Channel estimation based on learning automata for OFDM systems

Abstract

Summary The abundant benefits of Orthogonal Frequency‐Division Multiplexing and its high flexibility have resulted in its widespread applications in many telecommunication standards. One important parameter for improving wireless system's efficiency is the accurate estimation of channel state information. In the literatures, many techniques have been studied in order to estimate the channel state information. Nowadays, the techniques based on intelligent algorithms such as genetic algorithm and particle swarm optimization (PSO) have attracted attention of researchers. With a very low pilot overhead, these techniques are able to estimate the channel frequency response properly only using the received signals. Unfortunately, each of these techniques suffers a common weakness: they have a slow convergence rate. In this paper, a new intelligent and different method has been presented for channel estimation using learning automata, entitled LA estimator, where the learning automata are search agents, and each pair is responsible for searching 1 complex coefficient of the channel frequency response. This method can achieve an accurate channel estimation with a moderate computational complexity in comparison with GA and PSO estimators. Furthermore, with higher convergence rate, our proposed method is capable of providing the same performance as GA and PSO. For a 2‐path fast fading channel, simulation results demonstrate the robustness of our proposed scheme according to the bit error rate and the mean square error.

Comparing Various Channel Estimation Techniques for OFDM Systems Using MATLAB

“…Salehi et. al [18] introduced a new intelligent and different method for channel estimation using learning automata, entitled LA estimator, where the learning automata are search agents, and each pair is responsible for searching one coefficient of the channel frequency response. The method can attain accurate channel estimation with a moderate computational complexity in comparison with genetic algorithms (GA) and particle swarm optimization (PSO) estimators.…”

Capacity and BER performance improvement in integrated MIMO‐OFDM system using optimal power allocation, channel estimation, and turbo coding

“…Bouanani and Bessate 21 investigated the analytical performance of OSTBC MIMO system and claim that this technique provides more efficient pattern over various fading channels. Learning automate (LA) estimator can achieve better channel estimation with moderate CC over genetic and particle swarm optimization estimators 22 …”

A Proposed Channel Estimation Based on Enhanced Sub-carrier Index Modulation and Packet-Discrete Wavelet Transform to Minimize Bit Error Rate

“…There are also some recent developments for wavelet-based OFDM systems discussed in [18]. The reference [19] compares the BER performance of a multi-wavelet-based OFDM system to DFT and Haar-wavelet based on OFDM systems. The proposed and compare the performance of BER for wavelet-based OFDM systems based on Haar, Daubechies, symlets, and coiflets with Doppler shifts of 0.005 and 0.05 Hz [20].…”