Low-complexity prediction of complex-valued sequences using a novel "residual-as-prediction" method

TL;DR

This paper introduces a low-complexity prediction method called residual-as-prediction for complex-valued sequences, effectively reducing residual magnitudes especially for high-bandwidth signals, aiding compression.

Contribution

It presents a novel prediction approach tailored for high-bandwidth complex sequences, addressing limitations of existing simple methods.

Findings

Reduces residual mean magnitude for sequences up to 85% bandwidth

Maintains low computational complexity for power-efficient implementation

Effective in compressing high-bandwidth complex signals

Abstract

A method of prediction is presented to aid compression of sequences of complex-valued samples. The focus is on using prediction to reduce the average magnitude of residual values after prediction (not on the subsequent compression of the residual sequence). The prediction method has low computational complexity, so as to keep power consumption in implementations of the method low. The new method presented applies specifically to sequences that occupy a significant percentage of the sampling bandwidth; something that existing, simple prediction methods fail to adequately address. The new method, labeled "residual-as-prediction" here, produces residual sequences with reduced mean magnitude compared to the original sequence, even for sequences whose bandwidth is up to 85% of the sampling bandwidth.