on time-frequency-space interleaved disruption, exhibits excellent anti-interference ability and confidentiality performance. Consequently, it holds promising prospects for future applications in optical communications.

Hand-Breathe: Noncontact Monitoring of Breathing Abnormalities From Hand Palm

In post-covid19 world, radio frequency (RF)-based non-contact methods, e.g., software-defined radios (SDR)-based methods have emerged as promising candidates for intelligent remote sensing of human vitals, and could help in containment of contagious viruses like covid19. To this end, this work utilizes the universal software radio peripherals (USRP)-based SDRs along with classical machine learning (ML) methods to design a non-contact method to monitor different breathing abnormalities. Under our proposed method, a subject rests his/her hand on a table in between the transmit and receive antennas, while an orthogonal frequency division multiplexing (OFDM) signal passes through the hand. Subsequently, the receiver extracts the channel frequency response (basically, fine-grained wireless channel state information), and feeds it to various ML algorithms which eventually classify between different breathing abnormalities. Among all classifiers, linear SVM classifier resulted in a maximum accuracy of 88.1%. To train the ML classifiers in a supervised manner, data was collected by doing real-time experiments on 4 subjects in a lab environment. For label generation purpose, the breathing of the subjects was classified into three classes: normal, fast, and slow breathing. Furthermore, in addition to our proposed method (where only a hand is exposed to RF signals), we also implemented and tested the state-of-the-art method (where full chest is exposed to RF radiation). The performance comparison of the two methods reveals a trade-off, i.e., the accuracy of our proposed method is slightly inferior but our method results in minimal body exposure to RF radiation, compared to the benchmark method.

Machine-learning-driven optical immunosensor based on microspheres-encoded signal transduction for the rapid and multiplexed detection of antibiotics in milk

Antibiotic residues are the most common contaminants in milk and other related dairy products. Simultaneous, convenient, and stable detection of antibiotic residues in foods is vital to secure public health. Herein, we proposed an optical immunosensor with easily-functionalized polystyrene nanoparticles differing in size and quantity, and bearing multiplex signal probes for the simultaneous detection of multiple antibiotics through a simple one-step signal conversion reaction. After the integration of the machine-learning-based transcoding analysis, this sensor is suitable for multiplexed detection of antibiotics in a broad linear range from pg/mL to ng/mL within 30 min, with an overall accuracy of >99 %. Compared to the conventional standard chemiluminescence immunoassays, this immunosensor is suitable for the accurate quantification of multiple antibiotics in milk, with improved accuracy, reduced costs, and simplified procedure. This ensures its applications in food safety monitoring when simultaneous detection of multiple hazardous substances in food matrices is needed.

TCR ligand potency differentially impacts PD-1 inhibitory effects on diverse signaling pathways.

Checkpoint blockade revolutionized cancer therapy, but we still lack a quantitative, mechanistic understanding of how inhibitory receptors affect diverse signaling pathways. To address this issue, we developed and applied a fluorescent intracellular live multiplex signal transduction activity reporter (FILMSTAR) system to analyze PD-1-induced suppressive effects. These studies identified pathways triggered solely by TCR or requiring both TCR and CD28 inputs. Using presenting cells differing in PD-L1 and CD80 expression while displaying TCR ligands of distinct potency, we found that PD-1-mediated inhibition primarily targets TCR-linked signals in a manner highly sensitive to peptide ligand quality. These findings help resolve discrepancies in existing data about the site(s) of PD-1 inhibition in T cells while emphasizing the importance of neoantigen potency in controlling the effects of checkpoint therapy.

