Adaptive Channel Identification Research Articles

Sparse adaptive channel identification method using the cross-relation

Cross-relation methods can be used for blind channel identification. The conventional algorithm based on the singular value composition is complex, so variations involving adaptive methods have been developed. These have been extended to algorithms that encourage sparsity in the estimated filter response. In this paper, these methods are applied to complex signals and the method is further extended to tie one of the signals to a particular lag as an anchor constraint, enabling it to be used for source localization in multipath environments. Results demonstrate the performance of these modified cross-relation techniques to source localization.

Analysis and compensation for nonlinear interference of two high-order modulation carriers over satellite link

We introduce analytical characterization of the nonlinear interference that results when passing more than one high-order modulation carrier through the same nonlinear transponder high-power amplifier (HPA). A Volterra filter is proposed which is novel in its implementation of this analytical characterization and modeling of intersymbol interference (ISI) and adjacent channel interference (ACI). Our focus is on adaptive algorithms with pilot-based training so that the solutions are completely blind to unknown transponder HPA characteristics, and can rapidly respond to varying operating back-off level. Furthermore, two families of adaptive solutions are provided to compensate for nonlinear ISI and ACI. The first set performs adaptive channel inversion and then applies equalization. The second set of solutions performs adaptive channel identification and then applies cancellation. The effectiveness of the proposed analysis and techniques is demonstrated via extensive simulations for high-order QAM and APSK modulations. Also included is the coded performance with selected LDPC codes designed for the DVB-S2 standard. Finally, computational complexity is assessed and performance impact is quantified when complexity is reduced by decreasing the number of Volterra coefficients.

Minimum Probability of Error-Based Equalization Algorithms for Fading Channels

Novel channel equalizer algorithms are introduced for wireless communication systems to combat channel distortions resulting from multipath propagation. The novel algorithms are based on newly derived bounds on the probability of error (PE) and guarantee better performance than the traditional zero forcing (ZF) or minimum mean square error (MMSE) algorithms. The new equalization methods require channel state information which is obtained by a fast adaptive channel identification algorithm. As a result, the combined convergence time needed for channel identification and PE minimization still remains smaller than the convergence time of traditional adaptive algorithms, yielding real-time equalization. The performance of the new algorithms is tested by extensive simulations on standard mobile channels.

Adaptive Bayesian decision feedback equaliser for alpha-stable noise environments

In some communication systems the channel noise is known to be non-Gaussian due, largely, to impulsive phenomena. The performance of signal processing algorithms designed under the Gaussian assumption may degrade seriously in such environments. In this paper we investigate the problem of adaptive channel equalisation in an impulsive noise environment. The impulsive interfering noise is modelled as an α-stable process. We first derive the optimum Bayesian decision feedback equaliser and present a novel analytical framework for the evaluation of systems in infinite variance environments. A family of generalised adaptive channel identification algorithms for this infinite variance noise environment is also presented. The combination of a Bayesian equaliser and a channel estimator operating as an adaptive channel equaliser is experimentally studied and its performance is compared with that of a traditional system designed under the Gaussian assumption. The experimental data suggest that the proposed combination of equaliser and channel estimator outperforms the traditionally designed adaptive equaliser in terms of error probability. We finally provide some useful approximations concerning the practical implementation of an α-stable adaptive equaliser.

Unbiased blind adaptive channel identification and equalization

The blind adaptive equalization and identification of communication channels is a problem of important current theoretical and practical concerns. Previously proposed solutions for this problem exploit the diversity induced by sensor arrays or time oversampling, leading to the so-called second-order algebraic/statistical techniques. The prediction error method is one of them, perhaps the most appealing in practice, due to its inherent robustness to ill-defined channel lengths as well as for its simple adaptive implementation. Unfortunately, the performance of prediction error methods is known to be severely limited in noisy environments, which calls for the development of noise (bias) removal techniques. We present a low-cost algorithm that solves this problem and allows the adaptive estimation of unbiased linear predictors in additive noise with arbitrary autocorrelation. This algorithm does not require the knowledge of the noise variance and relies on a new constrained prediction cost function. The technique can be applied in other noisy prediction problems. Global convergence is established analytically. The performance of the denoising technique is evaluated over GSM test channels.