Decision Feedback Research Articles

Impact of Equalizer step size in Underwater Acoustic Communication Channel

The Underwater Acoustic Channel (UAC) is a time variant channel and its multipath effects create ISI. This is one of the most important obstacles in the UAC channel which reduces the transmission rate. To remove this obstacle, a proper filter has to be designed in the receiver section. In this article, optimal step size for equalizer is computed and compared the results with the known techniques namely Decision Feedback Equalizer with interleave division multiple access (DFE IDMA) and Cyclic Prefix - Orthogonal Frequency Division Multiplexing (CP-OFDM) Equalizer. Channels are modeled using ray tracing methods. The various factors considered are ambient noise, attenuation loss, bottom and surface loss. The overall path loss for channels is computed by summing up the attenuation loss, surface and bottom loss. Simulation results evident that for short range UAC channel, the BER in the order of 10-2 is achieved using proposed methodology with least Eb/No compared to standard DFE method.

Research on Energy Consumption Analysis System of Large Buildings Based on Big Data Monitoring

Public buildings should install itemized electricity metering devices. The thesis establishes an energy consumption monitoring system and ensures that the energy consumption statistics are true and complete. The characteristics of big data on energy consumption of public buildings are analysed. At the same time, the article expounds the design thinking and implementation strategy of the big data public building energy consumption monitoring system. The building energy management system can monitor the status of energy-consuming equipment, with more detailed and broader classification, and realization of energy consumption decision feedback and optimized control.

Carrier Phase Recovery Based on KL Divergence in Probabilistically Shaped Coherent Systems

We propose a novel carrier phase recovery (CPR) method in probabilistically-shaped (PS) coherent systems. This method targets to make the distribution of the received samples match that of the desirable constellation, which is realized by minimizing the Kullback-Leibler (KL) divergence between these two distributions. This is in contrast to conventional blind phase searching (BPS) or maximum likelihood estimation that targets to match the received samples with the transmitted symbols. Consequently, the proposed method avoids pre-decisions or feedback of data decisions in conventional methods. The distribution of the target constellation is the same as that used in soft-decision forward error correction (SD-FEC) and can be implemented via look-up tables. This distribution is re-used in the KL-based CPR, thus avoiding additional overhead and the calculation of the Euclidean distance as in conventional BPS. Minimizing the KL divergence can be achieved via a recursive algorithm, blind phase searching, or the combination of both. Simulation and experimental results show that the proposed methods outperform principal component analysis (PCA), PCA+BPS and Kalman filtering, and achieve similar performance as conventional 2-stage BPS but with greatly reduced complexity. The proposed methods are also format transparent, suitable for parallel processing in a feedforward manner, and so are promising for CPR especially in SD-FEC based PS coherent systems.

Low Complex Receiver Design for Modified Inverse Source Coded Diffusion-Based Molecular Communication Systems

Diffusion Based Molecular Communication (DBMC) uses messenger molecules to transfer information between nanomachines. A major challenge for DBMC is Inter Symbol Interference (ISI), which is due to the diffusive nature of the molecular channel. By computing lower and upper bounds for the number of received molecules, in this letter we prove that ISI and Bit Error Rate (BER) of a DBMC system depend on a-priori probability of transmitted data. We derive the optimal value of the a-priori probability for which ISI and BER are minimized. A coding technique, called Modified Inverse Source Coding (MISC), is proposed to control the a-priori probability of the transmitted data. We also propose two low complexity receivers for MISC-DBMC. The first is a low complexity Reduced State Sequence Detector (RSSD) based on the Viterbi algorithm. The second is a computationally less intensive Decision Feedback based Maximum A-Posteriori (DF-MAP) threshold detector. Simulation results demonstrate that the DF-MAP receiver has a similar BER performance as that of the RSSD receiver in the ideal scenario, but practically it is limited by error propagation. Also, we show that the MISC-DBMC system using RSSD or DF-MAP receiver has better BER performance, increased range, and lower complexity compared to uncoded DBMC system.

