MMSE Equalizer Research Articles

A Low-Complexity Widely-Linear MMSE Equalizer for DFT-Spread OFDM With Frequency-Domain Spectrum Shaping

A Low-Complexity Widely-Linear MMSE Equalizer for DFT-Spread OFDM With Frequency-Domain Spectrum Shaping

Blind MMSE Equalization of SISO IIR Channels Using Oversampling and Multichannel Linear Prediction

In this correspondence, a novel linear prediction based blind equalization algorithm for Single Input Single Output (SISO) IIR channels is proposed. The new algorithm is based on second-order statistics and it does not require channel order estimation. By oversampling the channel output, the SISO channel model is converted to a special Single Input Multiple Output (SIMO) model. Two forward linear predictors with consecutive prediction steps are applied to the subchannel outputs of the SIMO model. It is proved that the partial parameters of the SIMO model can be estimated from the difference between the prediction errors when the length of the predictors is sufficiently large. The sufficient filter length for achieving the optimal prediction is also derived. Based on the estimated parameters, both batch and adaptive Minimum-Mean-Square-Error (MMSE) equalizers are developed. The performance of the proposed equalizers is evaluated by computer simulations and compared with existing algorithms.

MMSE Equalizer Design Optimization for Wireline SerDes Applications

MMSE Equalizer Design Optimization for Wireline SerDes Applications

Neural-Network-Based Equalization and Detection for Underwater Acoustic Orthogonal Frequency Division Multiplexing Communications: A Low-Complexity Approach

The performance of the underwater acoustic (UWA) orthogonal frequency division multiplexing (OFDM) system is often restrained by time-varying channels with large delays. The existing frequency domain equalizers do not work well because of the high complexity and difficulty of finding the real-time signal-to-noise ratio. To solve these problems, we propose a low-complexity neural network (NN)-based scheme for joint equalization and detection. A simple NN structure is built to yield the detected symbols with the joint input of the segmented channel response and received symbol. The coherence bandwidth is investigated to find the optimal hyperparameters. By being completely trained offline with real channels, the proposed detector is applied independently in both simulations and sea trials. The results show that the proposed detector outperforms the ZF and MMSE equalizers and extreme learning machine (ELM)-based detectors in both the strongly reflected channels of the pool and time-variant channels of the shallow sea. The complexity of the proposed network is lower than the MMSE and ELM-based receiver.

A Block-Based Concatenated LDPC-RS Code for UAV-to-Ground SC-FDE Communication Systems

To enhance the reliability of UAV-to-Ground data transmission in SC-FDE, this study proposes a combined MMSE equalization and LDPC-RS concatenated code approach, utilizing source redundancy to mitigate errors in low-elevation G2A communication. The proposed coding scheme combines a binary LDPC code as the outer code and an improved RS code as the inner code, resulting in improved resistance to burst and random errors. To address the coding aspect, this paper designs a coding table using RS coding as the underlying logic, enabling efficient coding. For decoding, a decoding scheme based on the minimum Euclidean distance criterion is devised, enabling high-performance decoding. Simulation results demonstrate that the proposed LDPC-RS concatenated coding scheme significantly enhances the performance of the communication system, thereby offering considerable potential for application in UAV video transmission systems.

Low-Resolution MMSE Equalization for Massive MU-MIMO Systems

Finite-alphabet equalization that represents a spatial equalization matrix with low-resolution coefficients is a promising technique to reduce the power consumption, processing delay, and circuit area of baseband processing in the context of all-digital massive multiuser multiple-input multiple-output uplink systems. However, to minimize the performance loss caused by a coarse-resolution spatial equalization matrix, its coefficients must be carefully designed in the minimum mean-square error sense to achieve the desired bit error rate (BER) performance, which unfortunately constitutes an NP-hard optimization problem. To tackle this problem, we first reformulate the finite-alphabet equalization design problem as an unconstrained optimization on a smooth Riemannian manifold. Then we propose an algorithm based on Riemannian manifold optimization (RMO) to solve the reformulated problem. Based on simulation results, the proposed 2-bit RMO-assisted equalizer outperforms its state-of-the-art counterparts while maintaining the same asymptotic complexity. In addition, the proposed 2-bit RMOassisted equalizer exhibits a loss of only 1.47 dB at a BER of 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-4</sup> compared to an unquantized linear minimum mean squared error equalizer.

