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Complex-valued neural networks have emerged boasting superior modelingperformance for many tasks across the signal processing, sensing, andcommunications arenas. However, developing complex-valued models currentlydemands development of basic deep learning operations, such as linear orconvolution layers, as modern deep learning frameworks like PyTorch and Tensorflow do not adequately support complex-valued neural networks. This paperoverviews a package built on PyTorch with the intention of implementinglight-weight interfaces for common complex-valued neural network operations andarchitectures. Similar to natural language understanding (NLU), which asrecently made tremendous leaps towards text-based intelligence, RF SignalUnderstanding (RFSU) is a promising field extending conventional signalprocessing algorithms using a hybrid approach of signal mechanics-based insightwith data-driven modeling power. Notably, we include efficient implementationsfor linear, convolution, and attention modules in addition to activationfunctions and normalization layers such as batchnorm and layernorm.Additionally, we include efficient implementations of manifold-basedcomplex-valued neural network layers that have shown tremendous promise butremain relatively unexplored in many research contexts. Although there is anemphasis on 1-D data tensors, due to a focus on signal processing,communications, and radar data, many of the routines are implemented for 2-Dand 3-D data as well. Specifically, the proposed approach offers a useful setof tools and documentation for data-driven signal processing research andpractical implementation.

Complex-Valued Neural Networks for Data-Driven Signal Processing and Signal Understanding