Channel equalization using simplified least mean-fourth algorithm (original) (raw)
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Adaptive Equalizer Based on a Power-Of-Two-Quantized-LMF Algorithm
eurasip.org
High speed and reliable data transmission over a variety of communication channels, including wireless and mobile radio channels, has been rendered possible through the use of adaptive equalization. In practice, adaptive equalizers rely heavily on the use of the least-mean square (LMS) algorithm which performs sub-optimally in the real world that is largely dominated by non-Gaussian interference signals. This paper proposes a new adaptive equalizer which relies on the judicious combination of the least-mean fourth (LMF) algorithm, which ensures a better performance in a non-Gaussian environment, and the power-of-two quantizer (PTQ) which reduces the high computational load brought about by the LMF and hence renders the proposed lowcomplexity equalizer capable of tracking fast-changing channels. This paper also presents a performance analysis of the proposed adaptive equalizer, based on a new linear approximation of the PTQ. Finally, the extensive simulation carried out here using the quantized LMF corroborates very well the theoretical predictions provided by the analysis of the linearized proposed algorithm.
Equalization of time-varying channels using the quantized least mean-fourth algorithm
10th International Conference on Information Science, Signal Processing and their Applications (ISSPA 2010), 2010
Adaptive equalization is undoubtedly one of the cornerstones of digital communication as it allows for fast and reliable data trans mission over practical channels that are non-ideal and that are plagued with serious problems such as signal dispersion and non Gaussian interferences. In such cases, the performance of the well-known least-mean square (LMS) algorithm or that of any of its variants remains sub-optimal at best. As a remedy to this, the least-mean fourth (LMF) algorithm was later proposed but its high computational load was found to be a constraining factor in its practical implementation, particularly for real-time applications.
Adaptive Channel Equalization by Using Mean Square Error
Journal of emerging technologies and innovative research, 2015
This paper deals with the study of various kinds of interferences in a communication system viz inter symbol interference, cochannel interference and adjacent channel interference during the transmission of signal from the transmitter and received by the receiver in space. We are using the adaptive equalization method in channel equalization to mitigate these interferences by the use of mean square error method and analyze the results through MATLAB.
Adaptive equalization algorithms for optimization of a generalized mean square cost
Information Sciences, 1981
Algorithms described in the literature for adaptation of equalizers usually consider mmimization of a mean square cost. The mean square cost considered is usually comprised of two components; one component is the mean square error which arises because of inexact equihzation of the channel response to the desired response. The other component can be identified as the mean square value of the noise at the output of the equalisor which is generated by channel noise. The paper describes algorithms which enable the two components to be independently weighted and the weighted mean square error nGmized by the adaptive algorithms. Motivation for considering the independent weight is discussed in relation to the use of a compromised Viterbi algorithm receiver for the recovery of digital data transmitted over a noisy diaper&e channel. However, other applications also exist.
An Overview of Adaptive Channel Equalization Techniques and Algorithms
2014
Wireless communication system has been proved as the best for any communication. However, there are some undesirable threats of a wireless communication channel on the information transmitted through it, such as attenuation, distortions, delays and phase shifts of the signals arriving at the receiver end which are caused by its band limited and dispersive nature. One of the threats is ISI (Inter Symbol Interference), which has been found as a great obstacle in high speed communication. Thus, there is a need to provide perfect and accurate technique to remove this effect to have an error free communication. Thus, different equalization techniques have been proposed in literature. This paper presents the equalization techniques followed by the concept of adaptive filter equalizer, its algorithms (LMS and RLS) and applications of adaptive equalization techniques.
ADAPTIVE CHANNEL EQUALIZER FOR WIRELESS COMMUNICATION SYSTEMS
The Data rates and spectrum efficiency of Wireless Mobile Communication have been significantly improved over the last decade or so. Recently, the advanced systems such as 3GPP LTE and terrestrial digital TV broadcasting have been sophisticatedly developed using OFDM and CDMA technology. In general, most mobile communication systems transmit bits of information in the radio space to receiver. The radio channels in mobile radio systems are usually multipath fading channels, which cause inter symbol interference (ISI) in the received signal. To remove ISI from the signal there is a need of strong equalizer which required the knowledge on the channel impulse response. (LMS) Least Mean Square, (RLS) Recursive Least squares and (PSO) Particle swarm optimization algorithms are used to implement the adaptive channel equalizer. The results are measured in terms of mean square error (MSE) and bit error rate (BER) Vs the number of iterations.
ADAPTIVE CHANNEL EQUALIZATION FOR FBMC BASED ON VARIABLE LENGTH STEP SIZE AND MEAN-SQUARED ERROR
Recently, increasing data transmission rates and the demand of more bandwidth at the same time have been a challenge. The trend now is to support high data rates in wireless communications. Multicarrier systems have overcome many challenges of high bandwidth efficiency and at the same time provided also high spectral efficiency. Filter bank multicarrier systems (FBMC) provide some advantages more than the traditional orthogonal frequency division multiplexing (OFDM) with cyclic prefix (CP). FBMC systems provide a much better spectral shaping of the subcarriers than orthogonal frequency division multiplexing (OFDM). Therefore, the most obvious difference between the two techniques is in frequency selectivity. In this paper, we will present a least-meansquare (LMS) algorithm which is based on well-known cost functions, which is the mean-squared error (MSE) adapted for FBMC system with offset QAM modulation (OQAM). This leads to a per-subchannel adaptive equalizer solution with low complexity. The proposed simulations have used practical channel information based on the International Telecommunications Union (ITU) Standards. Moreover, we will discuss how the proposed algorithm will optimize and evaluate the convergence characteristic curves of LMS equalization algorithm per-subcarrier.
— The adaptive algorithm has been widely used in the digital signal processing like channel estimation, channel equalization, echo cancellation, and so on. One of the most important adaptive algorithms is the NLMS algorithm. We present in this paper an multiple objective optimization approach to fast blind channel equalization. By investigating first the performance (mean-square error) of the standard fractionally spaced CMA (constant modulus algorithm) equalizer in the presence of noise, we show that CMA local minima exist near the minimum mean-square error (MMSE) equalizers. Consequently, Fractional Spaced CMA may converge to a local minimum corresponding to a poorly designed MMSE receiver with considerablely large mean-square error. The step size in the NLMS algorithm decides both the convergence speed and the residual error level, the highest speed of convergence and residual error level.
CHANNEL EQUALISATION USING ADAPTIVE ALGORITHM
The of the major practical problems in digital communication systems is channel distortion which causes errors due to intersymbol interference. Since Decision feedback equalizers are used extensively in practical communication systems. They are more powerful than linear equalizers especial y for severe inter -symbol interference (ISI) channels without as much noise enhancement as the linear equalizers. This thesis addresses the problem of adaptive channel equalization in environments where the interfering noise exhibits Gaussian behavior. In this thesis, radial basis function (RBF) network is used to implement DFE. Advantages and problems of this system are discussed and its results are then compared with DFE using multi layer perceptron net (MLP).Results indicate that the implemented system outperforms both the least-mean square(LMS) algorithm and MLP, given the same signal-to-noise ratio as it offers minimum mean square error. The learning rate of the implemented system is also faster than both LMS and the multilayered case.