Adaptive signal processing: A discussion of trade-offs from the perspective of artificial learning

Since many signal processing problems can be posed as sample-based decision and estimation tasks, we discuss how studies from other fields such as neural networks might suggest improved architectures (models) and algorithms for these types of problems. We then concentrate on PAM equalization, showing that a reordering of the equivalent classification problem generates a ‘staircase’ which, while retaining the simplicity of the classical equalizer, allows modifications to made in the outputs and in the training objectives which provide advantages even in the least complex cases. We go on to demonstrate that these advantages increase when one considers, for example, nonlinear channels with memory. From our simulations we draw conclusions and suggest futher related research. We also present two new avenues of inquiry, offering significant practical advantages, which are motivated by the discussions.