Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  evaluates the performance of OFDM-BPSK and -QPSK system in α-µ distribution. A fading model which is based on the non-linearity present in the propagation medium is utilized here for generation of α-µ variants. Different combinations of α and µ provides various fading distributions, one of which is ibull fading. Investigations of channel estimation schemes gave an idea of further reducing the BER as to improve the performance of OFDM based systems. In flat fading environment, channel estimation is done using phase estimation of the transmitted signal with the help of trained symbols. Final results show the improvement in BER. Hover, the amount of results improved depends upon the amount of trained symbols. The more trained symbols will result into more improved BER.
['Neetu Sood', 'Ajay K. Sharma', 'Moin Uddin']
I am writing a research paper on a idea On Channel Estimation of OFDM-BPSK and -QPSK over Generalized Alpha-Mu Fading Distribution
[{"from": "human", "value": "I am writing a research paper on a idea On Channel Estimation of OFDM-BPSK and -QPSK over Generalized Alpha-Mu Fading Distribution"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  evaluates the performance of OFDM-BPSK and -QPSK system in \u03b1-\u00b5 distribution. A fading model which is based on the non-linearity present in the propagation medium is utilized here for generation of \u03b1-\u00b5 variants. Different combinations of \u03b1 and \u00b5 provides various fading distributions, one of which is ibull fading. Investigations of channel estimation schemes gave an idea of further reducing the BER as to improve the performance of OFDM based systems. In flat fading environment, channel estimation is done using phase estimation of the transmitted signal with the help of trained symbols. Final results show the improvement in BER. Hover, the amount of results improved depends upon the amount of trained symbols. The more trained symbols will result into more improved BER."}]