### Related Work  
State-of-the-art channel fading prediction algorithms were reviewed in [9]. Parametric radio channel-based methods [10] consider channel changes faster than multipath parameters, and the estimation of these parameters can help in the extrapolation of the channel into future. However, the effective time of static multipath parameters is inversely proportional to the terminal moving speed, rendering the channel prediction based on radio parameters not appropriate for high-speed scenarios. Autoregressive (AR) model-based methods [11; 13] do not explicitly model physical scatterings, considering the time-varying channel as a stochastic wide-sense stationary (WSS) process and the temporal autocorrelation function is used for prediction. Nevertheless, they are not capable of predicting ephemeral variations in non-wide-sense-stationary (NWSS) channels due to their linear correlation assumption. When treating NWSS channels, many studies attempted to use machine learning in channel prediction. The authors in [14] proposed a backpropagation (BP) framework regarding channel prediction for backscatter communication networks that considers both spatial and frequency diversity. In [15], the authors developed a machine-learning method for predicting a mobile communication channel on the basis of a specific type of convolutional neural network. Although these algorithms can achieve good performance, they need to build neural networks, require a large number of samples for training, and the complexities of these algorithms are high. The channel prediction method based on a support vector machine [16; 23] uses [PERSON]'s theorem [24] to map the channel sample space to the high-dimensional space, and performs linear regression in the high-dimensional space to solve the problem of tracking nonlinear channels well. The kernel recursive least-squares (KRLS) [17; 18; 19; 20; 21; 22] algorithm is a nonlinear version of the recursive least-squares (RLS) algorithm. It can not only solve nonlinear problems, but also adaptively iterate the model parameters.  
In order to solve this problem and render the online kernel method feasible, researchers proposed various sparseness methods or criteria, such as approximate linear dependency (ALD) [17], the novelty criterion (NC) [20], the surprise criterion (SC) [21], and the coherence criterion (CC) [22]. On the basis of these sparseness methods or criteria, only new input samples that meet the prerequisites are added to the dictionary. However, these sparse methods cannot precisely control the size of the kernel dictionary, which motivated us to introduce a sliding window to control the size of the kernel dictionary. Recently, some KRLS-based algorithms with an inserted forgetting factor have achieved better performance than that of QKRLS algorithms [25; 26] and ALD-KRLS, which motivated us to insert a forgetting factor into the proposed SSW-KRLS algorithm. This paper proposes a new sparse sliding-window KRLS algorithm where a candidate discard set is selected through correlation analysis between the mapping vectors in the kernel Hilbert spaces of the new input sample and the existing samples in the kernel dictionary; then, the discarded sample is determined in combination with its corresponding output to achieve dynamic sample updates.