Benchmarking of learning architectures for digital predistortion
Indirect and direct learning architectures are the two main parameter identification approaches for digital predistortion systems. While the indirect scheme is less complex, its inherent shortcomings are avoided by the direct learning approach. Trying to answer the question whether this advantage of the direct approach can be exploited in terms of measurable linearization-performance improvement i
