Statistical covariance-matching based blind channel estimation for zero-padding MIMO–OFDM systems

Abstract

We propose a statistical covariance-matching based blind channel estimation scheme for zero-padding (ZP) multipleinput multiple-output (MIMO)-orthogonal frequency division multiplexing (OFDM) systems. By exploiting the block Toeplitz channel matrix structure, it is shown that the linear equations relating the entries of the received covariance matrix and the outer product of the MIMO channel matrix taps can be rearranged into a set of decoupled groups. The decoupled nature reduces computations, and more importantly guarantees unique recovery of the channel matrix outer product under a quite mild condition. Then the channel impulse response matrix is identified, up to a Hermitian matrix ambiguity, through an eigen-decomposition of the outer product matrix. Simulation results are used to evidence the advantages of the proposed method over a recently reported subspace algorithm applicable to the ZP-based MIMO-OFDM scheme.

Quality Analysis for Real-Time Data in MIMO Communication Using Adaptive Kalman Filtration

“…An estimation approach based on channel estimation and superimposition of training sequence on the MIMO-OFDM system is outlined in [8]. The signal boundness losses to the actual value with the increase in block length in such system, to obtain minimum channel interference in block fading channels as presented in [9], a random upper bound [10,11] and a lower bound coding [12], with instantaneous channel capacity developed for the improvement of MIMO system. As the upper and lower bound converges at the channel outage probability for large MIMO blocks, the semi-blind approach of signal estimation based on frequency domain analysis outlined in [13], wherein timing synchronization process is developed for the signal estimation in channel variant condition.…”

A Survey on MIMO-OFDM Systems: Review of Recent Trends

“…In [125], a blind scheme for a zero-padding MIMO-OFDM system, based on the covariance statistics, was presented. The channel matrix was obtained by resolving an equation series resulting from the covariance matrix of the received signal by exploiting the block Toeplitz structure.…”