A Genetic Algorithm–Sparse Bayesian Joint Optimization Modeling Approach for Efficient Predistortion

doi:10.1109/LMWT.2025.3596000

A Genetic Algorithm–Sparse Bayesian Joint Optimization Modeling Approach for Efficient Predistortion

2025-08-13

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DOI：10.1109/LMWT.2025.3596000

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The predistortion model represented by the Volterra series can effectively compensate for the nonlinearity of power amplifiers (PAs). Traditional models require a large amount of samples to solve for the polynomial coefficients, which makes it difficult to meet the demands of green communication. Therefore, this letter proposes an improved genetic algorithm (GA) to select the smallest training sample subset that preserves the model’s features. However, due to the nonorthogonality of the basis functions, ill-conditioned matrix problems are likely to occur during few-sample learning (FSL). To address this, Bayesian learning is applied before sample selection to sparsify the basis functions. This not only further reduces the dimensionality of the polynomial but also accelerates the update process of the GA fitness function. Experimental results show that the convergence speed of this joint optimization strategy is 66% faster than that of the traditional GA, and even with an 80.5% reduction in training data, it can still achieve accuracy comparable to that of full-sample fitting.

Keywords:

Digital predistortion (DPD)

genetic algorithm (GA)

nonlinearity

power amplifiers (PAs)

sparse Bayesian learning (SBL)

A Genetic Algorithm–Sparse Bayesian Joint Optimization Modeling Approach for Efficient Predistortion

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