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.A...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.展开更多
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 gradie...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).展开更多
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 ...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.展开更多
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 inferrin...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.展开更多
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 f...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.展开更多
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 ...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.展开更多
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 equaliz...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.展开更多
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 o...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.展开更多
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...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.展开更多
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 de...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.展开更多
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) syst...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.展开更多
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 arch...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.展开更多
文摘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.
文摘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).
基金This work was supported by the National Natural Science Foundation of China(NSFC)(Nos.62301128,61871082,and 62111530150)the Open Fund of IPOC(BUPT)(No.IPOC2020A011)+1 种基金the STCSM(No.SKLSFO2021-01)the Fundamental Research Funds for the Central Universities(Nos.ZYGX2020ZB043 and ZYGX2019J008).
文摘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.
文摘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.
文摘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.
文摘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.
基金supported by the National Basic Research Program of China (2009CB320401)the National Key Scientific and Technological Project of China (2010ZX03003-001,2010ZX03003-003-01)+2 种基金the Research Funds for Doctoral Program of Higher Education of China (20090005110003)the National Natural Science Foundation of China (60902048)the Fundamental Research Funds for the Central Universities (2010PTB-03-04 G470220)
文摘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.
文摘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.
文摘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.
文摘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.
基金supported by the National Science and Technology Pillar Program (Nos 2008BAH30B12 and 2008BAH30B09)the Important National Science and Technology Specific Projects (Nos 2008ZX 03003-004, 2009ZX03003-008, 2009ZX03003-009, and 2009ZX 03002-009)+1 种基金the National Natural Science Foundation of China (No 60802009)the National High-Tech R & D Program (863) of China (Nos 2008AA01Z204 and 2009AA01Z205)
文摘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.
基金Supported partially by the National Natural Science Foundation of China (Grant No. 60971104)the Program for New Century Excellent Talents in University of China (Grant No. NCET-05-0794)the Doctoral Innovation Fund of Southwest Jiaotong University
文摘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.