Supervised identification and equalization of transmission channel using reproducing kernel Hilbert space

The subject matter of the article is to identify and equalize the parameters of telecommunication channels. The goal is to develop a new mathematical approach based on positive definite kernels on a Hilbert space. The tasks to be solved are: (a) to formulate a mathematical procedure based on a kernel; a kernel is a function that maps pairs of data points to a scalar value, and positive definite kernels are widely used in machine learning and signal processing applications; (b) to identify the channel parameters using the proposed method; and (c) to apply the Zero Forcing and MMSE equalizer to measure the performance of the proposed system. This article introduces a new method to address the problem of supervised identification of transmission channel parameters based on the positive definite kernel on Hilbert space, which implements Gaussian kernels. The input sequence, used as an input for a system or process, is assumed to be independent, have a zero mean, a non-Gaussian distribution, and be identically distributed. These assumptions are made to simplify the analysis and modeling. The proposed method for estimating the parameters of the channel impulse response yields promising results, indicating that the estimated parameters are close to the measured parameters of the model for various channels. The convergence of the estimated parameters toward the measured parameters of the model is particularly noticeable for BRAN A (indoor) and BRAN E (outdoor) channels. The method has been tested with different channel models, and the results remain consistent. Overall, the proposed method appears to be a reliable and effective approach for estimating channel impulse response parameters. The accuracy of the estimated parameters is particularly noteworthy considering the challenges inherent in modeling wireless channels, which can be influenced by various factors such as obstacles and interference. These findings have important implications for the design and optimization of wireless communication systems. Accurate estimates of channel impulse response parameters are essential for predicting and mitigating the effects of channel distortion and interference, and the proposed method represents a promising tool for achieving this goal. Further research and testing are needed to validate and refine the method and to explore its potential applications in different settings and scenarios. We evaluated the performance of the system using the estimated parameters obtained from the proposed method. Two equalizers, MMSE and ZF, were used, and the results show that MMSE outperforms ZF. Both equalizers produced highly satisfactory outcomes.

An Estimated δ-Based Iterative Block Decision Feedback Equalization in SC-FDE System

We provide a novel nonlinear frequency domain equalization algorithm for the frequency domain equalization of an SC-FDE system by improving the classical iterative block decision feedback equalization (IBDFE) algorithm and applying δ estimation to the improved algorithm. The improvement of the IBDFE algorithm is carried out by replacing the ZF equalization in the feedback branch with the MMSE equalization and eliminating the iteration of the correlation factor, thus reducing the noise error and the computational complexity of the original algorithm. δ estimation can estimate residual inter-symbol interference in the signal after MMSE equalization and reject it, thus further improving the equalization accuracy. The simulation results show that the performance of the novel algorithm is better than that of the IBDFE algorithm with similar complexity, or the complexity of the novel algorithm is lower than that of the IBDFE algorithm with similar performance.

One-Bit MMSE Equalization for All-Digital Massive MU-MIMO Communication Systems

This article considers using a 1-b spatial equalization matrix to enable low-power and high-throughput hardware processing in massive multiuser multiple-input--multiple-output (MIMO) uplink systems with all-digital base station architectures. We aim to design 1-b spatial equalization matrix coefficients that minimize the postequalization mean squared error that leads to a nonconvex problem. Known solutions based on semidefinite relaxation (SDR) and forward–backward splitting (FBS) are proposed to solve the 1-b spatial equalization matrix design problem. The SDR-based algorithm can achieve excellent bit error rate (BER) performance. However, its high complexity limits its use in large-scale system configurations. The FBS-based algorithm contains several predetermined tuning parameters that are scenario-dependent and nontrivial to obtain. Moreover, the performance of the FBS-based algorithm still requires improvement. On the basis of these considerations, we propose two algorithms for solving the 1-b spatial equalization matrix design problem. The first algorithm reduces the performance gap between the unquantized linear minimum mean squared error equalizer and the 1-b SDR-assisted equalizer while maintaining a reasonable complexity; the second algorithm balances the tradeoff between complexity and performance. Simulation results show that the proposed algorithms can achieve excellent BER performance compared with state-of-the-art methods.

