Semi-blind Estimation Research Articles

Blind and Semi-Blind FIR Multichannel Estimation: (Global) Identifiability Conditions

Two channel estimation methods are often opposed: training sequence methods that use the information induced by known symbols and blind methods that use the information contained in the received signal and, possibly, hypotheses on the input symbol statistics but without integrating the information from known symbols, if present. Semi-blind methods combine both training sequence and blind information and are more powerful than the two methods separately. We investigate the identifiability conditions for blind and semi-blind finite impulse response (FIR) multichannel estimation in terms of channel characteristics, received data length, and input symbol excitation modes, as well as number of known symbols for semi-blind estimation. Two models corresponding to two different cases of a priori knowledge on the input symbols are studied: the deterministic model in which the unknown symbols are considered as unknown deterministic quantities and the Gaussian model in which they are considered as Gaussian random variables. This last model includes the methods using the second-order statistics of the received data. Semi-blind methods appear superior to blind and training sequence methods and allow the estimation of any channel with only few known symbols. Furthermore, the Gaussian model appears more robust than the deterministic one as it leads to less demanding identifiability conditions.

Channel estimation for multicarrier multiple input single output systems using the em algorithm

This paper investigates the problem of blindly and semi-blindly acquiring the channel gains for an underdetermined synchronous multiuser multicarrier system. The special case of a multiple-input single-output (MISO) channel is considered where the different users transmit at the same time and in the same bandwidth. In order to separate the different users blindly, techniques exploiting the finite alphabet are used. For such techniques, and for a general underdetermined MIMO system, we study conditions under which the channel and the data for each user are blindly and semi-blindly identifiable. We consider the stochastic maximum likelihood (SML) criterion in which the unknown input symbols are modeled as discrete random variables. We apply the expectation-maximization (EM) algorithm in the frequency domain to get blind and semi-blind channel estimates for each user in the MISO case. We also present a recursive EM solution that updates the channel and noise estimates at each time instant. Simulations show that users can be separated, even at low SNR. Furthermore, semi-blind estimation allows for a more robust estimation solution since a possible singularity problem is avoided.

Blind and semi-blind equalization for generalized space-time block codes

This paper presents a general framework for space-time codes (STCs) that encompasses a number of previously proposed STC schemes as special cases. The STCs considered are block codes that employ arbitrary redundant linear precoding of a given data sequence together with embedded training symbols, if any. The redundancy introduced by the linear precoding imposes structure on the received data that under certain conditions can be exploited for blind or semi-blind estimation of the transmitted sequence, a linear receiver that recovers the sequence, or both simultaneously. Algorithms based on this observation are developed for the single-user flat-fading case and then extended to handle multiple users, frequency-selective fading, as well as situations where the channel is rank deficient, or there are fewer receive than transmit antennas.

Structured semi-blind interference rejection in dispersive multichannel systems

The impact of interference is one of the most capacity limiting factors in cellular networks today. In order to host more users in existing systems and maximize the capacity of future networks, the use of interference suppression techniques are instrumental. We propose a structured semi-blind parameter estimation procedure to support rejection of unknown cochannel interference (CCI) in systems facing multipath channels with non-negligible delay spread. Employing spatio-temporal processing, the proposed scheme exploits the temporal correlation present in the output signal from an antenna array and the training data of a desired user in order to find parameter estimates that fit the range space of a structured CCI model to the signal subspace of the interfering signals. Simulation results illustrating the behavior of a spatio-temporal receiver using the proposed scheme with respect to both signal-to-noise and signal-to-interference ratios (SNRs and SIRs) are presented. In addition, results highlighting the impact of the training sequence length as well as the impact of the channel model order of the CCI users are also included.

Semiblind estimation of spatio-temporal filter coefficients based on a training-like approach

A semiblind approach for equalization and jammers rejection at the base station in wireless communications is presented. It is based on the idea of enlarging the effective length of a known short training sequence by using a few additional information symbols and the finite alphabet property of the communication signals. Simulation results for spatio-temporal processing of 4-QPSK signals transmitted by simultaneous intracell users demonstrate the advantage of the proposed approach in situations where the length of the training sequence is not large enough to allow estimation of the desired signal with previously proposed methods.