Wideband Channel Estimation and Prediction in Single-Carrier Wireless Systems

Abstract—In this contribution wideband channel estimation and prediction designed for single-carrier wideband wireless communications systems are investigated. Specifically, the single-carrier wideband pilot signal received by the receiver is first converted to the frequency-domain. Then, the envelope of the channel transfer function (CTF) is estimated in the frequency-domain, in order to reduce the effects of background noise on the channel prediction step to be invoked. Finally, channel prediction is carried out based on the estimated CTF in the frequency-domain, where a Kalman filter assisted long-range channel prediction algorithm is employed. Our simulation results show that for a reasonable signal-to-noise ratio (SNR) value the proposed frequency-domain based wideband channel estimator is capable of efficiently mitigating the effects of the background noise, hence enhancing the performance of wideband channel prediction

Recurrent Neural Network Based Narrowband Channel Prediction

In this contribution, the application of fully connected recurrent neural networks (FCRNNs) is investigated in the context of narrowband channel prediction. Three different algorithms, namely the real time recurrent learning (RTRL), the global extended Kalman filter (GEKF) and the decoupled extended Kalman filter (DEKF) are used for training the recurrent neural network (RNN) based channel predictor. Our simulation results show that the GEKF and DEKF training schemes have the potential of converging faster than the RTRL training scheme as well as attaining a better MSE performance

Spatial Wireless Channel Prediction under Location Uncertainty

Spatial wireless channel prediction is important for future wireless networks, and in particular for proactive resource allocation at different layers of the protocol stack. Various sources of uncertainty must be accounted for during modeling and to provide robust predictions. We investigate two channel prediction frameworks, classical Gaussian processes (cGP) and uncertain Gaussian processes (uGP), and analyze the impact of location uncertainty during learning/training and prediction/testing, for scenarios where measurements uncertainty are dominated by large-scale fading. We observe that cGP generally fails both in terms of learning the channel parameters and in predicting the channel in the presence of location uncertainties.\textcolor{blue}{{} }In contrast, uGP explicitly considers the location uncertainty. Using simulated data, we show that uGP is able to learn and predict the wireless channel

Dynamic Models and Nonlinear Filtering of Wave Propagation in Random Fields

In this paper, a general model of wireless channels is established based on the physics of wave propagation. Then the problems of inverse scattering and channel prediction are formulated as nonlinear filtering problems. The solutions to the nonlinear filtering problems are given in the form of dynamic evolution equations of the estimated quantities. Finally, examples are provided to illustrate the practical applications of the proposed theory.Comment: 12 pages, 1 figur

Information Flow through a Chaotic Channel: Prediction and Postdiction at Finite Resolution

We reconsider the persistence of information under the dynamics of the logistic map in order to discuss communication through a nonlinear channel where the sender can set the initial state of the system with finite resolution, and the recipient measures it with the same accuracy. We separate out the contributions of global phase space shrinkage and local phase space contraction and expansion to the uncertainty in predicting and postdicting the state of the system. Thus, we determine how the amplification parameter, the time lag, and the resolution influence the possibility for communication. A novel representation for real numbers is introduced that allows for a visualization of the flow of information between scales.Comment: 14 pages, 13 figure