Adaptive support vector machine decision feedback equalizer

An adaptive support vector machine decision feedback equalizer（ASVM-DFE） based on the least square support vector machine（LS-SVM） is proposed,it solves linear system iteratively with less computational intensity.An adaptive non-singleton fuzzy support vector machine decision feedback equalizer（ANSFSVMDFE） is also presented,it adopts the non-singleton fuzzy Gaussian kernel function with similar characteristic of pre-filter and is modified with a space transformation based approach.Simulations under nonlinear time variant channels show that ASVM-DFE and ANSFSVM-DFE perform very well on nonlinear equalization and ANSFSVM-DFE acts especially well in resisting abrupt interference.

Decision feedback equalizer based on non-singleton fuzzy regular neural networks

A new equalization method is proposed in this paper for severely nonlinear distorted channels. The structure of decision feedback is adopted for the non-singleton fuzzy regular neural network that is trained by gradient-descent algorithm. The model shows a much better performance on anti-jamming and nonlinear classification, and simulation is carried out to compare this method with other nonlinear channel equalization methods. The results show the method has the least bit error rate （BER）.

BER ANALYSIS OF A GMSK SYSTEM USING DECISION FEEDBACK

There are considerable literatures on the Bit Error Rate(BER)evaluation of DifferentialDetection of Gaussian Minimum Shift Keying(DDGMSK)system using Decision Feedback(DF),butmost of them give the performance based on the Monte Carlo Error Counting(MCEC)technique.Fromthe probability distribution of the phase angle between two vectors perturbed by Gaussian noises,theformulae of BER are derived for the performance analysis of DDGMSK system with DF in this letter.Considering the m-bit dock-tailed sequence,the new formulae of Gaussian Minimum Shift Keying(GMSK)modulated phase and the Time-Varying Signal to Noise Ratio(TVSNR)of the demodulatedsignal are presented,and it is proved that the relationship between the TVSNR of the demodulatedsignal and the size of eye opening is not inevitable.Simulation results show that the theoretical in-vestigation gives analogous results with the MCEC technique.The formulae presented are useful for theperformance analysis of systems using GMSK as modulating and demodulating method,for instance,the analysis of synchronous performance of frequency-hopping communication system.

Design of a 6.25 Gbps backplane SerDes with adaptive decision feedback equalization

A 6.25 Gbps SerDes core used in the high signed based on the OIF-CEI-02.0 standard. To speed backplane communication receiver has been decounteract the serious Inter-Syrmbol-Interference （ISI）, the core employed a half-rate four-tap decision feedback equalizer （DFE）. The equalizer used the Signsign least mean-squared （SS-LMS） algorithm to realize the coefficient adaptation. An automatic gain control （AGC） amplifier with the sign least mean-squared （S-LMS） algorithm has been used to compensate the transmission media loss. To recover the clock signal from the input data serial and provide for the DFE and AGC, a bang-bang clock recovery （BB-CR） is adopted. A third order phase loop loek （PLL） model was proposed to predict characteristics of the BB-CR. The core has been verified by behavioral modeling in MATLAB. The results indicate that the core can meet the specifications of the backplane receiver. The DFE recovered data over a 34＂ FR-4 backplane has a peak-to-peak jitter of 17 ps, a horizontal eye opening of 0.87 UI, and a vertical eye opening of 500 mVpp.

A DECISION FEEDBACK EQUALIZER FOR NON-LINEAR CHANNELS

A Simple and useful decision feedback equalizer used for non-linear channels with severe linear distortion and mild non-linear distortion is proposed. It is a combination of a nonlinear channel equalizer based on connectionist model and a common decision feedback equalizer for linear channels. For a typical non-linear channel model it is shown that the equalization performances of the proposed equalizer are improved significantly.

A Robust Decision Feedback Equalizer for ATSC DTV Receivers

Least mean square (LMS) decision feedback equalizer (DFE) is preferred as an effective solution to coping with inter-symbol interference (ISI) for ATSC digital television (DTV) receivers. In DTV transmission environment, echo delay often covers several hundreds symbols, which leads to very large-scale equalizer. One consequence of the large-scale equalizer is the very slow convergence, which combined with error propagation, inherent drawback of DFE, seriously deteriorates the performance of the receivers, especially in severe channels More working modes and corresponding robust control mechanism were given to help the equalizer converge to the stable state smoothly. Simulation results show that the improved equalizer can perform better, especially in the severe channels.

