Abstract
Boosting is a machine learning approach built upon the idea of producing a highly precise prediction rule by combining many relatively weak and imprecise rules. The Adaptive Boosting (AdaBoost) algorithm was the first practical boosting algorithm. It remains one of the most broadly used and studied, with applications in many fields. In this paper, the AdaBoost algorithm is utilized to improve the bit error rate (BER) of different modulation techniques. By feeding the noisy received signal into the AdaBoost algorithm, it is able to recover the transmitted data from the noisy signal. Consequently, it reconstructs the constellation diagram of the modulation technique. This is done by removing the noise that affects and changes the signal space of the data. As a result, AdaBoost shows an improvement in the BER of coherently detected binary phase shift keying (BPSK) and quadrature phase shift keying (QPSK). The AdaBoost is next used to improve the BER of the noncoherent detection of the used modulation techniques. The improvement appears in the form of better results of the noncoherent simulated BER in comparison to that of the theoretical noncoherent BER. Therefore, the AdaBoost algorithm is able to achieve a coherent performance for the noncoherent system. The AdaBoost is simulated for several techniques in additive white Gaussian noise (AWGN) and Rayleigh fading channels so, as to verify the improving effect of the AdaBoost algorithm.
Highlights
Boosting is a general technique used to increase the accuracy of any given learning algorithm.The Adaptive Boosting (AdaBoost) algorithm, introduced in 1995 by Freund and Schapire, has been used to solve many of the real-world problems of the earlier boosting algorithms
This is caused by adding the AdaBoost algorithm that a result, both curves coincide with each other
The AdaBoost algorithm is used to improve the signal-to-noise ratio ratio (SNR) of different modulation techniques. It shows an improvement in the SNR of Binary Phase Shift Keying (BPSK) and quadrature phase shift keying (QPSK)
Summary
Boosting is a general technique used to increase the accuracy of any given learning algorithm. AdaBoost is able to improve the BER, and the simulated BER coincides with that of the theoretical BER curve of each technique This is achieved by using the AdaBoost algorithm to recover the transmitted data from the noisy received signal. It reconstructs the constellation diagram of the modulation technique by removing the noise that is affecting and changing the signal space of the data This leads to an improvement in the BER of coherently detected BPSK and QPSK. Instead of using known phase reference information at the receiver side, such as in the case of coherent detection, noncoherent detection uses the phase information of the prior symbol to detect the current one As it is comparing two noisy signals with each other, the BER of noncoherent detection is estimated to be approximately two times worse than that of the coherent detection.
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