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）.

Adaptive Kernel Firefly Algorithm Based Feature Selection and Q-Learner Machine Learning Models in Cloud

CC’s(Cloud Computing)networks are distributed and dynamic as signals appear/disappear or lose significance.MLTs(Machine learning Techniques)train datasets which sometime are inadequate in terms of sample for inferring information.A dynamic strategy,DevMLOps(Development Machine Learning Operations)used in automatic selections and tunings of MLTs result in significant performance differences.But,the scheme has many disadvantages including continuity in training,more samples and training time in feature selections and increased classification execution times.RFEs(Recursive Feature Eliminations)are computationally very expensive in its operations as it traverses through each feature without considering correlations between them.This problem can be overcome by the use of Wrappers as they select better features by accounting for test and train datasets.The aim of this paper is to use DevQLMLOps for automated tuning and selections based on orchestrations and messaging between containers.The proposed AKFA(Adaptive Kernel Firefly Algorithm)is for selecting features for CNM(Cloud Network Monitoring)operations.AKFA methodology is demonstrated using CNSD(Cloud Network Security Dataset)with satisfactory results in the performance metrics like precision,recall,F-measure and accuracy used.

FUNDAMENTALS OF THE ANALYTIC NETWORK PROCESS- DEPENDENCE AND FEEDBACK IN DECISION-MAKING WITH A SINGLE NETWORK

The Analytic Network Process (ANP) is a multicriteria theory of measurement used to derive relative priority scales of absolute numbers from individual judgments (or from actual measurements normalized to a relative form) that also belong to a fundamental scale of absolute numbers. These judgments represent the relative influence, of one of two elements over the other in a pairwise comparison process on a third element in the system, with respect to an underlying control criterion. Through its supermatrix, whose entries are themselves matrices of column priorities, the ANP synthesizes the outcome of dependence and feedback within and between clusters of elements. The Analytic Hierarchy Process (AHP) with its independence assumptions on upper levels from lower levels and the independence of the elements in a level is a special case of the ANP. The ANP is an essential tool for articulating our understanding of a decision problem. One had to overcome the limitation of linear hierarchic structures and their mathematical consequences. This part on the ANP summarizes and illustrates the basic concepts of the ANP and shows how informed intuitive judgments can lead to real life answers that are matched by actual measurements in the real world (for example, relative dollar values) as illustrated in market share examples that rely on judgments and not on numerical data.

Frequency domain decision feedback equalizer for time-reversal space-time block coding

In this paper, a frequency domain decision feedback equalizer is proposed for single carrier transmission with time-reversal space-time block coding （TR-STBC）. It is shown that the diagonal decision feedback equalizer matrix can be calculated from the frequency domain channel response. Under the perfect feedback assumption, the proposed equalizer can approach matched filter bound （MFB）. Compared with the existing time domain decision feedback equalizer, the proposed equalizer exhibits better performance with the same equalization complexity.

LOW-POWER SURVIVOR MEMORY ARCHITECTURE FOR DFSE IN 1000BASE-T TRANSCEIVER

A novel approach to survivor memory unit of Decision Feedback Sequence Estimator(DFSE) for 1000BASE-T transceiver based on hybrid architecture of the classical register-exchange and trace-back methods is proposed.The proposed architecture is investigated with special emphasis on low power and small decoder latency,in which a dedicated register-exchange module is designed to provide tentative survivor symbols with zero latency,and a high-speed trace back logic is presented to meet the tight latency budget specified for 1000BASE-T transceiver.Furthermore,clock-gating register banks are constructed for power saving.VLSI implementation reveals that,the proposed architecture provides about 40% savings in power consumption compared to the traditional register-exchange architecture.

Fast Fading Channel Neural Equalization Using Levenberg-Marquardt Training Algorithm and Pulse Shaping Filters

Artificial Neural Network (ANN) equalizers have been successfully applied to mitigate Inter symbolic Interference (ISI) due to distortions introduced by linear or nonlinear communication channels. The ANN architecture is chosen according to the type of ISI produced by fixed, fast or slow fading channels. In this work, we propose a combination of two techniques in order to minimize ISI yield by fast fading channels, i.e., pulse shape filtering and ANN equalizer. Levenberg-Marquardt algorithm is used to update the synaptic weights of an ANN comprise only by two recurrent perceptrons. The proposed system outperformed more complex structures such as those based on Kalman filtering approach.

