Design of Protograph LDPC-Coded MIMO-VLC Systems with Generalized Spatial Modulation

This paper investigates the bit-interleaved coded generalized spatial modulation(BICGSM) with iterative decoding(BICGSM-ID) for multiple-input multiple-output(MIMO) visible light communications(VLC). In the BICGSM-ID scheme, the information bits conveyed by the signal-domain(SiD) symbols and the spatial-domain(SpD) light emitting diode(LED)-index patterns are coded by a protograph low-density parity-check(P-LDPC) code. Specifically, we propose a signal-domain symbol expanding and re-allocating(SSER) method for constructing a type of novel generalized spatial modulation(GSM) constellations, referred to as SSERGSM constellations, so as to boost the performance of the BICGSM-ID MIMO-VLC systems.Moreover, by applying a modified PEXIT(MPEXIT) algorithm, we further design a family of rate-compatible P-LDPC codes, referred to as enhanced accumulate-repeat-accumulate(EARA) codes,which possess both excellent decoding thresholds and linear-minimum-distance-growth property. Both analysis and simulation results illustrate that the proposed SSERGSM constellations and P-LDPC codes can remarkably improve the convergence and decoding performance of MIMO-VLC systems. Therefore, the proposed P-LDPC-coded SSERGSM-mapped BICGSMID configuration is envisioned as a promising transmission solution to satisfy the high-throughput requirement of MIMO-VLC applications.

Cluster Modulation: A Generalized Modulation Scheme Leading to NOMA or Adaptive Modulation

The need for higher data rate and higher systems capacity leads to several solutions including higher constellation size, spatial multiplexing, adaptive modulation and Non-Orthogonal Multiple Access (NOMA). Adaptive Modulation makes use of the user’s location from his base station, such that, closer users get bigger constellation size and hence higher data rate. A similar idea of adaptive modulation that makes use of the user’s locations is the NOMA technique. Here the base station transmits composite signals each for a different user at a different distance from the base station. The transmitted signal is formed by summing different user’s constellations with different weights. The closer the users the less average power constellation is used. This will allow the closer user to the base station to distinguish his constellation and others constellation. The far user will only distinguish his constellation and other user’s data will appear as a small interference added to his signal. In this paper, it is shown that the Adaptive modulation and the NOMA are special cases of the more general Cluster Modulation technique. Therefore, a general frame can be set to design both modulation schemes and better understanding is achieved. This leads to designing a multi-level NOMA and/or flexible adaptive modulation with combined channel coding.

High photoelectric conversion efficiency and fast relaxation time of FA_(0.4)MA_(0.6)PbI_(3) applied in ultrafast modulation of terahertz waves

Active control of terahertz(THz)waves is attracting tremendous attentions in terahertz communications and active photonic devices.Perovskite,due to its excellent photoelectric conversion performance and simple manufacturing process,has emerged as a promising candidate for optoelectronic applications.However,the exploration of perovskites in optically controlled THz modulators is still limited.In this work,the photoelectric properties and carrier dynamics of FA_(0.4)MA_(0.6)PbI_(3)perovskite films were investigated by optical pumped terahertz probe(OPTP)system.The ultrafast carrier dynamics reveal that FA_(0.4)MA_(0.6)PbI_(3)thin film exhibits rapid switching and relaxation time within picosecond level,suggesting that FA_(0.4)MA_(0.6)PbI_(3)is an ideal candidate for active THz devices with ultrafast response.Furthermore,as a proof of concept,a FA_(0.4)MA_(0.6)PbI_(3)-based metadevice with integrating plasma-induced transparency(PIT)effect was fabricated to achieve ultrafast modulation of THz wave.The experimental results demonstrated that the switching time of FA_(0.4)MA_(0.6)PbI_(3)-based THz modulator is near to 3.5 ps,and the threshold of optical pump is as low as 12.7μJ cm^(-2).The simulation results attribute the mechanism of ultrafast THz modulation to photo-induced free carriers in the FA_(0.4)MA_(0.6)PbI_(3)layer,which progressively shorten the capacitive gap of PIT resonator.This study not only illuminates the potential of FA_(0.4)MA_(0.6)PbI_(3)in THz modulation,but also contributes to the field of ultrafast photonic devices.

