Adaptive Channel Equalization Research Articles

Adaptive Channel Equalization for Digital Communication with Tunicate Swarm Algorithm

In wireless communication systems, the information is transferred as digital symbols these symbols are affected by noise interference while transmitting through the wireless channel. In today’s world, the increased demand for rapid data transmission rates leads to more inter-symbol interference in the received signal. To diminish the effects of inter-symbol interference adaptive channel equalization is utilized in digital communication. In this paper, the inter-symbol interference effect in the finite impulse response channel is reduced in terms of an adaptive equalizer method. The weights/coefficients of the equalizer are optimized through a tunicate swarm algorithm. Channel equalization involves optimizing the channel coefficients to mitigate the impact of inter-symbol interference. This equalization process can be viewed as an iterative optimization task, aimed at lessening the mean square error among the transmitted signal and the equalizer’s output. The implementation of the proposed method is executed using MATLAB software. We assess its effectiveness through a comparative analysis, considering metrics such as mean square error, learning rate, bit error rate, convergence rate, and computational time. Furthermore, we conduct a comparative evaluation with other optimization approaches, including the Bat Algorithm, Slime Mould Algorithm, and Harris Hawks Optimization Algorithm. This comparison demonstrates the superiority of the proposed algorithm over traditional optimization techniques.

Adaptive channel equalisation using different hybrid metaheuristic algorithms in digital communication

Adaptive channel equalisation using different hybrid metaheuristic algorithms in digital communication

A Correlation-Less Approach Toward the Steepest-Descent-Based Adaptive Channel Equalizer

A Correlation-Less Approach Toward the Steepest-Descent-Based Adaptive Channel Equalizer

Polar Coding Aided by Adaptive Channel Equalization for Underwater Acoustic Communication

Polar Coding Aided by Adaptive Channel Equalization for Underwater Acoustic Communication

Uni-Cycle Genetic Algorithm as an Adaptation Engine for Wireless Channel Equalizers

A recently developed adaptive channel equalizer driven by a so-called Uni-Cycle Genetic Algorithm (UCGA) is examined in the paper. The authors consider different initialization strategies of the iterative process and compare UCGA against the reference Recursive Least Squares (RLS) algorithm in terms of Bit Error Rate (BER) vs. Signal to Noise Ratio (SNR) performance and convergence rate of an adaptive channel equalizer. The results display a reasonable performance gain of UCGA over RLS for most of wireless channel models studied in the paper. Additionally, UCGA is capable of boosting the equalizer convergence. Thus, it can be considered a promising candidate for the future adaptive wireless channel equalizer.

Adaptive Channel Equalization Using Seagull Optimization with Various Initialization Strategies

In a digital communication channel, the transmitted signal may be dispersive causing the information to not be transmitted as same. Due to the distortion, the communication channel is affected known as Inter-Symbol Interference (ISI). To reducing the ISI effect, adaptive channel equalization plays an important role in digital communication. In the proposed method, the Finite Impulse Response (FIR) channel ISI effect is reduced by the proposed optimization algorithm. The FIR channel weight or coefficients are optimized by the proposed Seagull Optimization Algorithm (SOA) with different initialization strategies. Normally, SOA has random initialization of population but to improve the process of adaptive channel equalization, Random Number Generation (RNG), Opposition based learning (OBL) and Quasi-Opposition based learning (QBL) methods are utilized for initialization. The objective function of the equalization process is minimization error values where the error value is estimated based on the desired signal and channel output signal. The experiment is carried out on the MATLAB platform. The proposed method's effectiveness is shown to compare the various initialization methods of RNG, OBL and QBL with SOA. Based on different SNRs, the error value is computed and shows the convergence results.

Analysis of Existing and Proposed 3-Bit and Multi-Bit Multiplier Algorithms for FIR Filters and Adaptive Channel Equalizers on FPGA

Different multiplication algorithms have different performance characteristics. Some are good at speed while others consume less area when implemented on hardware, like Field Programmable Gate Array (FPGA)-the advanced implementation technology for DSP systems. The eminent parallel and sequential multiplication algorithms include Shift and Add, Wallace Tree, Booth, and Array. The multiplier optimization attempts have also been reported in adders used for partial product addition. In this paper, analogous to conventional multipliers, two new multiplication algorithms implemented on FPGA are shown and compared with conventional algorithms as stand-alone and by using them in the implementation of FIR filters and adaptive channel equalizer using the LMS algorithm. The work is carried out on Spartan-6 FPG that may be extended for any type of FPGA. Results are compared in terms of resource utilization, power consumption, and maximum achieved frequency. The results show that for a small length of coefficients like 3-bit, the proposed algorithms work very well in terms of achieved frequency, consumed power, and even resource utilization. Whilst for the length greater than 3-bit, the Pipelined multiplier is much better in frequency than the proposed and conventional ones, and the Booth multiplier consumes fewer resources in terms of lookup tables.

