## 1 Introduction  
Boosting is a general technique used to increase the accuracy of any given learning algorithm. The Adaptive Boosting (AdaBoost) algorithm, introduced in 1995 by [PERSON] and [PERSON], has been used to solve many of the real-world problems of the earlier boosting algorithms. The AdaBoost algorithm was defined firstly for two class problems, but it can be continued for multi-class and regression problems [1; 2]. When applied to two-class classification problems, AdaBoost has been shown to be extremely successful in creating accurate classifiers. [PERSON] and [PERSON]'s AdaBoost algorithm for classification [3; 4] has attracted much attention in the machine learning community [5] as well as in associated areas in statistics [6; 7; 8]. Different versions of the AdaBoost algorithm have proven to be very competitive in terms of the prediction accuracy in a diversity of applications. Boosting approaches have been proposed as ensemble methods, which depend on the principle of generating multiple predictions and majority voting (averaging) among the individual classifiers. Later, [PERSON] [6; 7] made a path-breaking remark that the AdaBoost algorithm can be seen as a gradient descent algorithm in functional space, inspired by mathematical optimization and statistical estimation. Moreover, [PERSON] et al. [8] laid out more important foundations that related AdaBoost and other boosting algorithms to the framework of statistical estimation and additive basis expansion. In their terminology, boosting is characterized as \"stagewise, additive modeling\". The word \"additive\" doesn't indicate a model fit which is additive in the covariates, but points to the fact that boosting is an additive (in fact, a linear) combination of \"simple\" (function) estimators. Also, [PERSON] et al. [9] and [PERSON] et al. [10] established related ideas that were mostly acknowledged in the machine learning community. In [11], further views on boosting are given; specifically, the authors first pointed out the relation between boosting and L\({}_{1}\) -penalized estimation. The visions of [PERSON] et al. [8] opened new perceptions, namely to use boosting approaches in many contexts other than classification. We refer here to boosting methods for regression (including generalized regression) [12; 13; 14], for density estimation [15], for survival analysis [14; 16], or for multivariate analysis [8; 17]. Reference [18] presents a multidimensional back propagation neural network (BP-NN) based life calculation method that takes into consideration different driving currents and ambient temperatures. Since the well-known neural network (NN) can easily get trapped in local minima and be subjected to low precision, the BP-NN method is improved using the Adaboost algorithm. In some of these opinions, boosting is not only a black-box prediction tool, but it is also an estimation method for models with an explicit structure such as linearity or additivity. Boosting can then be considered as a very interesting regularization structure for estimating a model [12; 16; 19]. The modulation techniques occupy an important position in many applications. In [20], [PERSON] et al. present a boosting algorithm as an ensemble frame to accomplish a higher accuracy than that of a single classifier. To calculate the effect of boosting algorithms, eight common communication signals are yet to be identified and five types of entropy are extracted as the training vector. Then, the AdaBoost algorithm based on a decision tree is used to confirm the idea of the boosting algorithm. The results show that AdaBoost is always a superior classifier. In [21], a multiple-input multiple-output (MIMO) two-way relaying channel (TWRC) with physical layer network coding (PLNC) needs the recognition of a pair of source-modulations from the superposed constellation at the relay. The suggested algorithm is divided into two steps.