From VIB‑to VB‑Group Transition Metal Disulfides:Structure Engineering Modulation for Superior Electromagnetic Wave Absorption

The laminated transition metal disulfides(TMDs),which are well known as typical two-dimensional(2D)semiconductive materials,possess a unique layered structure,leading to their wide-spread applications in various fields,such as catalysis,energy storage,sensing,etc.In recent years,a lot of research work on TMDs based functional materials in the fields of electromagnetic wave absorption(EMA)has been carried out.Therefore,it is of great significance to elaborate the influence of TMDs on EMA in time to speed up the application.In this review,recent advances in the development of electromagnetic wave(EMW)absorbers based on TMDs,ranging from the VIB group to the VB group are summarized.Their compositions,microstructures,electronic properties,and synthesis methods are presented in detail.Particularly,the modulation of structure engineering from the aspects of heterostructures,defects,morphologies and phases are systematically summarized,focusing on optimizing impedance matching and increasing dielectric and magnetic losses in the EMA materials with tunable EMW absorption performance.Milestones as well as the challenges are also identified to guide the design of new TMDs based dielectric EMA materials with high performance.

Automatic modulation recognition of radio fuzes using a DR2D-based adaptive denoising method and textural feature extraction

The identification of intercepted radio fuze modulation types is a prerequisite for decision-making in interference systems.However,the electromagnetic environment of modern battlefields is complex,and the signal-to-noise ratio(SNR)of such environments is usually low,which makes it difficult to implement accurate recognition of radio fuzes.To solve the above problem,a radio fuze automatic modulation recognition(AMR)method for low-SNR environments is proposed.First,an adaptive denoising algorithm based on data rearrangement and the two-dimensional(2D)fast Fourier transform(FFT)(DR2D)is used to reduce the noise of the intercepted radio fuze intermediate frequency(IF)signal.Then,the textural features of the denoised IF signal rearranged data matrix are extracted from the statistical indicator vectors of gray-level cooccurrence matrices(GLCMs),and support vector machines(SVMs)are used for classification.The DR2D-based adaptive denoising algorithm achieves an average correlation coefficient of more than 0.76 for ten fuze types under SNRs of-10 d B and above,which is higher than that of other typical algorithms.The trained SVM classification model achieves an average recognition accuracy of more than 96%on seven modulation types and recognition accuracies of more than 94%on each modulation type under SNRs of-12 d B and above,which represents a good AMR performance of radio fuzes under low SNRs.

Systemic modulation of skeletal mineralization by magnesium implant promoting fracture healing: Radiological exploration enhanced with PCA-based machine learning in a rat femoral model

The clinical application of magnesium(Mg)and its alloys for bone fractures has been well supported by in vitro and in vivo trials.However,there were studies indicating negative effects of high dose Mg intake and sustained local release of Mg ions on bone metabolism or repair,which should not be ignored when developing Mg-based implants.Thus,it remains necessary to assess the biological effects of Mg implants in animal models relevant to clinical treatment modalities.The primary purpose of this study was to validate the beneficial effects of intramedullary Mg implants on the healing outcome of femoral fractures in a modified rat model.In addition,the mineralization parameters at multiple anatomical sites were evaluated,to investigate their association with healing outcome and potential clinical applications.Compared to the control group without Mg implantation,postoperative imaging at week 12 demonstrated better healing outcomes in the Mg group,with more stable unions in 3D analysis and high-mineralized bridging in 2D evaluation.The bone tissue mineral density(TMD)was higher in the Mg group at the non-operated femur and lumbar vertebra,while no differences between groups were identified regarding the bone tissue volume(TV),TMD and bone mineral content(BMC)in humerus.In the surgical femur,the Mg group presented higher TMD,but lower TV and BMC in the distal metaphyseal region,as well as reduced BMC at the osteotomy site.Principal component analysis(PCA)-based machine learning revealed that by selecting clinically relevant parameters,radiological markers could be constructed for differentiation of healing outcomes,with better performance than 2D scoring.The study provides insights and preclinical evidence for the rational investigation of bioactive materials,the identification of potential adverse effects,and the promotion of diagnostic capabilities for fracture healing.