Price of Fairness in Digital Subscriber Line Systems Using Dynamic Spectrum Management

Bit rate fairness is an important concern in current G.fast and the forthcoming fifth generation DSL systems where the frequency is extended to hundreds of MHz and crosstalk couplings reach unprecedented levels. In this paper, we study the price of fairness (PoF) in dynamic spectrum management (DSM) enabled DSL systems. We propose optimal low complexity PoF-constrained max-min fairness (MMF), weighted max-min fairness (WMMF), proportional fairness (PF), and (p,α)-PF algorithms. The proposed algorithms inherently provide weight factors which can be used to reduce the computational complexity of user encoding or decoding order optimizing algorithms used in nonlinear vectoring. Our simulation results show that although PoF grows exponentially with the minimum bit rate, fairness can be improved considerably with a relatively small price in DSM level 2 spectrum balancing and particularly in DSM level 3 using minimum mean square error (MMSE) generalized decision feedback equalizer (GDFE). It is also seen that the proposed optimal PoF-constrained MMF algorithm can reach the solutions of PF and (p,α)-PF measures for some PoF. That is, the PoF-constrained MMF or WMMF algorithms can be used instead of the non-linear PF and (p,α)-PF measures, which often result in computationally intensive solutions.

Teacher Empowerment on Second Language Education, a Malnourished Baby

As it was revealed in a preliminary survey, teacher empowerment programmes done in the Sri Lankan context focus more on content instruction and classroom pedagogy relating to general education. Empowerment programmes specifically designed to address crucial aspects of Teaching English as a Second Language are minimal. One of the objectives of this survey is to study genuine second language teacher needs to be addressed in teacher empowerment programmes . Another objective is to identify a benchmark for context sensitive teaching English as a second language teacher empowerment programmes and then to identify a framework for a quality teacher empowerment programme .A purposive sample of 100 teachers representing rural, semi urban and urban schools were selected to be the sample for the study. An available sample of 10 in Service Advisors was also interviewed to collect data for the study. The study was a qualitative research where questionnaires, semi structured interviews and classroom observations are the data gathering instruments. Data analysis revealed that the teacher empowerment programmes done were not based on the finding of the need analysis of the teachers of English. Problems that the teachers truly and practically faced in teaching English as a second language are not taken as the base line in organizing teacher empowerment programmes. Consequently the teacher teaches the average learner applying general approaches and methodologies unadapted. Second language teachers have not been fully empowered to accommodate pupils with low cognitive levels and common psycho social abnormalities. No mechanism was found to address the disadvantaged learners in the learning teaching process. Strategies to accommodate teacher feedback in decision making too are minimal. It is necessary to re define teacher empowerment programme and role of stake holders on Teaching English as a Second Language in Sri Lankan context.

Error-rate performance for signals with controlled intersymbol interference when applying detection algorithms of symbol-by-symbol processing with decision feedback

Error-rate performance for signals with controlled intersymbol interference when applying detection algorithms of symbol-by-symbol processing with decision feedback

A 32 Gb/s PAM-4 Optical Transceiver With Active Back Termination in 40 nm CMOS Technology

This article describes the design of a 32 Gb/s four-level pulse amplitude modulation (PAM- 4) optical transceiver in a 40 nm CMOS technology. At the transmitter side, the laser driver is composed of an asymmetric waveform equalizer, a 3-tap feed-forward equalizer (FFE), and a novel active-back termination (ABT) circuit. The ABT circuit provides a self-tracking, tunable source impedance to match the characteristic impedance of different laser diodes. At the receiver side, the fully integrated optical receiver consists of a transimpedance amplifier (TIA), a variable gain amplifier (VGA), an automatic threshold tracking circuit (ATC), and a quarter-rate decision feedback equalizer (DFE). By using the adaptive ATC, it reduces the BER induced by the harmonic distortion along the signal path by more than 27X under a THD of −20dB. Both the ATC and DFE are automatically adapted by an on-chip sign-sign LMS (SSLMS) engine. Fabricated in TSMC 40 nm CMOS process, the chip area for the transmitter and receiver are about 0.029 mm2 and 0.23 mm2. The power consumptions are about 146.8 mW and 128.8 mW respectively for the PAM-4 transmitter and receiver.

