Abstract
We propose a statistical covariance-matching based blind channel estimation scheme for zero-padding (ZP) multiple-input 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.
Highlights
Orthogonal frequency division multiplexing (OFDM) combined with guard intervals, in the form of cyclic prefix (CP) or zero-padding (ZP), is an effective transmission scheme through frequency selective fading channels [1]
We will focus on blind estimation of ZP-based multipleinput multiple-output (MIMO)–OFDM systems
By exploiting the block Toeplitz channel matrix structure, we show that the linear equations relating the entries of the received covariance matrix and the products of the channel matrix taps can be rearranged into decoupled groups
Summary
Orthogonal frequency division multiplexing (OFDM) combined with guard intervals, in the form of cyclic prefix (CP) or zero-padding (ZP), is an effective transmission scheme through frequency selective fading channels [1]. For ZP-based single-input single-output (SISO) OFDM systems, a subspace algorithm is proposed to blindly identify the channels in [20], and is generalized to MIMO cases [21]. This approach is known to suffer a sever performance degradation when the signal-to-noise ratio (SNR) is low or moderate [5]. The problem we study in this article is blind estimation of the MIMO channel matrix taps H(m), 0 ≤ m ≤ L, by using second-order statistics of the received data.
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