Successive Interference Cancellation on Frequency-Selective Channels With Mode-Dependent Gain

In ultra-long-haul optical communication systems, polarization-dependent gain from single-mode semiconductor optical amplifiers or mode-dependent gain from multi-mode erbium-doped fiber amplifiers causes a channel’s achievable information rate (AIR) using linear minimum-mean-square error (MMSE) detection to become substantially lower than the capacity of optimal maximum likelihood detection owing to loss of orthogonality between modes. However, such capacity loss can be mitigated using techniques that retain reasonable complexity, such as successive interference cancellation (SIC). In wireless systems, multicarrier transmission with many subcarriers transforms the frequency-selective channel into a set of uncoupled memoryless subchannels, and it is possible to obtain good performance using SIC on each subchannel independently. Multicarrier transmission in optical fibers, however, is susceptible to performance loss caused by Kerr nonlinearity. Therefore, we study methods for applying SIC on frequency-selective optical fiber channels using single-carrier transmission. We compare the theoretical performance of MMSE and SIC equalizers on frequency-selective optical fiber channels operating at 64 Gbaud with transmission distances up to 15,000 km and evaluate the penalties of SIC implementation. We consider single-mode fiber links with random artificial polarization-mode dispersion inserted along the link to enhance frequency diversity, as well as multi-core fiber links supporting 14 spatial and polarization modes. We show that SIC can increase outage capacity by 19%, but the AIR increase is reduced by almost half if error correction coding is not performed across modes. We confirm that SIC can adapt to fast channel perturbations with only a 3% decrease in AIR.

Hybridization of zero forcing-minimum mean square error equalizer in multiple input multiple output system

Transmission of high data rate over multipath environment is associated with many wireless applications. However, this transmission results in high delay which leads to ISI distortion and causes high error. The existing LE in MIMO systems such as ZF equalizer used in addressing this problem reduces the ISI distortion completely but results in noise amplification. Likewise, MMSE equalizer only reduces the noise but cannot eliminate ISI. Therefore, this paper proposed a hybrid LE for Wi-Fi over Weibull fading channel. The hybridized ZF-MMSE equalizer was developed using conventional ZF and MMSE equalizers. Digital transmitted signal propagated over Weibull fading channel is received at the receiver through multiple antennas and then combined using MRC. The combined signal is then equalized using hybrid ZF-MMSE to eliminate both the ISI and the amplified noise. The developed model was simulated in MATLAB software environment and evaluation was performed using BER and Pout. The results obtained revealed that the hybrid ZF-MMSE equalizer gave better performance over existing equalizers in a MIMO system. Therefore, the proposed equalizer will help improve the performance of the Wi-Fi technology.

Data-Driven and Model-Driven Joint Detection Algorithm for Faster-Than-Nyquist Signaling in Multipath Channels.

In recent years, Faster-than-Nyquist (FTN) transmission has been regarded as one of the key technologies for future 6G due to its advantages in high spectrum efficiency. However, as a price to improve the spectrum efficiency, the FTN system introduces inter-symbol interference (ISI) at the transmitting end, whicheads to a serious deterioration in the performance of traditional receiving algorithms under high compression rates and harsh channel environments. The data-driven detection algorithm has performance advantages for the detection of high compression rate FTN signaling, but the current related work is mainly focused on the application in the Additive White Gaussian Noise (AWGN) channel. In this article, for FTN signaling in multipath channels, a data and model-driven joint detection algorithm, i.e., DMD-JD algorithm is proposed. This algorithm first uses the traditional MMSE or ZFinear equalizer to complete the channel equalization, and then processes the serious ISI introduced by FTN through the deepearning network based on CNN or LSTM, thereby effectively avoiding the problem of insufficient generalization of the deepearning algorithm in different channel scenarios. The simulation results show that in multipath channels, the performance of the proposed DMD-JD algorithm is better than that of purely model-based or data-driven algorithms; in addition, the deepearning network trained based on a single channel model can be well adapted to FTN signal detection under other channel models, thereby improving the engineering practicability of the FTN signal detection algorithm based on deepearning.