FPGA based area-power-performance analysis of LMS filter using conventional and proposed multipliers

This paper compares LMS filter-based adaptive channel equalizer, implemented on Vertex 7 FPGA using several existing and proposed multipliers. The three main performance parameters taken into consideration are the consumed resources (in terms of lookup tables), utilized power (among various design components), and the performance achieved (in terms of observed frequency). The results show the area-power-performance tradeoff amongst the conventional and proposed multipliers based designs. It is to mention that the proposed multipliers result in more compact systems due to less utilization of resources in all means.

A Correlation-Less Approach Towards Adaptive Channel Equalizer Based on Wiener–Hopf Equation

Wiener, the well-known adaptive filter is seen in almost all the applications of adaptive signal processing. It gets the weight updating by solving the Wiener –Hopf equation (ωopt = R−1P). When implemented on the state of the art hardware, like ACIS or FPGA, this filter results in substantial resource consumption. This paper proposes updating the filter weights by reducing the need for an auto-correlation matrix (R) and calculating the filter weights based on cross-correlation vector (P) only. This approach would result in a noticeable resource reduction by compromising some of the performance that may be acceptable in voice or video being less sensitive. The proposed correlation-less approach and the conventional method are first simulated in Matlab and then is implemented in Xilinx Vertex 7 FPGA for one to one comparison. The results indicate that the proposed approach reduces a significant amount of resources along with acceptable performance characteristics. Therefore, it may be incorporated in various applications of mobile communication, especially the adaptive channel equalizer.

Combined adaptive antenna array and adaptive channel equalizer, based on multichannel fast recursive least squares algorithms

An adaptive antenna array (AAA) is a sort of directional antennas, which is able to suppress the signals of the interference sources. The AAA works well if there is no multipath propagation of the information signal. A device, which removes the multipath effects, is an adaptive equalizer. However, the independent AAA and equalizer do not work well if the interferences and multipath exist simultaneously. The purpose of this paper is to present a new efficient solution of the task by using of the AAA, whose weights are shared with the weights of the fractionally-spaced feed-forward (FF) part of the equalizer. The array output signal is combined with that of the feed-backward (FB) part of the equalizer. The array and such distributed equalizer operate as an indivisible adaptive filter, providing good performance and ability to receive the multipath information signals in the presence of the signals of the external interference sources. Because the FF part of the equalizer operates at the up-sampled rate, which is a few times higher than the information symbols rate, but the FB filter operates at the symbol rate, the computationally efficient fast recursive least squares (RLS) algorithms cannot be used directly in the considered adaptive unit. To solve this problem, it is proposed to use polyphase representation of the FF part of the equalizer. In this case, the architecture of the adaptive unit is viewed as a multichannel adaptive filter with unequal number of weights in channels. The paper presents the architecture of the adaptive unit and the details of its equalizer part. The mathematical description of a multichannel fast adaptive filtering algorithm, called stabilized fast a posteriori sequential technique (FAEST), fitted for the weights calculation of the considered unit is also presented. The verification of the proposed adaptive unit has been carried out via simulation, using a linear array with sixteen omnidirectional antennas, which operates in the conditions of –30 dB signal-to-interference ratio (SIR), 10…30 dB signal-to-noise ratio (SNR), receives the phase shift keying (8-PSK) desired signal, passed through a two-rays communication channel with about –65 dB gaps in the channel amplitude-frequency response. In the steady-state, the proposed adaptive unit demonstrates about –100 dB radiation pattern gaps towards the interference sources and the transient response with less than 500 symbols duration. The simulation demonstrates the ability of the proposed unit simultaneously to steer the main lobe of antenna array radiation pattern towards the desired signal source without a priori search of its angular location and to remove the signals of the external interferences and intersymbol interference in the array output signal. Due to these properties, the proposed AAA/equalizer can be used in equipment of modern mobile communication systems.

Comments on “Design of fractional-order variants of complex LMS and NLMS algorithms for adaptive channel equalization”

The purpose of this note is to discuss some aspects of recently proposed fractional-order variants of complex least mean square (CLMS) and normalized least mean square (NLMS) algorithms in Shah et al. (Nonlinear Dyn. 88(2):839–858, 2017). It is observed that these algorithms do not always converge, whereas they have apparently no advantage over the CLMS and NLMS algorithms whenever they converge. Our claims are based on analytical reasoning and are supported by numerical simulations.

Real time low-complexity adaptive channel equalization for coherent optical transmission systems.