Bayesian Iterative Channel Estimation and Turbo Equalization for Multiple-Input–Multiple-Output Underwater Acoustic Communications

This article investigates a robust receiver scheme for a single carrier, multiple-input-multiple-output (MIMO) underwater acoustic (UWA) communications, which uses the sparse Bayesian learning algorithm for iterative channel estimation embedded in Turbo equalization (TEQ). We derive a block-wise sparse Bayesian learning framework modeling the spatial correlation of the MIMO UWA channels, where a more robust expectation-maximization algorithm is proposed for updating the joint estimates of channel impulse response, residual noise, and channel covariance matrix. By exploiting the spatially correlated sparsity of MIMO UWA channels and the second-order a priori channel statistics from the training sequence, the proposed Bayesian channel estimator enjoys not only relatively low complexity but also more stable control of the hyperparameters that determine the channel sparsity and recovery accuracy. Moreover, this article proposes a low complexity space-time soft decision feedback equalizer (ST-SDFE) with successive soft interference cancellation. Evaluated by the undersea 2008 Surface Processes and Acoustic Communications Experiment, the improved sparse Bayesian learning channel estimation algorithm outperforms the conventional Bayesian algorithms in terms of the robustness and complexity, while enjoying better estimation accuracy than the orthogonal matching pursuit and the improved proportionate normalized least mean squares algorithms. We have also verified that the proposed ST-SDFE TEQ significantly outperforms the low-complexity minimum mean square error TEQ in terms of the bit error rate and error propagation.

An Efficient Interference-Aware Constrained Massive MIMO Beamforming for mm-Wave JSDM

Low-complexity beamformer design with practical constraints is an attractive research area for hybrid analog/digital systems in mm-wave massive multiple-input multiple-output (MIMO). This paper investigates interference-aware pre-beamformer (analog beamformer) design for joint spatial division and multiplexing (JSDM) which is a user-grouping based two-stage beamforming method. Single-carrier frequency domain equalization (SC-FDE) is employed in uplink frequency-selective channels. First, unconstrained slowly changing statistical analog beamformer of each group, namely, generalized eigenbeamformer (GEB) which has strong interference suppression capability is designed by maximizing the mutual information in reduced dimension. Then, constant-modulus constrained approximations of unconstrained beamformer are obtained by utilizing alternating minimization algorithms for fully connected arrays and fixed subarrays. In addition, a dynamic subarray algorithm is proposed where the connections between radio frequency (RF) chains and antennas are changed with changing channel statistics. Convergence of the proposed alternating minimization-based algorithms is provided along with their complexity analysis. It is observed that the additional complexity of proposed algorithms is insignificant for the overall system design. Although most of the interference is suppressed with the help of proposed constrained beamformers, there may be some residual interference after analog beamforming stage. Thus, minimum mean square error (MMSE) criterion based iterative block decision feedback equalization (IB-DFE) method, which takes the residual interference in reduced dimension into account, is promoted for digital beamforming stage. Simulation results verify the superiority of the proposed interference-aware constrained design over existing approaches in terms of beampattern, spectral efficiency, outage capacity, bit-error rate (BER), and channel estimation accuracy.