Recent Advance in Synaptic Plasticity Modulation Techniques for Neuromorphic Applications

Manipulating the expression of synaptic plasticity of neuromorphic devices provides fascinating opportunities to develop hardware platforms for artifi-cial intelligence.However,great efforts have been devoted to exploring biomimetic mechanisms of plasticity simulation in the last few years.Recent progress in various plasticity modulation techniques has pushed the research of synaptic electronics from static plasticity simulation to dynamic plasticity modulation,improving the accuracy of neuromorphic computing and providing strategies for implementing neuromorphic sensing functions.Herein,several fascinating strategies for synap-tic plasticity modulation through chemical techniques,device structure design,and physical signal sensing are reviewed.For chemical techniques,the underly-ing mechanisms for the modification of functional materials were clarified and its effect on the expression of synaptic plasticity was also highlighted.Based on device structure design,the reconfigurable operation of neuromorphic devices was well demonstrated to achieve programmable neuromorphic functions.Besides,integrating the sensory units with neuromorphic processing circuits paved a new way to achieve human-like intelligent perception under the modulation of physical signals such as light,strain,and temperature.Finally,considering that the relevant technology is still in the basic exploration stage,some prospects or development suggestions are put forward to promote the development of neuromorphic devices.

A High Resolution Convolutional Neural Network with Squeeze and Excitation Module for Automatic Modulation Classification

Automatic modulation classification(AMC) technology is one of the cutting-edge technologies in cognitive radio communications. AMC based on deep learning has recently attracted much attention due to its superior performances in classification accuracy and robustness. In this paper, we propose a novel, high resolution and multi-scale feature fusion convolutional neural network model with a squeeze-excitation block, referred to as HRSENet,to classify different kinds of modulation signals.The proposed model establishes a parallel computing mechanism of multi-resolution feature maps through the multi-layer convolution operation, which effectively reduces the information loss caused by downsampling convolution. Moreover, through dense skipconnecting at the same resolution and up-sampling or down-sampling connection at different resolutions, the low resolution representation of the deep feature maps and the high resolution representation of the shallow feature maps are simultaneously extracted and fully integrated, which is benificial to mine signal multilevel features. Finally, the feature squeeze and excitation module embedded in the decoder is used to adjust the response weights between channels, further improving classification accuracy of proposed model.The proposed HRSENet significantly outperforms existing methods in terms of classification accuracy on the public dataset “Over the Air” in signal-to-noise(SNR) ranging from-2dB to 20dB. The classification accuracy in the proposed model achieves 85.36% and97.30% at 4dB and 10dB, respectively, with the improvement by 9.71% and 5.82% compared to LWNet.Furthermore, the model also has a moderate computation complexity compared with several state-of-the-art methods.

Spatial Coded Modulation:Coding over Antennas

In spatial modulation systems,the reliability of the active antenna detection is of vital importance since the modulated symbols tend to be correctly demodulated when the active antennas are accurately identified.In this paper,we propose a spatial coded modulation(SCM)scheme,which improves the accuracy of the active antenna detection by coding over the transmit antennas.Specifically,the antenna activation pattern in the SCM corresponds to a codeword in a properly designed codebook with a larger minimum Hamming distance than the conventional spatial modulation.As the minimum Hamming distance increases,the reliability of the active antenna detection is directly enhanced,which yields a better system reliability.In addition to the reliability,the proposed SCM scheme also achieves a higher capacity with the identical antenna configuration compared to the conventional counterpart.The optimal maximum likelihood detector is first formulated.Then,a low-complexity suboptimal detector is proposed to reduce the computational complexity.Theoretical derivations of the channel capacity and the bit error rate are presented in various channel scenarios.Further derivation on performance bounding is also provided to reveal the insight of the benefit of increasing the minimum Hamming distance.Numerical results validate the analysis and demonstrate that the proposed SCM outperforms the conventional spatial modulation techniques in both channel capacity and system reliability.

