Nonlinear Adaptive Equalizer Research Articles

An Efficient ANN Interference Cancelation for High Order Modulation over Rayleigh Fading Channel

High order modulation (HOM) presents a key challenge in increasing spectrum efficiency in 4G and upcoming 5G communication systems. In this paper, two non-linear adaptive equalizer techniques based on multilayer perceptron (MLP) and radial basis function (RBF) are designed and applied on HOM to optimize its performance despite its high sensitivity to noise and channel distortions. The artificial neural network’s (ANN) adaptive equalizer architectures and learning methods are simplified to avoid more complexity and to ensure greater speed in symbol decision making. They will be compared with the following popular adaptive filters: least mean square (LMS) and recursive least squares (RLS), in terms of bit error rate (BER) and minimum square error (MSE) with 16, 64, 128, 256, 512 and 1024 quadrature amplitude modulation (QAM). By that, this work will show the advantage that the MLP equalizer has, in most cases, over RBF and traditional linear equalizers.

Design of Complex Non-Linear Adaptive Equalizer in Mitigating Severe Intersymbol Interferences

In this paper, we propose an efficient architectural design for Adaptive Decision Feedback Equalizer (ADFE) for high-speed reliable data transmission through wired as well as wireless communication channels to minimize Inter Symbol Interference (ISI). High hardware cost of the system makes an urge to redesign it in order to minimize overall circuit complexity and cost. Realization of CORDIC based pipelined ADFE architecture using reformulated LMS algorithm has not only reduced multipliers in its feed forward path, the flexibility and modularity of the components makes it possible for easy implementation also. The realization results area efficient, high speed and throughput with good convergence. The efficacy of the proposed equalizer is corroborated with MATLAB simulations for mitigation of severe channel distortion arise due to ISI in high speed communication systems.

Nonlinear Adaptive Equalizer Using a Pipelined Decision Feedback Recurrent Neural Network in Communication Systems

In this letter, a novel pipelined decision feedback RNN equalizer (PDFRNE) with low computational complexity is proposed. Since each module is a DFRNN with the decision feedback structure so that it can eliminate the past error remaining in the network. Moreover, the performance can be further improved. At the same time, it can overcome the unstableness due to its nature of the infinite impulse response (IIR) structure.

Adaptive nonlinear channel equalization based on combination neural network for chaos-based communication systems

For nonlinear channel distortions of chaos-based communication systems，based on the analysis of the theory of chaotic signal reconstruction and the characteristics of transversal filter and functional link neural network (FLNN)，a novel nonlinear adaptive equalizer with the architecture of combination of transversal filter and functional link neural network (CFFLNN) is proposed in this paper. The novel nonlinear equalizer fully utilizes faster convergence characteristics of transversal filter and the nonlinear approximation capability of FLNN by function expansion due to enhanced input space. Furthermore，the performance of the novel nonlinear adaptive equalizer is also improved. Finally，the proposed equalizer is designed and its adaptive algorithm is deduced by the low complexity normalized least mean square (NLMS) method. And an analysis of stability and convergence for the derived algorithm is provided. To illustrate the analysis，results obtained from the computer simulation are also provided for both linear and nonlinear channels in chaos-based communication system.