Electric Power Intelligent Inspection Robot: a Review

The electric power intelligent inspection robot is equipped with high-definition visible light camera, infrared thermal imager, sound-collecting equipment and other intelligent detection devices and intelligent analysis algorithm software to complete the control loop from rapid acquisition of all-weather data, real-time information transmission, intelligent analysis and early warning to fast decision feedback. Therefore, instead of manual inspection, the automatic detection and intelligent analysis of the state of the power equipment are realized, and the reliability of the operation of the power grid and the power equipment is improved. The use of electric power intelligent inspection robots is an important means to realize the intelligentization of power grids, and is an important direction for the future development of smart grids. Given the current research status and deficiencies at home and abroad, this paper discusses the electric power intelligent inspection robots from aspects of main technologies, cutting-edge technology, functional positioning and standard system, and discusses the research status of electric intelligent inspection robots. On this basis, future research and development direction are put forward. This paper has a guiding role and reference value for the research of electric power intelligent inspection robot.

A Differentiating Receiver With a Transition-Detecting DFE for Dual-Rank Mobile Memory Interface

The signal integrity of dual-rank LPDDR5 interface is challenged by reflections from the stub of the inactive rank within the redistribution layer (RDL). These reflections arrive at the active rank within the unit-interval of the data-rate and distort not only the post-cursor but also the pre-cursor of the signal that the decision feedback equalizer (DFE) cannot remove. We introduce a differentiating receiver that restores data through edge information to eliminate the reflective distortions, including the pre-cursor, and a transition-detecting DFE to address remaining inter-symbol interference. A prototype receiver, fabricated in a 65 nm CMOS process, eliminates pre-cursor distortion with a reflection flight time of 50 ps and 70 ps, at 8 Gb/s and 6.4 Gb/s, resulting in a wide horizontal margin at a bit-error-rate of 10-12. The energy efficiency is 0.80 pJ/bit at 8 Gb/s.

A Blind Demodulation Algorithm for Underwater Acoustic MPSK Signal

Multiple phase-shift keying (MPSK) is a common carrier modulation in underwater acoustic (UWA) communication. This paper proposes a robust blind demodulation algorithm to improve the performance and practicability of blind demodulation of MPSK signals under impulsive noise and UWA sparse multipath channels. The proposed algorithm adopts a $T$ /2-spaced blind equalizer based on quasi-affine projection, so it has strong adaptability to sparse UWA multipath channels. First, the received signal is preprocessed to suppress the impulsive interference, and the parameters are estimated. Then, timing synchronization is performed point by point based on sliding discrete Fourier transform (SDFT) to avoid equalizer performance degradation or even lock-loss caused by large clock frequency deviation and long-time error accumulation. Based on this, the over-sampling signal with two fixed sampling points per symbol is obtained. Meanwhile, following the idea of memory improved proportionate affine projection algorithm and normalized factor, the cost function of the blind equalizer is constructed by using the $l_{0}$ norm penalty on the tap coefficients. In this way, the updating formula of the tap coefficients is derived, and the blind equalization is then conducted on the signal after timing synchronization to eliminate or weaken the channel influence. Finally, the residual frequency offset and phase offset of the carrier are removed through the M-power transform and the second-order decision feedback digital phase-locked loop. Simulation experiments and practical signal demodulation results indicate that the proposed algorithm achieves higher BER performance and modulation parameter robustness for impulsive noise and UWA multipath channel.

Sparse decision feedback equalization for underwater acoustic channel based on minimum symbol error rate

Abstract Underwater Acoustic Channels (UAC) have inherent sparse characteristics. The traditional adaptive equalization techniques do not utilize this feature to improve the performance. In this paper we consider the Variable Adaptive Subgradient Projection (V-ASPM) method to derive a new sparse equalization algorithm based on the Minimum Symbol Error Rate (MSER) criterion. Compared with the original MSER algorithm, our proposed scheme adds sparse matrix to the iterative formula, which can assign independent step-sizes to the equalizer taps. How to obtain such proper sparse matrix is also analyzed. On this basis, the selection scheme of the sparse matrix is obtained by combining the variable step-sizes and equalizer sparsity measure. We call the new algorithm Sparse-Control Proportional-MSER (SC-PMSER) equalizer. Finally, the proposed SC-PMSER equalizer is embedded into a turbo receiver, which perform turbo decoding, Digital Phase-Locked Loop (DPLL), time-reversal receiving and multi-reception diversity. Simulation and real-field experimental results show that the proposed algorithm has better performance in convergence speed and Bit Error Rate (BER).

