Fast Convergence on Blind and Semi-Blind Channel Estimation for MIMO–OFDM Systems

Abstract

In this paper, blind and semi-blind subspace channel estimations with fast convergence rate are proposed for multiple-input multiple-output orthogonal frequency division multiplexing (OFDM) systems in the presence of virtual carriers. The subspace method has a main drawback of slow convergence rate when deriving the noise subspaces from the second-order statistics of received signals. This phenomenon is especially evident when the size of received signals is large. Inspired by this fact, we present a block matrix scheme (BMS) to generate a group of sub-vectors from each OFDM symbol when virtual carriers are used. The number of equivalent signals is increased and therefore, the convergence rate of channel estimation is enhanced. The semi-blind method is also investigated by incorporating subspace technology with least square scheme. The identifiability of the BMS-based channel estimation is analyzed to derive the applicable range of the BMS size. The computational complexity of the proposed channel estimation is calculated at the end. Computer simulations show that the proposed blind and semi-blind methods converge very well in channel estimation and equalization.

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“…However, due to the necessity for a considerable number of samples for accurate autocorrelation matrix estimation, the algorithm ©International Telecommunication Union, 2024 converges slowly. To address this issue, [62] proposed to use a small sliding window over the received OFDM symbols to increase the number of equivalent symbols and use them to estimate the autocorrelation matrix. This increases the convergence speed at the cost of accuracy.…”

“…For the interference cancellation problem, we exploit the ideas provided in [61] and [62] to come to an approach that quickly converges while preserving the accuracy of the estimation.…”

“…In Fig. 13, the simulation results are shown for the interference cancellation achieved using the modi ied blind channel estimation algorithm compared to [61] and [62].…”

“…The scenario consists of a single BS and two reference antennas. Here, 𝐽 refers to the window size used for the calculation of the autocorrelation matrix in terms of OFDM symbols in [61] and 𝐺 refers to the window size used for the calculation of autocorrelation matrix in terms of samples in [62]. 𝛾 𝑥𝑠 refers to the 5G signal-to-satellite-signal ratio.…”

“…The drawback with 𝐽 = 1 is that the interference reduction for a long coherence time is significantly degraded. The method in [62] increases the number of OFDM blocks even further by partitioning the received signal into smaller blocks. In this case, the number of OFDM blocks used for the calculation of autocorrelation matrix is increased even further compared to 𝐽 = 1.…”