EFFICIENT CYCLIC PREFIX RECONSTRUCTION USING DECISION FEEDBACK FILTER FOR CODED SINGLE-CARRIER SYSTEMS WITH FREQUENCY-DOMAIN EQUALIZATION (SC-FDE)

In this paper, an efficient Cyclic Prefix （CP） reconstruction scheme is proposed for Single-Carrier systems with Frequency-Domain Equalization （SC-FDE） that employ insufficient length of CP at the transmitter. By utilizing a decision feedback filter to cancel the residual InterSymbol Interference （ISI） in the equalized signal, the proposed scheme can effectively lower the low bound of performance for the CP reconstruction schemes and can greatly improve the Bit Error P^te （BER） performance of SC-FDE systems. In addition, the existing methods and the proposed scheme are also optimized. It is shown in the simulation results that, when the Signal-to-Noise Ratio （SNR） exceeds a certain threshold, the proposed scheme can achieve the low bound of performance for the existing methods. Moreover, by increasing the number of iteration or through optimization, the low bound can be outperformed.

Least Squares Training LMS Decision Feedback Equaliser for Terrestrial HDTV Broadcasting

In this paper, an LMS decision feedback equaliser (DFE) with recursive least squares (RLS) algorithm used in training period is proposed for terrestrial HDTV broadcasting. The RLS is implemented in a non real time manner, rather than real time, to drastically reduce computational requirement for hardware realization. The only penalty paid is an acceptable or tolerable small time delay. Simulation results show that this equaliser provides 3.0 dB signal to noise ratio (SNR) improvement at a BER of 3.0×10 -6 with respect to the conventional LMS DFE suggested by Grand Alliance.

ON DECORRELATING MULTIUSER SIGNAL DETECTORS WITH DECISION FEEDBACK FOR DS-SSMA CHANNELS

The main shortcomings of direct sequence spread spectrum multiple ac-cess(DS-SSMA)communication systems are the near-far effect and multiaccess in-terferences,which impair the stability,capacity and application areas of the commu-nication systems.In this paper,a new kind of multiuser detector——decorrelatingdetector with decision feedback is proposed.In linear channels,this detector can e-liminate the multiaccess interferences with low complexity.Computer simulationsverify the theoretical analysis in this paper.

Multi-Branch Fractional Multi-Bit Differential Detection of Continuous Phase Modulation with Decision Feedback

Differential detection of continuous phase modulation suffers from significant intersymbol interference. To reduce bit error rate, multi-branch fractional multi-bit differential detection (MFMDD) with decision feed-back is proposed. By introducing decision feedback in multi-bit differential detected signals, severe inter-symbol interference can be removed. Simulation results show that the proposed structure can greatly im-proves the performance compared with MFMDD without decision feedback, and the performance of 9 FMDD is very near to the performance of the coherent detection.

Stress-assisted corrosion mechanism of 3Ni steel by using gradient boosting decision tree machining learning method

Traditional 3Ni weathering steel cannot completely meet the requirements for offshore engineering development,resulting in the design of novel 3Ni steel with the addition of microalloy elements such as Mn or Nb for strength enhancement becoming a trend.The stress-assisted corrosion behavior of a novel designed high-strength 3Ni steel was investigated in the current study using the corrosion big data method.The information on the corrosion process was recorded using the galvanic corrosion current monitoring method.The gradi-ent boosting decision tree(GBDT)machine learning method was used to mine the corrosion mechanism,and the importance of the struc-ture factor was investigated.Field exposure tests were conducted to verify the calculated results using the GBDT method.Results indic-ated that the GBDT method can be effectively used to study the influence of structural factors on the corrosion process of 3Ni steel.Dif-ferent mechanisms for the addition of Mn and Cu to the stress-assisted corrosion of 3Ni steel suggested that Mn and Cu have no obvious effect on the corrosion rate of non-stressed 3Ni steel during the early stage of corrosion.When the corrosion reached a stable state,the in-crease in Mn element content increased the corrosion rate of 3Ni steel,while Cu reduced this rate.In the presence of stress,the increase in Mn element content and Cu addition can inhibit the corrosion process.The corrosion law of outdoor-exposed 3Ni steel is consistent with the law based on corrosion big data technology,verifying the reliability of the big data evaluation method and data prediction model selection.

