Reduce complexity computation of MIMO decomposition based on single RF using Kalman filter

Multiple Input Multiple Output (MIMO) systems based on single radio frequency (RF) receiver have been shown to achieve high data rates due to the exploitation of spatial diversity with the use of multiple antennas at the transmitters and/or receivers. However, exploiting the full multiplexing gain of the MIMO channel requires a RF front end and analog to digital converters for each antenna to process each data stream at baseband, which implies an increase in hardware size, cost computation, and power consumption with respect to single-input-single-output (SISO) systems. This technique can be great, which we can not get the additional diversity gain using the linear MIMO decomposition method. The resulting performance trends showed a significant increase when compared to MIMO-OFDM schemes using conventional antenna. But the complexity of computational cost is high, especially for the larger antenna size. For that we propose to reduce the complexity of computation cost hence technology of a RF signal processing for MIMO-OFDM can reliable applied. In this research we proposes a technique to reduce complexity of computational cost using Kalman filter as MIMO equalizer. In this technique we perform the reduction of the larger matrix size into lower matrix size. In additional part we use an error correction coding (ECC) technique using convolution code and Viterbi algorithm decoding applied at receiver side. Computer simulation result showed that our proposed scheme give low computation.