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.

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）.

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.

MLSE-assisted decision feedback equalizer for error-propagation suppression in high-speed IM-DD transmission systems

We propose a trellis-compressed maximum likelihood sequence estimation(TC-MLSE)-assisted sliding-block decision feedback equalizer(DFE)to suppress the error propagation resulting from the DFE in high-speed systems.We use an out-ofrange detector to detect the end of burst errors from the DFE and activate the optional TC-MLSE to correct burst errors.We conduct experiments to transmit a 201-Gbit/s PAM-8 signal.The results show that the proposed method achieves a bit error rate of 3.65×10^(-3),which is close to that of MLSE.The optional MLSE is only activated when needed and processes 11.4%of the total symbols.Moreover,the proposed method compresses the maximum length of burst errors from 19 to 5.

Application of novel super-exponential iteration algorithm in underwater acoustic channel

A novel variable step-size modified super-exponential iteration(MSEI)decision feedback blind equalization(DFE)algorithm with second-order digital phase-locked loop is put forward to improve the convergence performance of super-exponential iteration DFE algorithm.Based on the MSEI-DFE algorithm,it is first proposed to develop an error function as an improvement to the error function of MSEI,which effectively achieves faster convergence speed of the algorithm.Subsequently,a hyperbolic tangent function variable step-size algorithm is developed considering the high variation rate of the hyperbolic tangent function around zero,so as to further improve the convergence speed of the algorithm.In the end,a second-order digital phase-locked loop is introduced into the decision feedback equalizer to track and compensate for the phase rotation of equalizer input signals.For the multipath underwater acoustic channel with mixed phase and phase rotation,quadrature phase shift keying(QPSK)and 16 quadrature amplitude modulation(16QAM)modulated signals are used in the computer simulation of the algorithm in terms of convergence and carrier recovery performance.The results show that the proposed algorithm can considerably improve convergence speed and steady-state error,make effective compensation for phase rotation,and efficiently facilitate carrier recovery.

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.

Acoustic MIMO Communications in a Very Shallow Water Channel

Underwater acoustic channels pose a great difficulty for the development of high speed communication due to highly limited band-width as well as hostile multipath interference. Enlightened by rapid progress of multiple-input multiple-output （MIMO） technologies in wireless communication scenarios, MIMO systems offer a potential solution by enabling multiple spatially parallel communication channels to improve communication performance as well as capacity. For MIMO acoustic communications, deep sea channels offer substantial spatial diversity among multiple channels that can be exploited to address simultaneous multipath and co-channel interference. At the same time, there are increasing requirements for high speed underwater communication in very shallow water area （for example, a depth less than 10 m）. In this paper, a space-time multichannel adaptive receiver consisting of multiple decision feedback equalizers （DFE） is adopted as the receiver for a very shallow water MIMO acoustic communication system. The performance of multichannel DFE receivers with relatively small number of receiving elements are analyzed and compared with that of the multichannel time reversal receiver to evaluate the impact of limited spatial diversity on multi-channel equalization and time reversal processing. The results of sea trials in a very shallow water channel are presented to demonstrate the feasibility of very shallow water MIMO acoustic communication.

MIMO-DFE BASED SPACE-TIME RECEIVER OVER FREQUENCY SELECTIVE CHANNELS WITH LIMITED ERROR PROPAGATION

MIMO-DFE(Multiple-Input-Multiple-Output Decision Feedback Equalizer) based receiver architectures are researched recently to detect signals in BLAST(Bell laboratories LAyered Space-Time) over frequency-selective channels. Due to their recursive structure, these receivers may suffer from error propagation which results in an overall mean square error degradation. An MIMO-DFE based BLAST receiver with limited error propagation to combat frequencyselective channel is proposed, which employs both norm constraint on feedback filter taps and soft decision device. Simulation results show that the proposed receiver outperforms conventional ones in various frequency selective channels.

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.

SYMMETRIC INFORMATION RATE OF CPM AND OPTIMAL DETECTOR DESIGN

This paper first calculates numerically the Symmetric Information Rate (SIR) of Cotinuous Phase Modulation (CPM) schemes over Additive White Gaussian Noise (AWGN) channel, modeling CPM and channel as a Memory-less Modulator (MM) plus a Markov Finite State Channel (FSMC), then proposes an optimal detector, which is of BCJR based Decision Feedback Detector (DFD) but non-iterative. It can achieve the SIR as the sequence length approaches infinity while the complexity is extremely low. Finally, both theoretic proof and numerical simulation are performed to show the op- timality. Simulation results show that it nearly achieves the theoretic bound and outperforms the Viterbi Detector (VD) with 2dB in low Eb/N0.

A 10Gb/s combined equalizer in 0.18μm CMOS technology for backplane communication

This paper presents a lOGb/s highspeed equalizer as the frontend of a receiver for backplane communication. The equalizer combines an analog equalizer and a twotap decisionfeedback equal izer in a halfrate structure to reduce the intersymbolinterference （ISI） of the communication chan nel. By employing inductive peaking technique for the highfrequency boost circuit, the bandwidth and the boost of the analog equalizer are improved. The decisionfeedback equalizer optimizes the size of the CMLbased circuit such as D flipflops （DFF） and multiplex （MUX）, shortening the feedback path delay and speeding up the operation considerably. Designed in the 0. 181μm CMOS technology, the equalizer delivers 10Gb/s data over 18in FR4 trace with 28dB loss while drawing 27mW from a 1.8V supply. The overall chip area including pads is 0. 6 -0.7mm2.

AN ENHANCED DETECTION ALGORITHM FOR V-BLAST SYSTEM

A decoding method complemented by Maximum Likelihood (ML) detection for V-BLAST (Verti- cal Bell Labs Layered Space-Time) system is presented. The ranked layers are divided into several groups. ML decoding is performed jointly for the layers within the same group while the Decision Feedback Equalization (DFE) is performed for groups. Based on the assumption of QPSK modulation and the quasi-static flat fading channel, simulations are made to testify the performance of the proposed algorithm. The results show that the algorithm outperforms the original V-BLAST detection dramatically in Symbol Error Probability (SEP) per- formance. Specifically, Signal-to-Noise Ratio (SNR) improvement of 3.4dB is obtained for SEP of 10?2 (4×4 case), with a reasonable complexity maintained.

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.

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.

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.