Deep learning for joint channel estimation and feedback in massive MIMO systems

The great potentials of massive Multiple-Input Multiple-Output(MIMO)in Frequency Division Duplex(FDD)mode can be fully exploited when the downlink Channel State Information(CSI)is available at base stations.However,the accurate CsI is difficult to obtain due to the large amount of feedback overhead caused by massive antennas.In this paper,we propose a deep learning based joint channel estimation and feedback framework,which comprehensively realizes the estimation,compression,and reconstruction of downlink channels in FDD massive MIMO systems.Two networks are constructed to perform estimation and feedback explicitly and implicitly.The explicit network adopts a multi-Signal-to-Noise-Ratios(SNRs)technique to obtain a single trained channel estimation subnet that works well with different SNRs and employs a deep residual network to reconstruct the channels,while the implicit network directly compresses pilots and sends them back to reduce network parameters.Quantization module is also designed to generate data-bearing bitstreams.Simulation results show that the two proposed networks exhibit excellent performance of reconstruction and are robust to different environments and quantization errors.

Cognitive interference decision method for air defense missile fuze based on reinforcement learning

To solve the problem of the low interference success rate of air defense missile radio fuzes due to the unified interference form of the traditional fuze interference system,an interference decision method based Q-learning algorithm is proposed.First,dividing the distance between the missile and the target into multiple states to increase the quantity of state spaces.Second,a multidimensional motion space is utilized,and the search range of which changes with the distance of the projectile,to select parameters and minimize the amount of ineffective interference parameters.The interference effect is determined by detecting whether the fuze signal disappears.Finally,a weighted reward function is used to determine the reward value based on the range state,output power,and parameter quantity information of the interference form.The effectiveness of the proposed method in selecting the range of motion space parameters and designing the discrimination degree of the reward function has been verified through offline experiments involving full-range missile rendezvous.The optimal interference form for each distance state has been obtained.Compared with the single-interference decision method,the proposed decision method can effectively improve the success rate of interference.

Attribute Reduction of Hybrid Decision Information Systems Based on Fuzzy Conditional Information Entropy

The presence of numerous uncertainties in hybrid decision information systems(HDISs)renders attribute reduction a formidable task.Currently available attribute reduction algorithms,including those based on Pawlak attribute importance,Skowron discernibility matrix,and information entropy,struggle to effectively manages multiple uncertainties simultaneously in HDISs like the precise measurement of disparities between nominal attribute values,and attributes with fuzzy boundaries and abnormal values.In order to address the aforementioned issues,this paper delves into the study of attribute reduction withinHDISs.First of all,a novel metric based on the decision attribute is introduced to solve the problem of accurately measuring the differences between nominal attribute values.The newly introduced distance metric has been christened the supervised distance that can effectively quantify the differences between the nominal attribute values.Then,based on the newly developed metric,a novel fuzzy relationship is defined from the perspective of“feedback on parity of attribute values to attribute sets”.This new fuzzy relationship serves as a valuable tool in addressing the challenges posed by abnormal attribute values.Furthermore,leveraging the newly introduced fuzzy relationship,the fuzzy conditional information entropy is defined as a solution to the challenges posed by fuzzy attributes.It effectively quantifies the uncertainty associated with fuzzy attribute values,thereby providing a robust framework for handling fuzzy information in hybrid information systems.Finally,an algorithm for attribute reduction utilizing the fuzzy conditional information entropy is presented.The experimental results on 12 datasets show that the average reduction rate of our algorithm reaches 84.04%,and the classification accuracy is improved by 3.91%compared to the original dataset,and by an average of 11.25%compared to the other 9 state-of-the-art reduction algorithms.The comprehensive analysis of these research results clearly indicates that our algorithm is highly effective in managing the intricate uncertainties inherent in hybrid data.

A Large-Scale Group Decision Making Model Based on Trust Relationship and Social Network Updating

With the development of big data and social computing,large-scale group decisionmaking(LGDM)is nowmerging with social networks.Using social network analysis(SNA),this study proposes an LGDM consensus model that considers the trust relationship among decisionmakers(DMs).In the process of consensusmeasurement:the social network is constructed according to the social relationship among DMs,and the Louvain method is introduced to classify social networks to form subgroups.In this study,the weights of each decision maker and each subgroup are computed by comprehensive network weights and trust weights.In the process of consensus improvement:A feedback mechanism with four identification and two direction rules is designed to guide the consensus of the improvement process.Based on the trust relationship among DMs,the preferences are modified,and the corresponding social network is updated to accelerate the consensus.Compared with the previous research,the proposedmodel not only allows the subgroups to be reconstructed and updated during the adjustment process,but also improves the accuracy of the adjustment by the feedbackmechanism.Finally,an example analysis is conducted to verify the effectiveness and flexibility of the proposed method.Moreover,compared with previous studies,the superiority of the proposed method in solving the LGDM problem is highlighted.