Modulation of Electronic States in Bimetallic-doped Nitrogen-Carbon Based Nanoparticles for Enhanced Oxygen Reduction Kinetics

Controlling the local electronic structure of active ingredients to improve the adsorption desorption characteristics of oxygen-containing intermediates over the electrochemical liquid-solid interfaces is a critical challenge in the field of oxygen reduction reaction(ORR)catalysis.Here,we offer a simple approach for modulating the electronic states of metal nanocrystals by bimetal co-doping into carbon-nitrogen substrate,allowing us to modulate the electronic structure of catalytic active centers.To test our strategy,we designed a typical bimetallic nanoparticle catalyst(Fe-Co NP/NC)to flexibly alter the reaction kinetics of ORR.Our results from synchrotron Xray absorption spectroscopy and X-ray photoelectron spectroscopy showed that the co-doping of iron and cobalt could optimize the intrinsic charge distribution of Fe-Co NP/NC catalyst,promoting the oxygen reduction kinetics and ultimately achieving remarkable ORR activity.Consequently,the carefully designed Fe-Co NP/NC exhibits an ultra-high kinetic current density at the operating voltage(71.94 mA/cm^(2)at 0.80 V),and the half-wave potential achieves 0.915 V,which is obviously better than that of the corresponding controls including Fe NP/NC,Co NP/NC.Our findings provide a unique perspective for optimizing the electronic structure of active centers to achieve higher ORR catalytic activity and faster kinetics.

Interfacial Electronic Modulation of Dual-Monodispersed Pt–Ni_(3)S_(2) as Efficacious Bi-Functional Electrocatalysts for Concurrent H_(2) Evolution and Methanol Selective Oxidation

Constructing the efficacious and applicable bifunctional electrocatalysts and establishing out the mechanisms of organic electro-oxidation by replacing anodic oxygen evolution reaction(OER) are critical to the development of electrochemicallydriven technologies for efficient hydrogen production and avoid CO_(2) emission. Herein, the hetero-nanocrystals between monodispersed Pt(~ 2 nm) and Ni_(3)S_(2)(~ 9.6 nm) are constructed as active electrocatalysts through interfacial electronic modulation, which exhibit superior bi-functional activities for methanol selective oxidation and H_(2) generation. The experimental and theoretical studies reveal that the asymmetrical charge distribution at Pt–Ni_(3)S_(2) could be modulated by the electronic interaction at the interface of dual-monodispersed heterojunctions, which thus promote the adsorption/desorption of the chemical intermediates at the interface. As a result, the selective conversion from CH_(3)OH to formate is accomplished at very low potentials(1.45 V) to attain 100 m A cm^(-2) with high electronic utilization rate(~ 98%) and without CO_(2) emission. Meanwhile, the Pt–Ni_(3)S_(2) can simultaneously exhibit a broad potential window with outstanding stability and large current densities for hydrogen evolution reaction(HER) at the cathode. Further, the excellent bi-functional performance is also indicated in the coupled methanol oxidation reaction(MOR)//HER reactor by only requiring a cell voltage of 1.60 V to achieve a current density of 50 m A cm^(-2) with good reusability.

Pit density reduction for AlN epilayers grown by molecular beam epitaxy using Al modulation method

We have investigated homoepitaxy of AlN films grown by molecular beam epitaxy on AlN/sapphire templates byadopting both the continuous growth method and the Al modulation epitaxy(AME)growth method.The continuous growthmethod encounters significant challenges in controlling the growth mode.As the precise Al/N=1.0 ratio is difficult toachieve,either the excessive Al-rich or N-rich growth mode occurs.In contrast,by adopting the AME growth method,sucha difficulty has been effectively overcome.By manipulating the supply time of the Al and nitrogen sources,we were able toproduce AlN films with much improved surface morphology.The first step of the AME method,only supplying Al atoms,is important to wet the surface and the Al adatoms can act as a surfactant.Optimization of the initial Al supply time caneffectively reduce the pit density on the grown AlN surface.The pits density dropped from 12 pits/μm^(2)to 1 pit/μm^(2)andthe surface roughness reduced from 0.72 nm to 0.3 nm in a 2×2μm^(2)area for the AME AlN film homoepitaxially grownon an AlN template.

