Block-Weighted Lasso for Joint Optimization of Memory Depth and Kernels in Wideband DPD

The optimizations of both memory depth and kernel functions are critical for wideband digital pre-distortion (DPD). However, the memory depth is usually determined via exhaustive search over a wide range for the sake of linearization optimality, followed by the kernel selection of each memory depth, yielding excessive computational cost. In this letter, we aim to provide an efficient solution that jointly optimizes the memory depth and kernels while preserving reasonable linearization performance. Specifically, we propose to formulate this optimization as a blockweighted least absolute shrinkage and selection operator (Lasso) problem, where kernels are assigned regularization weights based on their polynomial orders. Then, a block coordinate descent algorithm is introduced to solve the block-weighted Lasso problem. Measurement results on a generalized memory polynomial (GMP) model demonstrates that our proposed solution reduces memory depth by 31.6% and kernel count by 85% compared to the full GMP, while achieving -46.4 dB error vector magnitude (EVM) for signals of 80 MHz bandwidth. In addition, the proposed solution outperforms both the full GMP and the GMP pruned by standard Lasso by at least 0.7 dB in EVM.