Frequency Domain Analysis and Equalization for Molecular Communication

Molecular Communication (MC) is a promising micro-scale technology that enables wireless connectivity in electromagnetically challenged conditions. The signal processing approaches in MC are different from conventional wireless communications as molecular signals suffer from severe inter-symbol interference (ISI) and signal-dependent counting noise due to the stochastic diffusion process of the information molecules. One of the main challenges in MC is the high computational complexity of the existing time-domain ISI mitigation schemes that display a third-order polynomial or even exponential growth with the ISI length, which is further exasperated under the high symbol rate case. For the first time, we develop a frequency-domain equalization (FDE) with lower complexity, capable of achieving independence from the ISI effects. This innovation is grounded in our characterization of the channel frequency response of diffusion signals, facilitating the design of receiver sampling strategies. However, the perfect counting noise power is unavailable in the optimal minimum mean square error (MMSE) equalizer. We address this issue by exploiting the statistical information of the transmit signal and decision feedback for noise power estimation, designing novel MMSE equalizers with low complexity. The FDE for MC is successfully developed with its immunity to ISI effects, and its signal processing cost has only a logarithmic growth with symbol length in each block.

Unified IRS-Aided MIMO Transceiver Designs via Majorization Theory

In this paper, we develop a unified framework for IRS-aided transceiver designs under general power constraints in multiple-input multiple-output (MIMO) systems which implement interference (pre-)subtraction via Tomlinson-Harashima precoding (THP) or Decision Feedback Equalization (DFE) technologies. Armed with majorization theory, two fundamental classes of performance criteria, namely $K$ -increasing Schur-concave and Schur-convex functions of the logarithm of Mean Square Error (MSE) of the data stream, are investigated in depth. Firstly, we propose a simplified counterpart of the optimal transceiver design under general power constraints, with equivalence guaranteed by Pareto optimization theory and Lagrange duality. Moreover, the optimal semi-closed form solution to this simplified transceiver design can be attained using the modified subgradient method. Next, we prove that for any Schur-concave objective, the optimal nonlinear THP (DFE) design is in essence the linear precoding (equalization). For any Schur-convex objective, the optimal transceiver design results in individual data streams with equal MSEs, and thereby reduces to the Gaussian mutual information maximization based design. Based on the above conclusions, we further propose an efficient alternating optimization algorithm to decouple the optimization of the transmit precoder and the IRS reflection coefficients, where the classical successive convex approximation (SCA) technique is applied to fight against non-convex subproblems. From the low computational complexity perspective, a two-stage scheme is also developed inspired by the capability of the IRS in constructing favorable wireless links. Finally, numerical results show the global optimality of the modified subgradient method and the excellent performance of the proposed alternating optimization algorithm and two-stage scheme.

Generalized Regression Neural Network Based Fast Fading Channel Tracking Using Frequency-Domain CSI Smoothing

In the high mobility environment, the channel state information (CSI) in the last part of the packet is different from the beginning part’s actual channel. This phenomenon degrades channel estimation accuracy, and hence it is necessary to be compensated to realize reliable communications. Decision feedback channel estimation (DFCE) has been widely considered as the channel tracking approach. It still causes estimation errors due to the decision-making process in the presence of time and frequency selective fading environments. To address these issues, this paper newly proposes a generalized regression neural network (GRNN) based channel tracking scheme incorporated with frequency-domain CSI smoothing. The latter part is the key to improve the dependability of the training data sets. Computer simulation results confirm that the proposed scheme can achieve superior BER performance and the lower root mean square error (RMSE) value of estimated CSI better than the conventional ones.