A Graph-Based Semi-Supervised Approach for Few-Shot Class-Incremental Modulation Classification

With the successive application of deep learning(DL)in classification tasks,the DL-based modulation classification method has become the preference for its state-of-the-art performance.Nevertheless,once the DL recognition model is pre-trained with fixed classes,the pre-trained model tends to predict incorrect results when identifying incremental classes.Moreover,the incremental classes are usually emergent without label information or only a few labeled samples of incremental classes can be obtained.In this context,we propose a graphbased semi-supervised approach to address the fewshot classes-incremental(FSCI)modulation classification problem.Our proposed method is a twostage learning method,specifically,a warm-up model is trained for classifying old classes and incremental classes,where the unlabeled samples of incremental classes are uniformly labeled with the same label to alleviate the damage of the class imbalance problem.Then the warm-up model is regarded as a feature extractor for constructing a similar graph to connect labeled samples and unlabeled samples,and the label propagation algorithm is adopted to propagate the label information from labeled nodes to unlabeled nodes in the graph to achieve the purpose of incremental classes recognition.Simulation results prove that the proposed method is superior to other finetuning methods and retrain methods.

Nano-Au-decorated hierarchical porous cobalt sulfide derived from ZIF-67 toward optimized oxygen evolution catalysis:Important roles of microstructures and electronic modulation

Enhancing both the number of active sites available and the intrinsic activity of Co-based electrocatalysts simultaneously is a desirable goal.Herein,a ZIF-67-derived hierarchical porous cobalt sulfide decorated by Au nanoparticles(NPs)(denoted as HP-Au@CoxSy@ZIF-67)hybrid is synthesized by low-temperature sulfuration treatment.The well-defined macroporous-mesoporous-microporous structure is obtained based on the combination of polystyrene spheres,as-formed CoxSy nanosheets,and ZIF-67 frameworks.This novel three-dimensional hierarchical structure significantly enlarges the three-phase interfaces,accelerating the mass transfer and exposing the active centers for oxygen evolution reaction.The electronic structure of Co is modulated by Au through charge transfer,and a series of experiments,together with theoretical analysis,is performed to ascertain the electronic modulation of Co by Au.Meanwhile,HP-Au@CoxSy@ZIF-67 catalysts with different amounts of Au were synthesized,wherein Au and NaBH4 reductant result in an interesting“competition effect”to regulate the relative ratio of Co^(2+)/Co^(3+),and moderate Au assists the electrochemical performance to reach the highest value.Consequently,the optimized HP-Au@CoxSy@ZIF-67 exhibits a low overpotential of 340 mV at 10 mA cm^(-2)and a Tafel slope of 42 mV dec-1 for OER in 0.1 M aqueous KOH,enabling efficient water splitting and Zn-air battery performance.The work here highlights the pivotal roles of both microstructural and electronic modulation in enhancing electrocatalytic activity and presents a feasible strategy for designing and optimizing advanced electrocatalysts.

Microwave electrometry with Rydberg atoms in a vapor cell using microwave amplitude modulation

We have theoretically and experimentally studied the dispersive signal of the Rydberg atomic electromagneticallyinduced transparency(EIT)Autler–Townes(AT)splitting spectra obtained using amplitude modulation of the microwave(MW)electric field.In addition to the two zero-crossing points interval△f_(zeros),the dispersion signal has two positive maxima with an interval defined as the shoulder interval△f_(sho),which is theoretically expected to be used to measure a much weaker MW electric field.The relationship of the MW field strength E_(MW)and△f_(sho)is experimentally studied at the MW frequencies of 31.6 GHz and 9.2 GHz respectively.The results show that△f_(sho)can be used to characterize the much weaker E_(MW)than that of△f_(zeros)and the traditional EIT–AT splitting interval△f_(m);the minimum E_(MW)measured by△f_(sho)is about 30 times smaller than that by△f_(m).As an example,the minimum E_(MW)at 9.2 GHz that can be characterized by△f_(sho)is 0.056 mV/cm,which is the minimum value characterized by the frequency interval using a vapor cell without adding any auxiliary fields.The proposed method can improve the weak limit and sensitivity of E_(MW)measured by the spectral frequency interval,which is important in the direct measurement of weak E_(MW).