Direct acoustic communication between underwater and airborne nodes

Direct acoustic communication between underwater and airborne nodes has always been considered unfeasible due to the energy loss caused by the strong surface reflection of sound waves. However, contrary to popular belief, our study demonstrates that underwater transducers can effectively transmit detectable acoustic signals in the air. To investigate this phenomenon, we conducted an experiment involving the deployment of an underwater transducer at a depth of 1 m, while an unoccupied aerial vehicle equipped with a voice recorder was positioned at various altitudes ranging from 2 to 30 m and horizontal distances of 0–30 m. Sound pressure levels were measured at 20 different positions within the frequency range of 10–20kHz, and orthogonal frequency division multiplex acoustic communication signals were recorded at specific positions. Our findings reveal the successful establishment of a direct acoustic communication link between the water and air interface, achieving a data rate of 4.565 kbps. This study opens up new possibilities for practical applications in underwater-to-air communication systems.

A novel PAPR reduction scheme based on nonlinear real-valued support vector regression for CO-OFDM systems

Coherent optical orthogonal frequency division multiplexing (CO-OFDM) systems suffer from the high peak-to-average ratio (PAPR) of orthogonal frequency division multiplexing (OFDM) signals, which leads to the back-off of laser power and fiber nonlinear effects, thereby deteriorating the bit error rate (BER) performance and limiting the transmission distance. Iterative clipping and filtering (ICF) scheme, as a direct, simple and effective PAPR reduction scheme that only processes signals at the transmitter, but requires multiple fast Fourier transform (FFT)/inverse fast Fourier transform (IFFT), resulting in high computational complexity. We propose a novel scheme using the machine learning (ML) technique, which is based on nonlinear real-valued support vector regression (NRSVR) and trained on ICF scheme. The simulation results show that compared with ICF scheme, the proposed scheme has a similar PAPR0 for 10−4 complementary cumulative distribution function (CCDF), an additional 15 km of single mode fiber (SMF) transmission for 16 dB optical signal-to-noise ratio (OSNR) and 10−3BER, and a significant time complexity reduction. Compared with other ML-based schemes, the proposed scheme also achieves better performance and lower computational complexity.

Research on full duplex FSO access system with hybrid 16PSK/256QAM-OFDM downlink and duobinary uplink signals

a 100Gbit/s novel full duplex free space optical (FSO) access system scheme is proposed and demonstrated. Using the downlink hybrid 16phase shift keying (PSK)/256quadrature amplitude modulation (QAM)-orthogonal frequency division multiplexing (OFDM) and duobinary uplink signals. In downlink branch, in-phase/quadrature-phase (I/Q) modulation based on Mach-Zehnder modulator (MZM) and coherent detection technology are adopted. Meanwhile, 0.1Terahertz (THz) wave signal generated by two independent optical sidebands beating frequency at positive intrinsic-negative photodiodes (PIN PDs) for hybrid downlink 16PSK/256QM-OFDM signals. In uplink branch, intensity modulation and direct detection (IM/DD) can achieve reduced the communication budget cost and simplified the system configuration Simulation results show that the value of bit error ratio (BER) less than hardware decision forward error correction (HD-FEC) threshold after 300 m FSO channel transmission under the different weather conditions (such as sunny, rainy and snow).

Low-Complexity Conditional Generative Adversarial Network (c-GAN) Based Nonlinear Equalizer for Coherent Data-Center Interconnections

Nonlinear impairments emerging from both fiber optical links and optoelectrical devices are the major bottleneck for enhancing the throughput of photonic data-center interconnection (DCI). Here, we propose a data-driven nonlinear equalizer based on a conditional generative adversarial network (c-GAN) for high-speed and large-capacity photonic DCI. Its performance is experimentally evaluated by transmitting C-band 40-channel 60-GBaud dual polarization-16 quadrature amplitude modulation (DP-16QAM) dense wavelength division multiplexing (DWDM) signals over 20-km standard single-mode fiber (SSMF). Our experimental results verify that, the proposed c-GAN equalizer outperforms both traditional nonlinear equalizers and other data-driven counterparts, in terms of both the bit-error ratio (BER) and computational complexity. Only the proposed c-GAN equalizer can guarantee the BER performance of all 40 channels to reach the threshold of 20% soft decision forward error correction (SD-FEC) at BER=0.027, leading to a net transmission capacity of 16 Tb/s. Meanwhile, the computational complexity of the proposed c-GAN equalizer can be significantly reduced by 31.8%, 80.7%, and 98.8%, respectively, in comparison with Volterra filter equalizer (VFE), multilayer perceptron (MLP), and long short-term memory neural network (LSTM-NN) schemes.

