The open nature of the wireless channel makes it vulnerable to many security attacks like jamming and eavesdropping. Jamming threatens the existence of wireless services and can lead to denial of service. Barrage jamming where the jammer emits white Gaussian noise that occupies the entire transmission bandwidth is known to be the most harmful when the jammer has no knowledge about the target system or when it targets many different systems. The majority of literature models the barrage jamming signal at the receiver as additive Gaussian noise. The accuracy of this model in mobile communication scenarios is questionable. The complex Gaussian signal transmitted over the unknown complex Gaussian channel induces a non-Gaussian signal at the receiver. Knowing the distribution of the received jamming signal is fundamental to develop detectors and to compute the probability of detection error. This thesis considers scenarios where a single-antenna transmitter sends complex symbols drawn from one-dimensional or multi-dimensional constellation to a receiver equipped with a single, double, or multiple antennas in the presence of a single antenna barrage jammer. The exact likelihood expressions of the received signal and the likelihood expressions based on the Gaussian approximation of the signal induced by the jammer's transmissions are derived for scenarios in which the receiver has full channel state information (CSI), full channel distribution information (CDI), or partial CDI about the transmitter channel. The jammer CDI is assumed to be either partially or fully available at the receiver. Using the derived likelihood expressions, two maximum likelihood (ML) detectors are developed for each scenario. One detector is based on the exact likelihood expressions and the other is based on the Gaussian approximation expressions. The performance of each detector is investigated and cases in which the two detector are equivalent are identified analytically and experimentally. Furthermore, the effects of the number of receive antennas, and symbol length on the detection performance are investigated.