Abstract-In laptop and desktop computers, clocks and busses generate significant radio frequency interference (RFI) for the embedded wireless data transceivers. RFI is impulsive in nature. When detecting a signal in additive impulsive noise, Spaulding and Middleton showed a potential improvement in detection of 25 dB at a bit error rate of 10 −5 when using a Bayesian detector instead of a standard correlation receiver. In this paper, we model impulsive noise using Middleton Class A and Symmetric Alpha Stable (SαS) models. The contributions of this paper are to evaluate (1) the performance vs. complexity of parameter estimation algorithms, (2) the closeness of fit of parameter estimation algorithms to measured RFI data from the computer platform, (3) the communication performance vs. computational complexity tradeoffs for the correlation receiver, Wiener filter, and Bayesian detector, and (4) the performance of myriad filtering in combating RFI interference modeled as SαS interference.

Abstract-Research in cognitive radios has renewed interest in tools, such as spectrum estimation and modulation identification, to characterize the radio frequency (RF) environment. The use of multiple antennas for multiple-input multiple-output (MIMO) communications presents a new challenge in detecting and classifying signals. In this paper, we propose a cyclostationarity-based statistical test to detect space-time block codes, focusing on the two transmitter Alamouti space-time block code (STBC). Our test exploits a new characterization of the Alamouti code using fourth order cyclic frequencies. The test requires only a single receive antenna, and does not require any symbol synchronization.

Abstract-Blind source separation (BSS) is the process of using multiple sensors to separate multiple random signals from a received linear combination. In this paper, we apply blind source separation techniques to mixtures of digital communications signals. We predict the achievable symbol error rate when the signals are received on the same carrier frequency and blindly separated. In a wireless environment where the sources are mobile or the environment is changing, the mixing matrix will vary with time. Our primary contribution is that we identify the major source of error in separation. We extend an adaptive step size algorithm to the complex-valued case to mitigate the errors in a dynamic environment.