A Novel LS/LMMSE Based PSO Approach for 3D-Channel Estimation in Rayleigh Fading

Abstract

A high transmission rate can be obtained using Multi Input Multi Output (MIMO) Orthogonal Frequency Division Multiplexing (OFDM) model. The most commonly used 3D-pilot aided channel estimation (PACE) techniques are Least Square (LS) and Least Minimum Mean Square (LMMSE) error. Both of the methods suffer from high mean square error and computational complexity. The LS is quite simple and LMMSE being superior in performance to LS providing low Bit Error Rate (BER) at high Signal to Noise ratio (SNR). Artificial Intelligence when combined with these two methods produces remarkable results by reducing the error between transmission and reception of data signal. The essence of LS and LMMSE is used priory to estimate the channel parameters. The bit error so obtained is compared and the least bit error value is fine-tuned using particle swarm optimization (PSO) to obtained better channel parameters and improved BER. The channel parameter corresponding to the low value of bit error rate obtained from LS/LMMSE is also used for particle initialization. Thus, the particles advance from the obtained channel parameters and are processed to find a better solution against the lowest bit error value obtained by LS/LMMSE. If the particles fail to do so, then the bit error value obtained by LS/LMMSE is finally considered. It has emerged from the simulated results that the performance of the proposed system is better than the LS/LMMSE estimations. The performance of OFDM systems using proposed technique can be observed from the imitation and relative results.