Covert communications can be a force for good, such as providing a means of message authentication to prevent malicious actors from spoofing networks. This dissertation explores the design of a covert signal to be hidden inside the bandwidth of an Orthogonal Frequency Division Multiplexing (OFDM) signal. In order to make detection by unintended observers as difficult as possible, the covert signal operates as interference inside the OFDM signal and is set to a high Signal to Interference Ratio (SIR). Given the high SIR, the OFDM signal must be cancelled in order to recover the covert signal. The detectability of the covert signal is tested using multiple detectors with and without cancellation. Among the detectors used is a Convolutional Neural Network (CNN) designed for image classification that has been repurposed through transfer learning to detect signal activity in noise and interference. The CNN detector demonstrates resilience in the presence of narrowband interference. The cancellation algorithm is enhanced with an estimate of OFDM windowing as applied at the transmitter, which is an often-overlooked parameter in cancellation applications. The enhanced cancellation-algorithm improves the cancellation of OFDM signals by 5.3 dB in an over-the-air test. The enhanced cancellation-algorithm also improves the Packet Error Rate of OFDM signals and improves the recovery of the covert signal. The improved recovery has direct application to Power-Domain Non-orthogonal Multiple Access and Rate-Splitting Multiple Access, which both rely on successive interference cancellation. Lastly, to frustrate any efforts to analyze the covert waveform, the covert signal is augmented with an adversarial waveform designed to exploit weaknesses in CNNs used for modulation classification. The classification system suffers from uncertainty in the bandwidth estimate of the covert signal. The system will likely err on the side of making the bandwidth wider than necessary. It is demonstrated that a wider bandwidth makes the attack more successful, as opposed to other estimation errors which prior literature has shown to weaken the effectiveness of these attacks
