A low-complexity channel training method for efficient SVD beamforming over MIMO channels

Abstract Singular value decomposition (SVD) beamforming is an attractive tool for reducing the energy consumption of data transmissions in wireless sensor networks whose nodes are equipped with multiple antennas. However, this method often not practical due to two important shortcomings: it requires channel state information at transmitter and computation SVD matrix generally too complex. To deal these issues, we propose a establishing link without requiring feedback actual or coefficients transmitter. Concretely, our takes advantage reciprocity power iteration algorithm (PIA) determining precoding decoding singular vectors from received preamble sequences. A low-complexity version that performs no iterations proposed shown have signal-to-noise-ratio (SNR) loss within 1 dB bit error rate least squares estimates. The significantly outperforms maximum ratio combining diversity Alamouti coding. We also show computational cost PIA-based less than one using Golub–Reinsch obtaining SVD. number computations order magnitude smaller Golub–Reinsch. This difference grows further antenna array size.