This work explores the use of Reservoir Computing for PHY-layer processing, where random nonlinear operations generate a reservoir of features, removing the need for expensive feature extraction training. Its performance is assessed in realistic scenarios to determine its potential as a cost-efficient alternative to conventionally trained models.