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Kernels Pruning for Volterra Digital Predistortion Using Sparse Bayesian Learning

In this paper, we present a pruning approach using sparse Bayesian learning (SBL) for Volterra series (VS) based digital predistortion (DPD) in broadband wireless communication system. The sparsity assumption for the kernel coefficients of the full VS behavioral model is analyzed firstly. Then, from the view of probability, the SBL is applied to extract the active kernels of VS-DPD by exploiting the learning procedure of hyperparameters that control the sparseness of VS kernels. With this methodology, the number of VS kernels required by the DPD architecture construction can be significantly reduced at least by 79.2% and the orthogonality requirement of measurement matrix is also relaxed in the pruning process. Moreover, even under lower size of training samplings, the proposed SBL-VS-DPD can achieve better considerable accuracy performance than orthogonal matching pursuit based DPD. The simulation results show that the proposed scheme reduces the nonlinearity of power amplifiers significantly, and exhibits good trade-off between model complexity and linearization capabilities, which demonstrates the validity of the SBL in kernels pruning of VS-DPD.