Efficient Transmission of Watermarked Images with SC-FDMA

This paper studies the transmission of watermarked images on wireless SC-FDMA systems. Different watermarking schemes are provided in this frame work for security and authentication purposes.SC-FDMA is accomplished for broadband wireless communications, for its advantages of low PAPR and low sensitivity to carrier frequency offsets. The SC-FDMA system is used for communication as it is the uplink standard in coefficient mobile communication system. In this paper, two types of equalizers(MMSE and ZF) is used to mitigate the effect of channel fading and noise at different subcarriers mapping schemes(localized and distributed).Watermark extraction is performed at the receiver and an evaluation process for the quality of extracted watermarks is performed. Simulation experiments were established in MATLAB program R2014a version. Simulation results approved that the interleaved system provides better performance than that of the localized systems. In addition the system performance has better enhancement when using the ZF equalizer than the MMSE equalizer. The quality of the received of extracted watermarks is investigated with different metrics like PSNR, correlation coefficient (Cr) , SSIM and the histogram . Simulation results also demonstrate an efficient of image clarity at extraction process.

Diversity Enhancement of MIMO System with GA Optimized Linear Precoding of Orthogonal Space-Time Block Codes

The MIMO (Multiple-Input Multiple-Output) systems in the field of digital communications make it possible to improve data transmission according to two main parameters which are often antagonistic: the information rate and the transmission reliability estimated in average binary error probability (BEP) term. With such systems, CSI (Channel State Information) on transmission is a key point for reducing the BEP thanks to different power allocation strategies. Thus, a linear precoder on transmission associated with a linear decoder on reception can optimize a particular criterion thanks to this information. This paper proposes a MIMO system with precoded orthogonal space-time block codes (OSTBC) optimized by Genetic Algorithm with ZF and MMSE equalization. The performance evaluation is achieved by BER vs. SNR graphs.

Performance Analysis of ZF-SIC and MMSE Equalizers for MIMO System in the Presence of Alpha(α)-MU(µ) Fading Channel

Performance Analysis of ZF-SIC and MMSE Equalizers for MIMO System in the Presence of Alpha(α)-MU(µ) Fading Channel

Filter Bank Multi Carrier Signal System for Frequency Selective Channels

Filter Bank Multicarrier (FBMC) frameworks are a subclass of multicarrier (MC) frameworks. The essential guideline, separating spectrum into many thin sub channels, may not be new, MC frameworks have seen wide appropriation. These days, multicarrier regulation frameworks dependent on the discrete Fourier transforms are usually used to transmit over recurrence particular channels subject to forceful noise aggravations. In any case, these handsets experience the ill effects of poor sub channel spectral control, that is, the measure of inter channel impedance isn't unimportant. It very well may be indicated that the framework execution reduces when it is dependent upon an unsettling influence with a large portion of its energy focused on a narrow frequency band. This Paper aims that identify the Filter Bank Multi Carrier (FBMC) performance. The MIMO system combined with the FBMC then identifies the over Frequency Selective Channel (FSC). Here the analysis for FSC, Flat fading model FBMC and system with MMSE equalization. The Prototype filters are analyzing the system performance characteristics. The Power Spectral Density (PSD) of the MIMO FBMC system for the given spectrum. The proposed systems are best to compare all existing technique and we measure the spectral efficiency of the system.

