Performance Of Channel Equalizer Research Articles

Numerical Study of Parallel Optoelectronic Reservoir Computing to Enhance Nonlinear Channel Equalization

Nonlinear impairment is one of the critical limits to enhancing the performance of high-speed communication systems. Traditional digital signal processing (DSP)-based nonlinear channel equalization schemes are influenced by limited bandwidth, high power consumption, and high processing latency. Optoelectronic reservoir computing (RC) is considered a promising optical signal processing (OSP) technique with merits such as large bandwidth, high power efficiency, and low training complexity. In this paper, optoelectronic RC was employed to solve the nonlinear channel equalization problem. A parallel optoelectronic RC scheme with a dual-polarization Mach–Zehnder modulator (DPol-MZM) is proposed and demonstrated numerically. The nonlinear channel equalization performance was greatly enhanced compared with the traditional optoelectronic RC and the Volterra-based nonlinear DSP schemes. In addition, the system efficiency was improved with a single DPol-MZM.

Multi-Channel Data Stream Transmission Method of Internet of Things in Power Systems (IOTIPS) Based on Big Data Analysis

To solve the problems of frequent network link jitter and high bit error rate is the development direction of power grid communication technology. Therefore, a multi-channel data stream transmission method of Internet of things in power systems based on big data analysis is proposed. The data stream matching method based on big data stability mechanism is constructed by using data stream matching method to match the data stream to be transmitted and improve the anti-noise performance of the transmission process; the multichannel model of data stream transmission is constructed, and the matched data stream is transmitted by the multi-channel model; the big data analysis technology is used to process the data stream transmission process and improve the transmission performance of the model; the adaptive multi-channel equalization control method of sampling decision is used to realize the equalization design of data stream transmission channel, optimize the model transmission process, and reduce the bit error rate of transmission. Experimental results show that this method has better channel equalization performance; the link jitter frequency of this method is low, and it has better transmission stability; the lowest bit error rate can reach 0%, and the reliability of data stream transmission is high.

Underwater acoustic communication in time-varying channel environment based on passive time reversal

Channels estimated from probe signal or training symbols will mismatch with the true channels in passive time reversal (TR) underwater acoustic (UWA) communication when the ocean environment is time-varying, leading to poor channel equalization performance. In order to solve this problem, this paper studies Block-based TR processing structure. The received data are divided into small blocks and the channel estimated from previously decoded symbols is used to match filter the current block to suppress the channel mismatch. Decision feedback equalizer (DFE) is also combined with Block-based TR to remove the residual inter symbol interference (ISI) and Doppler effect (Block-based TR-DFE). Mobile UWA communication experiment was conducted near Xiao Changshan island, Dalian in July, 2015. Though the received data are preprocessed by resampling, residual Doppler effect still exists due to ununiform motion between the receiver and transmitter, making the channel time-varying fastly. Block-based TR realized effective tracking and compensation for time-varying channels. The output signal-to-noise ratio (SNR) after equalization is greatly improved and the bit error rate (BER) is significantly reduced.

Improving performance of channel equalization in RSOA-based WDM-PON by QR decomposition.

In reflective semiconductor optical amplifier (RSOA)-based wavelength division multiplexed passive optical network (WDM-PON), the bit rate is limited by low modulation bandwidth of RSOAs. To overcome the limitation, we apply QR decomposition in channel equalizer (QR-CE) to achieve successive interference cancellation (SIC) for discrete Fourier transform spreading orthogonal frequency division multiplexing (DFT-S OFDM) signal. Using an RSOA with a 3-dB modulation bandwidth of only ~800 MHz, we experimentally demonstrate a 15.5-Gb/s over 20-km SSMF DFT-S OFDM transmission with QR-CE. The experimental results show that DFTS-OFDM with QR-CE attains much better BER performance than DFTS-OFDM and OFDM with conventional channel equalizers. The impacts of several parameters on QR-CE are investigated. It is found that 2 sub-bands in one OFDM symbol and 1 pilot in each sub-band are sufficient to achieve optimal performance and maintain the high spectral efficiency.

Performance analysis of channel estimation in amplify‐and‐forward multi‐hop direct forwarding wireless networks

AbstractIn this paper, we study the performance of a training‐based least square (LS) and linear minimum mean‐square‐error (LMMSE) channel estimation for both hop‐by‐hop and multi‐hop direct forwarding wireless sensor networks over frequency‐selective fading channels. Specifically, to investigate the properties of the channel estimation, we accomplish a theoretical analysis of MSE in terms of various link parameters. From the performance evaluation, we analytically present the effects of the number of hops on the MSE performance for channel estimations in both multi‐hop networks. Interesting observations of MSE behaviors under various conditions are discussed, and the receiver complexity and channel equalization performance are also analyzed. Finally, through the computer simulations, the analytical results and detection performance are demonstrated. Copyright © 2014 John Wiley & Sons, Ltd.

