Performance Analysis of Linear and Non-Linear Equalizer in Rician Channel

Agarwal, Amit; Sur, Samarendra Nath; Singh, Arun Kumar; Gurung, Hemanth; Gupta, Abhishek Kumar; Bera, Rabindranath

doi:10.1016/j.protcy.2012.05.111

Cited by 7 publications

(5 citation statements)

References 11 publications

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“…The last stage in the proposed receiver is equalization, in which the estimate of the SI-free signal, r[n], is equalized with the purpose of eliminating the multipath effects of the remote channel from the data symbols. In the proposed receiver, we use a decision feedback equalizer (DFE), which is a nonlinear equalizer; by employing a feedforward filter (FFF) and a feedback filter (FBF), a DFE outperforms the linear zero forcing (ZF) and minimum mean squared error (MMSE) equalizers in frequency selective channels [12]. In a DFE, the FFF with length L acts as linear equalizer for the first L paths of the channel and the FBF removes the inter-symbol-interference caused by the rest of the paths and the FFF.…”

Section: Equalization Stagementioning

confidence: 99%

An Adaptive Receiver for Underwater Acoustic Full-Duplex Communication with Joint Tracking of the Remote and Self-Interference Channels

Towliat

Guo

Cimini

et al. 2021

Preprint

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Full-duplex (FD) communication is a promising candidate to address the data rate limitations in underwater acoustic (UWA) channels. Because of transmission at the same time and on the same frequency band, the signal from the local transmitter creates self-interference (SI) that contaminates the the signal from the remote transmitter. At the local receiver, channel state information for both the SI and remote channels is required to remove the SI and equalize the SI-free signal, respectively. However, because of the rapid time-variations of the UWA environment, real-time tracking of the channels is necessary. In this paper, we propose a receiver for UWA-FD communication in which the variations of the SI and remote channels are jointly tracked by using a recursive least squares (RLS) algorithm fed by feedback from the previously detected data symbols. Because of the joint channel estimation, SI cancellation is more successful compared to UWA-FD receivers with separate channel estimators. In addition, due to providing a real-time channel tracking without the need for frequent training sequences, the bandwidth efficiency is preserved in the proposed receiver.

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Section: Equalization Stagementioning

confidence: 99%

An Adaptive Receiver for Underwater Acoustic Full-Duplex Communication with Joint Tracking of the Remote and Self-Interference Channels

Towliat

Guo

Cimini

et al. 2021

Preprint

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“…It uses a past symbol to estimate a present symbol. As shown the figure 1, the input terminal has been connected to FFF and the output terminal has been connected to FBF [3][4]. In order to update equalizer's coefficients, RLS algorithm can be used [5].…”

Section: A Decision Feedback Equalizermentioning

confidence: 99%

Optimization of decision feedback equalizer tap-length using genetic algorithm

Son

Hwang

Kim

et al. 2015

2015 Seventh International Conference on Ubiquitous and Future Networks

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In this paper, we propose an method which is optimizing length of the equalizer taps using genetic algorithm (GA). The equalizer is a decision feedback equalizer (DFE). Feedforward filter length and feed-back filter length are regarded as genetic information in GA. The GA uses different random crossover methods. It finds out the optimized DFE tap length for low bit error rate BER. There are two methods for BER calculation in the object function of GA; first, it is to use all data of preamble and payload in order to calculating BER; second, it is merely to use preamble data for communication.

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“…Consider the development where K b and K w are the feedback and feedforward filter coefficients derived in minimum-mean-square-sense, by making error orthogonal to the received sequence. Y(t) represents the received signal, the channel input data symbol as K x , and the channel-impulse response as h(t) where n(t) is additive-white Gaussian noise and T is the symbol duration [13].…”

Section: Derivation Of Dfe-mmsementioning

confidence: 99%

Performance Analysis of Non-Linear Equalizer in MIMO System for Vehicular Channel

Tiwary¹,

Agarwal²,

Sur³

2013

IJIGSP

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All wireless technologies face the challenges of multipath signal fading, attenuation delay and phase delay which led to the interference between users and there is the possibility of limited spectrum. Linear and Non-Linear receiver is used to combat the effect of multipath signal fading and delay. The linear receiver gives best result in case of static environment but in case of dynamic environmental condition it fails to give better results and hence in order to improve the system performance non-linear receiver is used in dynamic environment condition. As a dynamic channel, Vehicular Channel model is considered because there is growing interest in vehicular networking and it is also a challenging channel model because of the complexity of the environment, and rapid variation in channel conditions. This paper studies the comparison between Zero Forcing (ZF) and Minimum Mean Square Error (MMSE) receiver in the Vehicular Channel. A comparative study between linear equalizer and non-linear equalizer in the Vehicular Channel is done and analyze the effect of the varying modulation and antenna configuration on the performance

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Performance Analysis of Linear and Non-Linear Equalizer in Rician Channel

Cited by 7 publications

References 11 publications

An Adaptive Receiver for Underwater Acoustic Full-Duplex Communication with Joint Tracking of the Remote and Self-Interference Channels

An Adaptive Receiver for Underwater Acoustic Full-Duplex Communication with Joint Tracking of the Remote and Self-Interference Channels

Optimization of decision feedback equalizer tap-length using genetic algorithm

Performance Analysis of Non-Linear Equalizer in MIMO System for Vehicular Channel

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