Multi-Aperture Coherent Digital Combining Based on Complex-Valued MIMO 2N × 2 Adaptive Equalizer for FSO Communication

Collecting light using multiple small apertures offers a lot of advantages over doing so using a single large aperture, including the effective mitigation of scintillation under strong turbulence, low implementation cost, and ease of scalability. However, the tricky of a multi-aperture system is to combine the multiple branches signals with static skew mismatch and with time-varying characteristics of gain, phase and state of polarization, especially for coherent digital combining of polarization multiplexing (PM) signals. In this paper, a complex-valued multiple-input multiple-output (MIMO) 2N×2 adaptive equalizer is designed to combine multi-aperture signals, and the tap coefficient of each branch is calculated based on the constant modulus algorithm (CMA). This technique relaxes hardware constraints on the time alignment of multi-aperture branch since the skew can be compensated electronically thanks to the proposed blind digital combining algorithm. In addition, laser linewidth within evaluation range smaller than 10 MHz has no effect on the performance of 2N×2 adaptive equalizer. Moreover, the feasibility of the proposed adaptive equalizer for moderate and strong turbulence suppression is verified by a 10-Gbps data rate PM-QPSK modulation offline two- and four-aperture simulated turbulence experiment.

Simple and low-latency multipoint-to-point aggregation architecture using commercial optical transceivers for uplink fronthaul.

The increasing demand of real-time applications poses a huge challenge to building next-generation radio access network (NG-RAN) with higher stability and lower system complexity. Parallel signal detection (PSD), which aggregates signals of different intermediate frequencies (IFs) on different wavelengths with a single photodiode (PD), becomes a promising candidate for uplink mobile fronthaul with the advantage of low-latency. However, high requirements on the transmitters inhibit the large-scale deployment of radio units (RU). In this paper, we propose an economical, low-latency, multipoint-to-point (MP2P) uplink fronthaul architecture capable of aggregating four end-users with commercial 25G-class optical modules and a single PD. With delta-sigma modulation (DSM), commercial off-the-shelf optical modules can replace analog transmitters in traditional systems. As a demonstration, we aggregated 4 × 380.16-MHz 5 G new radio (NR) orthogonal frequency division multiplexing (OFDM) signals in an IF band with a fixed interval of 400 MHz over 20 km fiber with 4 users.

Distance-Regulated Photoelectrochemical Sensor "Signal-On" and "Signal-Off" Transitions for the Multiplexed Detection of Viruses Exposed in the Aquatic Environment.

Photochemical (PEC) sensors were severely limited for multiplex detection applications due to the cross interference between multiplex signals at the single recognition interface. In this work, a distance-regulated PEC sensor was developed for multiplex detection by using an i-Motif sequence with conformational transformation activity as the signal transduction unit. Through dynamic regulation of the spatial distance between the end site of the functional sequence and the electrode material, the photogenerated electrons on the surface of the sensor were directionally transferred. Thus, a PEC sensor with "signal-on" and "signal-off" dual signal output modes was developed for simultaneous detection of multitarget molecules. Combining isothermal nucleic acid amplification, the PEC sensor constructed in this work was successfully applied to the detection of two virus (Norovirus and Rotavirus) nucleic acid sequences. Under the optimal condition, this bioassay protocol exhibits a linear range of 0.01-100 nM for both viruses with detection limits of 0.72 and 0.53 pM, respectively. In this study, a stimulus-mediated distance regulation strategy successfully addressed the transduction of multiplex detection signals at the single recognition interface of the PEC sensor. It is expected that the technical barriers to multiplex detection of PEC sensors will be overcome and the application of PEC sensing technology will be expanded in the field of environmental analysis.