Adaptive Optimal Discrete-Time Output-Feedback Using an Internal Model Principle and Adaptive Dynamic Programming

In order to address the output feedback issue for linear discrete-time systems, this work suggests a brand-new adaptive dynamic programming(ADP) technique based on the internal model principle(IMP). The proposed method, termed as IMP-ADP, does not require complete state feedback-merely the measurement of input and output data. More specifically, based on the IMP, the output control problem can first be converted into a stabilization problem. We then design an observer to reproduce the full state of the system by measuring the inputs and outputs. Moreover, this technique includes both a policy iteration algorithm and a value iteration algorithm to determine the optimal feedback gain without using a dynamic system model. It is important that with this concept one does not need to solve the regulator equation. Finally, this control method was tested on an inverter system of grid-connected LCLs to demonstrate that the proposed method provides the desired performance in terms of both tracking and disturbance rejection.

Attribute Reduction Method Based on Sequential Three-Branch Decision Model

Attribute reduction is a research hotspot in rough set theory. Traditional heuristic attribute reduction methods add the most important attribute to the decision attribute set each time, resulting in multiple redundant attribute calculations, high time consumption, and low reduction efficiency. In this paper, based on the idea of sequential three-branch decision classification domain, attributes are treated as objects of three-branch division, and attributes are divided into core attributes, relatively necessary attributes, and unnecessary attributes using attribute importance and thresholds. Core attributes are added to the decision attribute set, unnecessary attributes are rejected from being added, and relatively necessary attributes are repeatedly divided until the reduction result is obtained. Experiments were conducted on 8 groups of UCI datasets, and the results show that, compared to traditional reduction methods, the method proposed in this paper can effectively reduce time consumption while ensuring classification performance.

Method for triangular fuzzy multiple attribute decision making based on two-dimensional density operator method

Aiming at the triangular fuzzy(TF)multi-attribute decision making(MADM)problem with a preference for the distribution density of attribute(DDA),a decision making method with TF number two-dimensional density(TFTD)operator is proposed based on the density operator theory for the decision maker(DM).Firstly,a simple TF vector clustering method is proposed,which considers the feature of TF number and the geometric distance of vectors.Secondly,the least deviation sum of squares method is used in the program model to obtain the density weight vector.Then,two TFTD operators are defined,and the MADM method based on the TFTD operator is proposed.Finally,a numerical example is given to illustrate the superiority of this method,which can not only solve the TF MADM problem with a preference for the DDA but also help the DM make an overall comparison.

AI Enlightens Wireless Communication:A Transformer Backbone for CSI Feedback

This paper is based on the background of the 2nd Wireless Communication Artificial Intelligence(AI)Competition(WAIC)which is hosted by IMT-2020(5G)Promotion Group 5G+AIWork Group,where the framework of the eigenvector-based channel state information(CSI)feedback problem is firstly provided.Then a basic Transformer backbone for CSI feedback referred to EVCsiNet-T is proposed.Moreover,a series of potential enhancements for deep learning based(DL-based)CSI feedback including i)data augmentation,ii)loss function design,iii)training strategy,and iv)model ensemble are introduced.The experimental results involving the comparison between EVCsiNet-T and traditional codebook methods over different channels are further provided,which show the advanced performance and a promising prospect of Transformer on DL-based CSI feedback problem.

Fuel recycling feedback control via real-time boron powder injection in EAST with full metal wall

A feedback control of fuel recycling via real-time boron powder injection,addressing the issue of continuously increasing recycling in long-pulse plasma discharges,has been successfully developed and implemented on EAST tokamak.The feedback control system includes four main parts:the impurity powder dropper(IPD),a diagnostic system measuring fuel recycling level represented by D_(α)emission,a plasma control system(PCS)implementing the Proportional Integral Derivative(PID)algorithm,and a signal converter connecting the IPD and PCS.Based on this control system,both active control and feedback control experiments have recently been performed on EAST with a full metal wall.The experimental results show that the fuel recycling can be gradually reduced to lower level as PCS control voltage increases.In the feedback control experiments,it is also observed that the D_(α)emission is reduced to the level below the target D_(α)value by adjusting boron injection flow rate,indicating successful implementation of the fuel recycling feedback control on EAST.This technique provides a new method for fuel recycling control of long pulse and high parameter plasma operations in future fusion devices.