## 1 Introduction
The first stage uses the higher order statistics based features in conjunction with a genetic algorithm as a features-selection method, while the second one employs AdaBoost as a classifier. The obtained simulation results show the capability of the proposed algorithm to provide a decent recognition performance at acceptable signal-to-noise values. Since the publishing of [21], the AdaBoost technique has been used in this line of work with noncoherent receivers for binary phase shift keying (BPSK) and quadrature phase shift keying (QPSK) modulation. It is important to include a brief survey about other noncoherent modulation schemes such as differential chaotic shift keying (DCSK), noise reduction DCSK (NR-DCSK), short reference-DCSK (SR-DCSK), permutation index DCSK (PI-DCSK) and multiuser orthogonal frequency division multiplexing DCSK (OFDM DCSK). The idea of considering the DCSK systems under pulse jamming environments was shown in [22], where the system was examined under slow- and fast-switching pulse jamming environments. A new noncoherent scheme called PI-DCSK modulation was proposed in [23]. This inventive design scheme aims to improve data security, energy, and spectral efficiencies compared to its competitors. The NR-DCSK modulation was used in [24]. The antijamming performance of the NR-DCSK system was studied. Practical jamming environments were taken into consideration including broadband jamming (BBJ), partial-time jamming (PTJ), tone jamming (TJ), and sweep jamming (SWJ). In [25], SR-DCSK was used to overcome the main drawbacks related to the low data rate and energy efficiency fondness of conventional DCSK systems. In [26], a multiuser OFDM-DCSK modulation was successfully established. It is a recent technique used to overcome the disadvantage of the OFDM. The target of OFDM is to increase spectral and energy efficiencies by letting multiple access transmissions decrease the complexity using inverse fast Fourier transform/fast Fourier transform (IFFT/FFT) operations instead of parallel matched filters. In this paper, the AdaBoost algorithm is used to improve the bit error rate (BER) of the different modulation techniques (BPSK and QPSK). For coherent detection, 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. Consequently, 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. Moving on to the next step, the AdaBoost is used to improve the BER of the noncoherent detection of the used modulation technique. The term noncoherent refers to detection of the received signal without the knowledge of the signal's phase. 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, consequently, the BER of noncoherent detection is estimated to be approximately two times worse than that of the coherent detection. Since the noncoherent system is worse in performance than the coherent one, when the AdaBoost algorithm is added, it improves the noncoherent detection's BER. This helps to improve the noncoherent detection of the receiver without adding more complexity, which was originally the advantage (i.e. lower complexity) of the noncoherent over the coherent detection. The AdaBoost algorithm is able to achieve a coherent performance for the noncoherent system. The proposed system is simulated for different techniques in additive white Gaussian noise (AWGN) and Rayleigh fading channel to verify the improving effect of the AdaBoost algorithm.