In this paper, a novel low-complexity adaptive channel equalization (ACE) algorithm for digital coherent optical systems is proposed and experimentally demonstrated. We divide the conventional N-tap butterfly ACE into two N-tap polarization independent filters and a 1-tap butterfly adaptive equalization filter. The computational complexity can reduce about 40% of multiplier operations in the digital signal processing (DSP). We evaluate the effectiveness of our proposed ACE algorithm in a 10-Gb/s real-time coherent transmission platform. It is shown that our proposed ACE algorithm has similar performance as conventional ACE algorithm and better polarization tracking ability.

Sigma-Delta Modulation Based Adaptive Channel Equalizer Based on Wiener–Hopf Equations

Recenlty, short word length DSP systems are proposed and reported that they outperform compared to their counterpart multi-bit systems in the sense of area-performance-power analysis. In this continuation, this paper presents the application of sigma-delta modulation (SDM) in the design of adaptive channel equalizer using the Wiener filter for wireless and underwater acoustic communication (UWA). To validate the application, various aspects of design are taken into consideration and comparison is carried out with contemporary (i.e., multi-bit) approach. Besides, FPGA based implementation of SDM based design and conventional approach is also carried out to endorse the proposed algorithm to be used in hardware based implementation. The results given in terms of area -perfromance analysis show that proposed algorithm works as desired and it opens the way of using the sigma-delta modulation in adaptive signal processing domain for UWA that has remained a quite challenging task ever before.

A Communication Method between High-speed UUV and Distributed Intelligent Nodes

In this paper, a doppler distortion compensation method for underwater transmission of OFDM (Orthogonal Frequency Division Multiplexing) signals is proposed, which is used to realize real-time underwater acoustic communication between high-speed UUV (Unmanned Underwater Vehicle) and intelligent nodes. The method decomposes channel variations based on a functions with a set of known equations. OFDM symbol reconstruction is performed after the Taylor(T) FFT (Fast Fourier Transformation) process. We design an adaptive stochastic gradient descent algorithm based on MMSE criterion to learn the combiner weights for differentially coherent detection, thereby achieving adaptive channel equalization in the underwater acoustic channel. Comprehensive data and experimental data from the recent mobile acoustic communication delta are used to demonstrate the feasibility of this approach. The method can realize high-speed underwater acoustic communication, provide data sample basis for long-term big data analysis, and realize real-time communication result analysis.

Analysis of BER Improvement of Adaptive Equalizer using CE and STB code

In a wireless communication channel, when the modulation bandwidth is exceeding the coherence bandwidth ISI takes place as the transmitted pulses are spread into the adjacent symbols. To combat the effects of ISI and noise and to reconstruct the signal and minimize Bit Error Rate (BER), the adaptive channel equalizer is used at the receiver end. Various kinds of interferences in a communication channel exists like Inter symbol Interference, Multipath Interference and Additive Interference which deals with the design of an Adaptive Equalizer. The idea of the equalizer is to build filter in the receiver that counter acts the effect of the channel. In essence, the equalizer must “unscatter” the impulse response and can be stated as the goal of designing the equalizer E so that the impulse response of the combined channel and equalizer CE has a single spike. We investigate channel equalization and introduce hierarchical and adaptive nonlinear channel equalization algorithms that are highly efficient and provide significantly enhanced bit error rate (BER) performance. Due to the high complexity of nonlinear equalizers and poor performance of linear ones, to equalize highly difficult channels, we employ piecewise linear equalizers.

Novel cat swarm optimization algorithm to enhance channel equalization

PurposeAdaptive equalization plays an important role in digital communication to reduce the distortions due to inter-symbol interference. An adaptive filter is used as an equalizer model in channel equalization. An adaptive algorithm is the heart of the adaptive filter which finds the optimum coefficients of the filter. The choice of the adaptive algorithm improves the convergence rate and minimizes the mean square error (MSE). This paper aims to propose a cat swarm optimization (CSO)-based adaptive algorithm and its modification to improve the performance of a channel equalizer.Design/methodology/approachThe input digital training data are transmitted through different channel conditions. A linear transversal filter is used as a channel and equalizer model. The equalizer coefficients are trained by the proposed simplified cat swarm optimization (SCSO) algorithm to find the estimated digital training data.FindingsThe performance of the proposed SCSO algorithm is compared with particle swarm optimization (PSO)-based channel equalization. The improvement in convergence rate and MSE is verified under linear and nonlinear channel conditions with different delay spreads. The optimum parameters of the SCSO are found using simulation-based sensitivity analysis.Originality/valueThis paper analyzes a CSO algorithm for adaptive channel equalization and proposes a SCSO algorithm to identify the optimum coefficients of a transversal equalizer. The seeking mode process is simplified in the proposed SCSO to achieve better performance in channel equalization. The proposed SCSO algorithm guarantees minimum MSE in all independent runs, whereas in PSO, few misses are possible.

