Search citation statements
Paper Sections
Citation Types
Year Published
Publication Types
Relationship
Authors
Journals
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.
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.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.