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

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

Implementation and analysis of MIMO system based on Alamouti coding

Use of multiple antennas at the receiver and transmitter in a wireless network is a rapidly emerging technology that promises higher data rates at longer ranges without consuming extra bandwidth or transmit power. These systems can be with single-input multiple-output (SIMO), multiple-input single-output (MISO), or with multiple-input multiple-output (MIMO) architectures to improve the signal quality at the receiver using multiple data pipes over a link. MIMO communication system considers multiple antennas used at the transmitting end as well as the receiving end. In addition, the MIMO system has the ability to spread in the spatial domain which is combined in such a way that they either create effective multiple parallel spatial data pipes and diversity to improve the quality (for example decrease the bit error rate). The benefit from the multiple antennas arise from the new dimension space. Hence, the spatial dimension comes as a complement to time. MIMO technology also known as space-time ‘wirelesses’. Space–time block coding (STBC) is a technique used in wireless communications to transmit multiple copies of a data stream across a number of antennas and to exploit the various received versions of the data to improve the reliability of data transfer. The Alamouti coding is a STBC coding technique that is widely use in wireless communication. Alamouti coding can be used in different models like using 2×1 MISO mode or a 2×2 MIMO mode and it can use OFDM system. The purpose of this project is to test a performance of wireless communication system under different type of noise, and the channel model. Also, implementation and analysis of MIMO system based on Alamouti STBC coding.