Enhancement in Channel Equalization Using Particle Swarm Optimization Techniques

This work proposes an improved inertia weight update method and position update method in Particle Swarm Optimization (PSO) to enhance the convergence and mean square error of channel equalizer. The search abilities of PSO are managed by the key parameter Inertia Weight (IW). A higher value leads to global search whereas a smaller value shifts the search to local which makes convergence faster. Different approaches are reported in literature to improve PSO by modifying inertia weight. This work investigates the performance of the existing PSO variants related to time varying inertia weight methods and proposes new strategies to improve the convergence and mean square error of channel equalizer. Also the position update method in PSO is modified to achieve better convergence in channel equalization. The simulation presents the enhanced performance of the proposed techniques in transversal and decision feedback models. The simulation results also analyze the superiority in linear and nonlinear channel conditions.

A 6.25 Gb/s equalizer in 0.18μm CMOS technology for high-speed SerDes

This paper presents a 0.18μm CMOS 6.25 Gb/s equalizer for high speed backplane communication. The proposed equalizer is a combined one consisting of a one-tap feed-forward equalizer （FFE） and a two-tap half-rate decision feedback equalizer （DFE） in order to cancel both pre-cursor and post-cursor ISI. By employing an active-inductive peaking circuit for the delay line, the bandwidth of the FFE is increased and the area cost is minimized. CML-based circuits such as DFFs, summers and multiplexes all help to improve the speed of DFEs. Measurement results illustrate that the equalizer operates well when equalizing 6.25 Gb/s data is passed over a 30-inch channel with a loss of 22 dB and consumes 55.8 mW with the supply voltage of 1.8 V. The overall chip area including pads is 0.3 × 0.5 mm^2.

A novel differential multiuser detection algorithm for multiuser MIMO-OFDM systems

We propose an efficient low bit error rate(BER) and low complexity multiple-input multiple-output(MIMO) multiuser detection(MUD) method for use with multiuser MIMO orthogonal frequency division multiplexing(OFDM) systems.It is a hybrid method combining a multiuser-interference-cancellation-based decision feedback equalizer using error feedback filter(MIMO MIC DFE-EFF) and a differential algorithm.The proposed method,termed 'MIMO MIC DFE-EFF with a differential algorithm' for short,has a multiuser feedback structure.We describe the schemes of MIMO MIC DFE-EFF and MIMO MIC DFE-EFF with a differential algorithm,and compare their minimum mean square error(MMSE) performance and computational complexity.Simulation results show that a significant performance gain can be achieved by employing the MIMO MIC DFE-EFF detection algorithm in the context of a multiuser MIMO-OFDM system over frequency selective Rayleigh channel.MIMO MIC DFE-EFF with the differential algorithm improves both computational efficiency and BER performance in a multistage structure relative to conventional DFE-EFF,though there is a small reduction in system performance compared with MIMO MIC DFE-EFF without the differential algorithm.

Neural FIR adaptive Laguerre equalizer with a gradient adaptive amplitude for nonlinear channel in communication systems

To mitigate the linear and nonlinear distortions in communication systems, two novel nonlinear adaptive equalizers are proposed on the basis of the neural finite impulse response （FIR） filter, decision feedback architecture and the characteristic of the Laguerre filter. They are neural FIR adaptive decision feedback equalizer （SNNDFE） and neural FIR adaptive Laguerre equalizer （LSNN）. Of these two equalizers, the latter is simple and with characteristics of both infinite impulse response （IIR） and FIR filters; it can use shorter memory length to obtain better performance. As confirmed by theoretical analysis, the novel LSNN equalizer is stable （0 〈α〈1）. Furthermore, simulation results show that the SNNDFE can get better equalized performance than SNN equalizer, while the latter exhibits better performance than others in terms of convergence speed, mean square error （MSE） and bit error rate （BER）. Therefore, it can reduce the input dimension and eliminate linear and nonlinear interference effectively. In addition, it is very suitable for hardware implementation due to its simple structure.