Nonlinear MIMO communication systems : channel estimation and information recovery using Volterra models

Note that most classical massive MIMO channel estimation methods assume a similar physical model, but discretize a priori the DoDs and DoAs, so that the problem fits the framework

maximum ratio combining (MRC) and zero-forcing (ZF) re- ceivers on the uplink (UL) in the large system limit. It has been shown in [1] that a linear receiver can be used to

In contrast to that we propose a sparse recovery method which relies on decomposing the multidimensional problem into successive one-dimensional parameter estimation problems, which

Motivated by the iterative power method, used in Numerical Analysis for estimating singular values and singular vectors, we develop RLS and LMS subspace based adaptive algorithms

The first step involves a large SVD in order to obtain a base for the noise subspace of the received signal, and the second consists in applying either a small SVD [8] or

The algorithm has been designed to work both with parametric L -path channel model (with known path delays) and equivalent discrete-time channel model to jointly estimate the

In this paper, we present a new iterative algorithm for joint Rayleigh channel (taps or complex gains) estimation and data recovery in fast time-varying environments.. After

Joint Carrier Frequency Offset and Fast Time-varying Chan- nel Estimation for MIMO-OFDM Systems.. CSNDSP 2010 - 7th IEEE IET International Sumposium on Communication Systems,