Design of fractional-order variants of complex LMS and NLMS algorithms for adaptive channel equalization

Equalization filtering is an effective technique applied to minimize the inter-symbol interference (ISI) in multipath fading channels; the problem gets worse for higher-order constellations which are required for high data rates in today’s communication systems. The least mean square (LMS) filter is a computationally efficient and easily implementable algorithm but suffers from slow convergence; highly complex filters are required to nullify the effects of ISI. In this paper, we develop complex modified fractional-order (FO) nonlinear variants of the LMS and the NLMS algorithms and apply in adaptive channel equalization, in both feed-forward and decision feedback configurations. In addition to the standard first-order derivative, the update in the modified LMS also depends on the FO derivative of the mean square error, the final update is formed using a combination of conventional update term and a nonlinear term obtained through Riemann–Liouville fractional derivative. The step size of the FNLMS scheme in fractional part is not only a function of the input energy but also the FO. The differintegral operator working as differentiator helps improve the convergence rate because the algorithm becomes nonlinear; the fractional algorithms provide more parameters to control the rate of convergence and have simple implementation with almost similar complexity. The performances of the schemes are validated through extensive simulation results for block fading channels (frequency flat and selective) to evaluate the symbol error rate for higher-order quadrature amplitude modulation schemes, mean square error and combined channel and equalizer responses to show the improved inverse modeling of the channel. Simulation experiments confirm the superiority of the proposed algorithm over the traditional counterparts.

Finite dictionary techniques for MSER equalization in RKHS

Adaptive channel equalization is a signal processing technique to mitigate inter-symbol interference in a time dispersive channel. For adaptive equalization, minimum mean square error (MMSE) criterion-based reproducing kernel Hilbert spaces (RKHS) approaches such as the kernel least mean squares (KLMS) algorithm and its variants have been suggested in the literature for nonlinear channels. Another optimality criterion, based on minimum bit/symbol error rate (MBER/MSER), is a better choice for adapting an equalizer as compared to MMSE criterion. A kernel-based minimum symbol error rate (KMSER) equalization algorithm combines minimum symbol error rate (MSER)-based approaches with RKHS techniques. However, most algorithms in RKHS such as KMSER/KLMS require infinite storage requirement and hence cannot be practically implemented. To curtail the infinite memory requirement, and make adaptive algorithm suitable for implementation with finite memory and processing power, we propose quantized KMSER (QKMSER) and fixed-budget quantized KMSER (FBQKMSER)-based equalizers in this paper. In this paper, we derive the dynamical equation for MSE evolution of the QKMSER and FBQKMSER and find their performance to be asymptotically close to the MSE behavior of the KMSER. Also, it is found via simulations that the tracking performance of FBQKMSER is better than all the compared algorithms in this paper which is particularly useful for non-stationary channels.

Single image haze removal based on haze physical characteristics and adaptive sky region detection

Outdoor images are often degraded by haze and other inclement weather conditions, which affect both consumer photographs and computer vision applications severely. Therefore, researchers have proposed plenty of restoration approaches to deal with this problem. However, it is hard to tackle the color distortion problem in restored images with ignoring the differences between fog and haze. Meanwhile, the atmospheric light is also an important variable that influences the global illumination of images. In this paper, we analyze the physical meaning of atmospheric light first, and estimate atmospheric light by a novel method of obtaining the sky region in images, which is based on our newly proposed sky region prior. Then after exploring physical characteristics of fog and haze, we explain why images taken in haze appear yellowish, and eliminate this phenomenon by our adaptive channel equalization method. Quantitative comparisons with seven state-of-art algorithms on a variety of real-world haze images demonstrate that our algorithm can remove haze effectively and keep color fidelity better.

Genetic algorithm assisted DS-UWB BPSK MMSE receiver

Ultra Wide-band (UWB) in recent times draws much consideration as an indoor short range high-speed wireless communication technology for future networks. Direct sequence spread ultra-wide-band (DS-UWB) systems uses extremely short pulses and has the advantage of high path resolution and can get multi-path diversity. Mainly it is useful for indoor communication, which generates many multipaths. The effect of multipath components can be positively used, if rake structured receivers such as selective rake (S-rake) receiver is used. Unfortunately inter-symbol interference (ISI) will become a significant performance limitation when data rate is high. Adaptive algorithms such as Least-Mean-Square (LMS) based channel equalizer aim to minimize the Inter symbol Interference (ISI) present in the transmission channel. However the adaptive algorithms suffer from long training time and undesirable local minima during training mode.A new adaptive channel equalizer using genetic algorithm (GA) is proposed here. This algorithm is suitably used to select the LMS convergence factor of the equalizer. The performance of the proposed channel equalizer is evaluated in terms of mean square error (MSE) and convergence rate and compared with analytical method.