Inter-Channel Fiber Nonlinearity Mitigation in High Baud-Rate Optical Communication Systems

Kerr nonlinearity in long distance fiber-optic link imposes a fundamental limitation to the capacity of wavelength division multiplexed (WDM) optical communication systems. Solving the inverse-propagating nonlinear Schrodinger equation (NLSE) is in principle a fundamental methodology to mitigate fiber nonlinear impairments. However, solving multi-channel coupled NLSE is too complexed to be implemented commercially. It is of great importance to compensate for nonlinear distortions in high baud-rate WDM systems based on the information of one single target channel. In this article, we experimentally study fiber nonlinearity mitigation in an 8 × 512 Gb/s (64Gbaud) polarization division multiplexed (PDM) 16-ary quadrature amplitude modulation (16-QAM) Nyquist-WDM system with 960 km standard single mode fiber (SSMF) loop transmission. The digital back-propagation (DBP) algorithm based on the target channel is applied, which can compensate for intra-channel nonlinearity. The inter-channel nonlinearity of cross phase modulation (XPM) induces nonlinear polarization scattering and nonlinear phase noise, which are partially correlated over adjacent pulses in high baud-rate systems. We propose an advanced nonlinear polarization crosstalk canceller (NPCC) with a novel decision feedback (DF) update strategy that is called the DF-NPCC, which incorporates the iterative process of the least mean square (LMS) algorithm and can thus mitigate the correlated nonlinear polarization crosstalk and nonlinear phase noise. Through nonlinearity mitigation, the DBP together with the DF-NPCC can achieve a 0.77 dB Q2 factor promotion. The DF-NPCC can suppress the remaining nonlinear distortions after the single channel DBP, which corresponds to a 0.36 dB Q2 factor improvement due to inter-channel nonlinear mitigation. Our work indicates XPM distortions can be mitigated with low complexity digital signal processing such as the DF-NPCC based on the single channel information, which provides an attractive solution to compensate inter-channel fiber nonlinearity in high baud-rate Nyquist-WDM systems.

A DFE-Enhanced Phase-Difference Modulation Signaling for Multi-Drop Memory Interfaces

This brief presents a phase-difference modulation signaling enhanced by decision feedback equalization for multi-drop memory interfaces. The phase-difference modulation signaling enables efficient data communication via a multi-drop channel, but only if the stub lengths are specially engineered. In this brief, the phase-difference modulation signaling is combined with decision feedback equalization to extend the applicable channel range. A test chip with a phase-difference-modulation transmitter and a decision feedback equalization receiver was fabricated in 65-nm CMOS technology. With a 2-tap decision feedback equalization, the test chip achieved the data rate of 7.8 Gb/s/pin via the both multi-drop channels with a long 10-cm stub and a short 2-cm stub, whereas the previous work without decision feedback equalization does not work with the 2-cm stub channel at 7.8Gb/s/pin.

Two Dimensional Adaptive Multichannel Decision Feedback Equalization for OTFS System

Orthogonal time-frequency-space (OTFS) modulation has shown significant error performance advantages over orthogonal frequency division multiplexing (OFDM) in time-varying channels. However, the symbols in OTFS frames still suffer from inter-Doppler interference (IDI) and inter-symbol interference (ISI). In this letter, we propose a two dimensional (2D) adaptive multichannel decision feedback equalizer (DFE) to combat the interference. The proposed receiver constructs the input signal and feedback signal of the DFE based on the characteristic of OTFS modulation. Meanwhile, by exploiting the sparsity feature of the channels in delay-Doppler domain, the iterative improved proportionate normalized least mean squares (IPNLMS) algorithm is employed to achieve fast convergence and fast tracking. The simulation results show that the proposed equalizer could achieve satisfied BER performance with lower complexity.