OFDM with Generalized Cascade Index Modulation for Secure Communications

Cascade index modulation(CIM) is a recently proposed improvement of orthogonal frequency division multiplexing with index modulation(OFDM-IM) and achieves better error performance.In CIM, at least two different IM operations construct a super IM operation or achieve new functionality. First, we propose a OFDM with generalized CIM(OFDM-GCIM) scheme to achieve a joint IM of subcarrier selection and multiple-mode(MM)permutations by using a multilevel digital algorithm.Then, two schemes, called double CIM(D-CIM) and multiple-layer CIM(M-CIM), are proposed for secure communication, which combine new IM operation for disrupting the original order of bits and symbols with conventional OFDM-IM, to protect the legitimate users from eavesdropping in the wireless communications. A subcarrier-wise maximum likelihood(ML) detector and a low complexity log-likelihood ratio(LLR) detector are proposed for the legitimate users. A tight upper bound on the bit error rate(BER) of the proposed OFDM-GCIM, D-CIM and MCIM at the legitimate users are derived in closed form by employing the ML criteria detection. Computer simulations and numerical results show that the proposed OFDM-GCIM achieves superior error performance than OFDM-IM, and the error performance at the eavesdroppers demonstrates the security of D-CIM and M-CIM.

Single Photon Detection Technology in Underwater Wireless Optical Communication:Modulation Modes and Error Correction Coding Analysis

This study explores the application of single photon detection(SPD)technology in underwater wireless optical communication(UWOC)and analyzes the influence of different modulation modes and error correction coding types on communication performance.The study investigates the impact of on-off keying(OOK)and 2-pulse-position modulation(2-PPM)on the bit error rate(BER)in single-channel intensity and polarization multiplexing.Furthermore,it compares the error correction performance of low-density parity check(LDPC)and Reed-Solomon(RS)codes across different error correction coding types.The effects of unscattered photon ratio and depolarization ratio on BER are also verified.Finally,a UWOC system based on SPD is constructed,achieving 14.58 Mbps with polarization OOK multiplexing modulation and 4.37 Mbps with polarization 2-PPM multiplexing modulation using LDPC code error correction.

Security-Enhanced Directional Modulation Based on Two-Dimensional M-WFRFT

Directional modulation(DM)is one of the most promising secure communication techniques.However,when the eavesdropper is co-located with the legitimate receiver,the conventional DM has the disadvantages of weak anti-scanning capability,anti-deciphering capability,and low secrecy rate.In response to these problems,we propose a twodimensional multi-term weighted fractional Fourier transform aided DM scheme,in which the legitimate receiver and the transmitter use different transform terms and transform orders to encrypt and decrypt the confidential information.In order to further lower the probability of being deciphered by an eavesdropper,we use the subblock partition method to convert the one-dimensional modulated signal vector into a twodimensional signal matrix,increasing the confusion of the useful information.Numerical results demonstrate that the proposed DM scheme not only provides stronger anti-deciphering and anti-scanning capabilities but also improves the secrecy rate performance of the system.

Automatic modulation recognition of radiation source signals based on two-dimensional data matrix and improved residual neural network

Automatic modulation recognition(AMR)of radiation source signals is a research focus in the field of cognitive radio.However,the AMR of radiation source signals at low SNRs still faces a great challenge.Therefore,the AMR method of radiation source signals based on two-dimensional data matrix and improved residual neural network is proposed in this paper.First,the time series of the radiation source signals are reconstructed into two-dimensional data matrix,which greatly simplifies the signal preprocessing process.Second,the depthwise convolution and large-size convolutional kernels based residual neural network(DLRNet)is proposed to improve the feature extraction capability of the AMR model.Finally,the model performs feature extraction and classification on the two-dimensional data matrix to obtain the recognition vector that represents the signal modulation type.Theoretical analysis and simulation results show that the AMR method based on two-dimensional data matrix and improved residual network can significantly improve the accuracy of the AMR method.The recognition accuracy of the proposed method maintains a high level greater than 90% even at -14 dB SNR.

Deep Learning Based Signal Detection for Quadrature Spatial Modulation System

With the development of communication systems, modulation methods are becoming more and more diverse. Among them, quadrature spatial modulation(QSM) is considered as one method with less capacity and high efficiency. In QSM, the traditional signal detection methods sometimes are unable to meet the actual requirement of low complexity of the system. Therefore, this paper proposes a signal detection scheme for QSM systems using deep learning to solve the complexity problem. Results from the simulations show that the bit error rate performance of the proposed deep learning-based detector is better than that of the zero-forcing(ZF) and minimum mean square error(MMSE) detectors, and similar to the maximum likelihood(ML) detector. Moreover, the proposed method requires less processing time than ZF, MMSE,and ML.