A Dual-mode Blind Equalization Algorithm for Improving the Channel Equalized Performance

In this paper, we propose a new dual-mode blind equalization algorithm to improve the channel equalization performance. First, a kind of new error function is proposed and a nonlinear function between the step-size factor and the new error function is built to realize the variable step-size constant modulus algorithm (CMA). Then, in order to optimize the channel equalization performance, the variable step-size CMA is combined with decision directed least mean square (DDLMS) algorithm to form the new dual-mode algorithm. The new dual- mode algorithm uses either switching-dual mode or weighted dual-mode to update the coefficient of blind decision feedback equalizer. The simulation results show that in the situation of Gaussian white noise with 30dB signal-to-noise ratio, the proposed algorithm reduces the steady-state error to 12dB and drops inter symbol interference to 31dB in the condition of satellite channel. The new dual-mode algorithm not only increases the rate of convergence but also reduces the steady- state error. The proposed algorithm can be widely used in different communication channels such as the satellite channel.  Index Terms—Blind equalization; constant modulus algorithm; variable step-size; decision directed least mean square.

MIMO Equalization With State-Space Channel Models

State-space models are proposed to represent multiple-input multiple-output (MIMO) frequency-selective wireless channels with the motivation of better model approximation and more robust channel equalization performance when the order of the channel model is lower that of the true channel. We develop a simple framework under which the equalizers for state-space channel models can be designed using the existing methods for designing equalizers for finite impulse response (FIR) models. In particular, a MIMO minimum mean-squared error decision feedback equalizer is developed for state-space models. When only estimates of the channel are available to the receiver, the equalization performance is affected by the channel estimation accuracy. Because reduced-order state-space models can provide lower <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">H</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> channel estimation error than reduced-length FIR models, state-space based equalizers typically exhibit significantly smaller symbol error rate than FIR-based ones. Thus, state-space channel models can be a more robust choice than FIR models in the presence of model order selection error. Numerical simulation also shows that adaptive state-space based receivers have slower but comparable convergence rates compared to FIR-based adaptive equalizers.

Power allocation of data dependent superimposed training

Data Dependent Superimposed Training (DDST) scheme outperforms the traditional superimposed training by fully canceling the effects of unknown data in channel estimator. In DDST, however, the channel estimation accuracy and the data detection or channel equalization performance are affected significantly by the amount of power allocated to data and superimposed training sequence, which is the motivation of this research. In general, for DDST, there is a tradeoff between the channel estimation accuracy and the data detection reliability, i.e., the more accurate the channel estimation, the more reliable the data detection; on the other hand, the more accurate the channel estimation, the more demanding on the power consumption of training sequence, which in turn leads to the less reliable data detection. In this paper, the relationship between the Signal-to-Noise Ratio (SNR) of the data detector and the training sequence power is analyzed. The optimal power allocation of the training sequence is derived based on the criterion of maximizing SNR of the detector. Analysis and simulation results show that for a fixed transmit power, the SNR and the Symbol Error Rate (SER) of detector vary nonlinearly with the increasing of training sequence power, and there exists an optimal power ratio, which accords with the derived optimal power ratio, among the data and training sequence.

Iterative Extrapolation for Channel Equalization in DVB-T Receivers

This paper proposes an iterative extrapolation method to improve the channel-equalization performance for DVB-T receivers. The coefficients of a one-tap equalizer are conventionally obtained by taking the reciprocals of the estimated channel coefficients. Inversion of the channel coefficients, however, may cause noise enhancement at those frequencies corresponding to spectral nulls. To reduce such undesirable noise amplification, iterative extrapolation based on the time-domain truncation is performed, resulting in smooth nulls. Simulation results show that the proposed method improves the channel-equalization performance compared to the conventional zero-forcing equalization method in terms of bit error rates in DVB-T receivers at all SNRs.

Serially Connected Bi-Directional DFE Suitable for 8-VSB Modulation

A serially connected bi-directional decision feedback equalizer (SB-DFE) is proposed to improve the performance of channel equalization by exploiting implicit multipath diversity. The SB-DFE improves equalization performance by cascading normal and time-reversed DFEs. Conventional bi-directional DFEs obtain improved performance by combining the two DFEs in parallel. They, however, need accurate channel estimation and the performance is not guaranteed for multi-level modulation because they utilize each DFEs hard decided output symbols. On the other hand, the SB-DFE utilizes the soft output of the normal DFE at the first DFE as the input to the following time-reversed DFE without channel estimation. The performance of the SB-DFE is compared with that of the normal DFE and the bi-directional arbitrated DFE (BAD) in the 8-level vestigial sideband (8-VSB) modulation system with Brazilian digital high-definition television (HDTV) test channels. The SB-DFE has 1 /spl sim/ 1.8 dB signal-to-noise ratio (SNR) gains over the others.

Least-squares channel equalization performance versus equalization delay

Linear channel equalization has been a successful way to combat intersymbol interference (ISI) introduced by physical communication channels at high enough symbol rates. We consider the performance of least-squares equalizers in the single-input/multi-output (SIMO) channel context when the true channel is composed of an mth-order significant part and tails of small leading and/or trailing terms. Using a perturbation analysis approach, we show that if the diversity of the significant part is sufficiently large with respect to the size of the tails, then the lth-order least-squares equalizers, with l/spl ges/m-1, perform well for all the delays corresponding to the significant part. On the other hand, the performance of the equalizers for the delays corresponding to the tails may be poor.