Next generation mobile networks bring unprecedented opportunities coupled with unique challenges thanks to the integration of multiple families of devices. Fast and robust signal classification and modulation identification become critical to meet the sustained demand on capacity. This paper presents a comparative study of data-centric and conventional approaches to signal identification at different noise levels on a real-world application. We demonstrate that a standard lightweight classifier can detect multiple modulation schemes with and without data compression and outperforms current state-of-the-art by as much as 6% on average across 15 different noise levels. More importantly, the detection speed is improved by at least 50-fold without a significant loss in accuracy when using feature compression.