Neural transmitter with co-optimized digital front end functions

Abstract

In order to increase Power Amplifier (PA) linearity and efficiency in the transmission chain wireless communication systems, Digital Front-End (DFE) functions such as Crest Factor Reduction (CFR), Hard Limiter (HL) and Digital Pre-Distortion (DPD) are typically built and adjusted independently. Despite the effectiveness of this modular strategy, it does result in a high hardware footprint. The use of Neural Networks (NNs) to simultaneously model and co-optimize DFE functions for wireless communication systems with memory-effect-exhibiting PAs is examined in this thesis. As a reference, a functional hardware-compatible legacy DFE with CFR, HL and DPD was developed, which was used to evaluate the NN-based DFE solutions. The NN models were compressed using pruning, projection, and quantization methods to allow deployment in contexts with limited resources. To compare the hardware cost of these solutions, bit-exact models were developed. The results demonstrate that, although at a much greater hardware cost, uncompressed NN-based DFE models give better performance than the legacy DFE. Implementation complexity is significantly reduced by compression, though the compression techniques investigated also give significant performance loss. The quantization-based compression gives the best implementation complexity reduction, though the projection-based compression offers a better performance-complexity trade-off than quantization. Everything being considered, the findings show the promise of NN-based cooptimization of DFE functions while emphasizing the necessity of better compression methods for realistic implementation.