Linear adaptive digital filters are applied to various fields for their simplicity in the design and implementation. Considering many kinds of nonlinearities inherent in practical systems, however, nonlinear adaptive filtering will be more desirable. This paper presents a design method for multi-input single-output nonlinear adaptive digital filters using recurrent neural networks. Furthermore, in comparison with this method and the method based on the conventional linear theory, if the proposed method is used, better results can be obtained, and, it is possible that the learning efficiency is improved, because the parallel learning is carried out in this method. Finally, the results of computer simulation are presented to illustrate the effectiveness of the proposed method.

A signal suffers from nonlinear, linear, and additive distortion when transmitted through a channel. Linear equalizers are commonly used in receivers to compensate for linear channel distortion. As an alternative, novel equalizer structures utilizing neural computation have been developed for compensating for nonlinear channel distortion. In this paper, we propose a neural detector based on self-organizing map (SOM) in a 16 QAM system. The proposed scheme uses the SOM algorithm and symbol-by-symbol detector to form a neural detector, and it adapts well to the changing channel conditions, including nonlinear distortions because of the topology-preserving property of the SOM algorithm. According to the theoretical analysis and computer simulation results, the proposed scheme is shown to have better performance than traditional linear equalizer when facing with nonlinear distortion.

This paper presents a compensating method based on Self-Organizing Map (SOM) for nonlinear distortion, which is caused by high-power amplifier (HPA) in 16-QAM-OFDM system. OFDM signals are sensitive to nonlinear distortions and different methods are studied to solve them. In the proposed scheme, the correction is done at the receiver by a SOM algorithm. Simulations are carried out considering an additive white Gaussian (AWG) transmission channel. Simulation results show that the SOM algorithm brings perceptible gains in a complete 16-QAM-OFDM system.

The performance of a twisted-pair channel under ADSL environment is assumed to be dominated by far end crosstalk (FEXT) and additive white Gaussian noise (AWGN). In this paper, we study the channel capacity of the copper twisted pair and the optimum input power spectral density distribution at this channel capacity in the presence of ADSL environment. The channel capacity under different loop length and different input power will also be given. The simulation results show that the distribution of the optimum input power spectral density in the presence of AWGN and FEXT is not uniform. This is different from the situation where AWGN is the only interference, where the input power distribution is approximately uniform.

Iterative decoding is a key feature of turbo code and each decoding results in additional power consumption of the decoder and decoding delay. In this letter, we propose an effective stop criterion based on the Gaussian assumption at the decoder output. Simulation results show that the proposed method can dynamically stop the iterative process with a negligible degradation of the error performance.

In a high-rate indoor wireless personal communication system, the delay spread due to multi-path propagation results in intersymbol interference which can significantly increase the transmission bit error rate (BER). The technique most commonly used for combating the intersymbol interference and frequency-selective fading found in communications channels is the adaptive equalization. In this paper, we propose a novel neural detector based on self-organizing map (SOM) to improve the system performance of the receiver. In the proposed scheme, the SOM is used as an adaptive detector of equalizer, which updates the decision levels to follow the received faded signal. To adapt the proposed scheme to the time-varying channel, we use the Euclidean distance, which will be updated automatically according to the received faded signal, as an adaptive radius to define the neighborhood of the winning neuron of the SOM algorithm. Simulations on a 16 QAM system show that the receiver using the proposed neural detector has a significantly better BER performance than the traditional receiver.

In order to support the increasing amount of mobile data traffic, Third Generation Partnership Project (3GPP) is actively discusses cell range expansion (CRE) and time domain multiplexing – inter-cell interference coordination (TDM-ICIC). They have shown to be attractive techniques for heterogeneous network (HetNet) deployment where pico base stations (BSs) overlay macro BSs. There are two control schemes of the TDM-ICIC. One, named ZP-scheme, stops radio resource assignments for data traffic in predetermined radio resources in the time domain (subframes). The other, named RP-scheme, maintains the resource assignment whereas it reduces the transmission power at macro BSs at predetermined subframes. In this paper, we clarify the effective ranges of both ZP-scheme and RP-scheme by conducting the system level simulations. Moreover, the appropriate power reduction value at predetermined subframes is also clarified from the difference in the effective range of various power reduction values. The comprehensive evaluation results show that both ZP-scheme and RP-scheme are not effective when the CRE bias value is 0 dB or less. If the CRE bias value is larger than 0 dB, they are effective when the ratio of predetermined subframes in all subframes is set to appropriate values. These values depend on the CRE bias value and power reduction in the predetermined subframes. The effective range is expanded when the power reduction in the predetermined subframes changes with the CRE bias value. Therefore, the effective range of RP-scheme is larger than that of ZP-scheme by setting an appropriate power reduction in the predetermined subframes.

Recently, neural networks (NNs) have been extensively applied to many signal processing problem due to their robust abilities to form complex decision regions. In particular, neural networks add flexibility to the design of equalizers for digital communication systems. Recurrent neural network (RNN) is a kind of neural network with one or more feedback loops, whereas self-organizing map (SOM) is characterized by the formation of a topographic map of the input patterns in which the spatial locations (i.e., coordinates) of the neurons in the lattice are indicative of intrinsic statistical features contained in the input patterns. In this paper, we propose a novel receiver structure by combining adaptive RNN equalizer with a SOM detector under serious ISI and nonlinear distortion in QAM system. According to the theoretical analysis and computer simulation results, the performance of the proposed scheme is shown to be quite effective in channel equalization under nonlinear distortion.

Detection of nonlinearly distorted signals is an essential problem in telecommunications. Recently, neural network combined conventional equalizer has been used to improve the performance especially in compensating for nonlinear distortions. In this paper, the self-organizing map (SOM) combined with the conventional symbol-by-symbol detector is used as an adaptive detector after the output of the decision feedback equalizer (DFE), which updates the decision levels to follow up the nonlinear distortions. In the proposed scheme, we use the box distance to define the neighborhood of the winning neuron of the SOM algorithm. The error performance has been investigated in both 16 QAM and 64 QAM systems with nonlinear distortions. Simulation results have shown that the system performance is remarkably improved by using SOM detector compared with the conventional DFE scheme.