A Performance Analysis of Wavelet based LTE-OFDM with Multi-equalizers

A rapid change in usage of mobile data and mobile based applications such as online gaming, mobile TV etc. motivates the mobile wireless communication system to work on new emerging trends Long Term Evolution (LTE). LTE-4G architecture provides an optimized packet based radio access technology. The basic signal format of LTE is Orthogonal Frequency Division Multiplexing (OFDM) Modulation. This modulation scheme satisfies the spectrum flexibility requirement in LTE. LTE-MIMO-OFDM with IFFT and FFT transform introduced to provide the cost-effective solution for very wide carriers with high PAPR. Inter-symbol Interference (ISI) and inter carrier interference (ICI) are predominant factors which exist in the wireless communication channel. Existing methods of ISI and ICI reduction to improve signal quality have been achieved using conventional OFDM techniques. However, a better modulation scheme is required to achieve high data rate, low latency and flexible bandwidth deployment. This paper has proposed a Wavelet based LTE-MIMO-OFDM modulation scheme with multi equalizer that improves the performance of system by reducing PAPR and BER in MCM. Experimental results show the performance of this scheme and the results is compared with existing MMSE and Multi equalizer MMSE and achieves a flexible spectral efficiency with reduction of PAPR and BER.

A Simple Two-Stage Equalizer for OTFS With Rectangular Windows

Orthogonal time-frequency space (OTFS) bears the potential to achieve excellent performance in high mobility scenarios. However, conventional linear equalizer performs poorly. In this letter, we propose a novel two-stage equalizer, wherein received signals are judiciously processed in two domains. First, in the time-frequency domain, a sliding window-assisted minimum mean square error (SW-MMSE) equalizer is proposed to restrict the inter-carrier interference effectively. Then in the second stage, a delay-doppler domain equalizer further mitigates the residual inter-symbol interference of the first stage. Our proposed two-stage equalizer enjoys low implementation complexity, and simulation validates its performance advantage over the conventional one-stage MMSE equalizer.

Study the BER Performance Comparison of MIMO Systems Using BPSK Modulation with ZF and MMSE Equalization

In the modern age, wireless communication is very helpful in various mobile antenna communication systems. In mobile communication systems, the transmission of data transfer rates is very high and it plays an important role in several services like video, top-quality audio, and mobile integrated service digital network. During the transmission of data at higher data transmission rates through the mobile radio channels, the channel impulse response can spread over many symbol periods as well as cause inter-symbol interference (ISI). Wireless transmission is suffering from fading and interference effects which may be combated with equalizer. Due to fading and interference, it creates a problem for signal recovery in wireless communication. The main objective of this paper is to analyze the different types of equalizers such as ZF and MMSE for BPSK modulation. The simulation result has been developed by using MATLAB toolbox version 2015a and a multi-tap ISI channel is considered. By analyzing the simulation result it shows that if the number of tap lengths is increasing, BER will decrease in ZF equalizer. And finally shows BER vs SNR comparison of two different types of an equalizer and is able to find out MMSE performance is better than ZF equalizer.

Nonlinear MMSE Equalizer for Impulsive Noise Mitigation in OFDM-Based Communications

Destructive effects of impulsive noise has been broadly observed not only in wireless communication systems but also in power-line communications. Impulsive noise is a common impediment in orthogonal frequency division multiplexing (OFDM) based communication systems for industry applications. This non-Gaussian noise degrades the performance of conventional equalizers and, hence, elicit a modified version of the equalizer that fits the non-Gaussian description of the noise. This letter proposes a nonlinear minimum mean square error equalizer for OFDM systems where the characteristics of the added noise to the system is known. The soft values were obtained based on the derivation of the equalizer for a memory-less channel impaired with impulsive noise. Obtaining such values are required for the implementation of a turbo-equalization scheme. The validity of such an equalizer is tested through simulations and the result of simulations shows that the nonlinear equalizer is successful in combating the effect of an impulsive noise. Thus, the turbo-coded OFDM system shows a significant boost at low signal-to-noise ratios.