Performance of multi level‐turbo coding with neural network‐based channel estimation over WSSUS MIMO channels

Abstract

SUMMARYThis paper presents the performance of the transmit diversity-multi level turbo codes (TD-MLTC) over the multiple-input-multiple-output (MIMO) channels based on the wide sense stationary uncorrelated scattering (WSSUS). The multi level-turbo code (ML-TC) system contains more than one turbo encoder/decoder block in its structure. At the transmitter side, the ML-TC uses the group partitioning technique that partitions a signal set into several levels and encodes each level separately by a proper component of the encoder to improve error performance. The binary input sequence is passed through the MLTC encoder and mapped to 4-PSK and then fed into the transmit diversity scheme for high data transmission over wireless fading channels. At the receiver side, distorted multi-path signals are received by multiple receiver antennae. WSSUS MIMO channel parameters are estimated by using an artificial neural network and an iterative combiner. Input sequence of the first level of the MLTC encoder is estimated at the first level of MLTC decoder. Subsequently, the other input sequences are computed by using the estimated input bit streams of the previous levels. 4-PSK two-level turbo codes are simulated for 2T x −1R x and 2T x −2R x antenna configurations over WSSUS MIMO channels. Here, TD-MLTC and its efficient implementations are discussed and simulation results are given.

Physical statistical modelling of MIMO channel based on Rician distribution

“…As known, the more precise of the characteristics of MIMO channel, the more accurate the assessment on system performance [1][2]. So how to model is important.…”