Channel Estimation Algorithms and Bit Error Rate Analysis of Cognitive Radio

This paper mathematically proposes channel estimation algorithms for Cognitive Radio (CR) in terms of Mean Square Error (MSE) and analysis of its Bit Error Rate (BER) performance. As cognitive radio associates itself with the family of opportunistic communication for using frequency spectrum efficiently, issues such as spectrum sensing and channel estimation gain attention. When the main issue of spectrum sensing is accomplished through different methods, information needs to be transferred in cognitive radio scenario. The issue of channel estimation is important for information transfer and it plays a role in data detection. Channel estimation is addressed in this paper and the channel estimation algorithms proposed for estimating the wireless channel coefficients in a CR scenario are Least Squares (LS) and Linear Minimum Mean Square Error (MMSE) approaches. Though, LS and LMMSE are existing algorithms, there is no such specific research paper which presents simulation results of MSE performance of both the algorithms for Cognitive Radio. Hence, the unique contribution of this research paper is to derive the channel estimation algorithms for estimating wireless channel coefficients, obtain simulation results in terms of MSE performance and use the estimated coefficients in analyzing BER performance of CR environments. Simulation results show that the LMMSE algorithm shows better MSE performance in comparison to LS algorithm in lower signal to noise ratio (SNR) values and same MSE performance in higher signal to noise ratio values. This performance in higher SNR is due to characteristics of the training sequences used for estimation. To verify the correctness of the claims made in this research paper theoretical Mean Square Error performance is derived to validate channel estimation. The results shown in this paper can be helpful for researchers working in the community of Cognitive Radio and can act as platform for many pioneering research works to evolve in near future.