A Hybrid Optimization-Based Technique for Channel Estimation in OFDM System: A Parametric Approach

Abstract

The performance of OFDM system depends on the how efficient channel estimation is, which directly impacts its bit error rate (BER). In this research work, the novel technique of pseudo-pilot aided OFDM system is used its channel estimation with least square (LS) and Recursive Least Squares (RLS) approach. In proposed technique, for channel estimation Pseudo-pilot technique is used in OFDM system over AWGN fading channels. A hybridization technique has been implemented to alleviate the error rate in the signals. The hybrid optimization approach is implemented on the estimated output for the optimum solution. The combination of the two optimization techniques is PSO (particle swarm optimization) and MFO (Moth Flame Optimization) method that is used for optimization of the performance rate. To get more optimum solutions (reducing BER) OFDM system is trained with neural network (NN). The performance of the proposed techniques and the weighted scheme are compared and verified using computerized simulation carried out using Matrix Laboratory (MATLAB) software. The hybrid approach is able to achieve low BER of the network. The BER of PSO is 1.004 x 10 -7 whereas for hybrid optimization (PSO+MFO) BER is 6.275 x 10 -8 at 18 dB SNR. After training of the whole system is done with BPNN which will further reduce the BER while increasing SNR. By using BPNN, BER will further reduce to 1.243 x 10 -8 at 18 dB SNR. It means hybrid optimization is done to optimize the performance of the channel and reduce the BERs, which will help to increase the channel estimation process as well as channel capacity and further reduces the losses while transmission from one end to the other end.

Advanced hybrid algorithms for precise multipath channel estimation in next-generation wireless networks

“…The simulation showed that the proposed CE of the MIMO-OFDM system can significantly result in better BER performance compared with other techniques. Arora and Chawla [7] combined two optimization techniques, the PSO and moth flame optimization (MFO) method, used to optimize the performance rate in the same field. Sohail et al [8] researched the precise and prompt collection of extensive channel state information (CSI).…”