Hyper-wideband communications represent the next frontier in spread spectrum RF systems with an excess of 10 GHz instantaneous bandwidth. In this thesis, an end-to-end physical layer link is implemented featuring 16k-OFDM with a 4 GHz-wide channel centered at 9 GHz. No a priori channel state information is assumed; channel information is derived from the preamble and comb pilot structure. Due to the unique expansive spectral properties, the channel estimator is primarily composed of least squares channel estimates combined with a robust support vector statistical learning approach using autonomously selected parameters. The system’s performance is demonstrated through indoor wireless experiments, including line-of-sight and near-line-of-sight links. Moreover, it is shown that the support vector approach performs superior to linear and cubic spline inter/extrapolation of the least squares channel estimates.