Research Projects

In modern wireless communications, scenarios often arise in which the receiver is required to perform detection of multi-user transmissions on the same channel or suppress co-channel interferers. In these scenarios, signal separation techniques based on statistical properties can be highly effective.

In modern wireless communications, scenarios often arise in which the receiver is required to perform detection of multi-user transmissions on the same channel or suppress co-channel interferers. In these scenarios, signal separation techniques based on statistical properties can be highly effective. However, for wireless systems operating in highly dynamic environments (such as mobile and vehicular communications), the rapidly time-varying channel condition remains a major challenge for block-based signal processing, in which the estimation of statistical properties is performed through averaging over a block of data samples. When the channel parameters change with time, long blocks mean substantial variation of mixing matrices within each block, which inevitably degrades the source separation performance. On the other hand, short blocks render the estimation of signals’ statistical properties inaccurate and biased, thus resulting in poor estimation performance.

We addresses the above-mentioned challenge via the adoption of signal separation algorithms specifically designed for dynamic channel conditions, and artificial data injection applied to short processing data blocks in wireless receivers. Through theoretical and simulation studies, we concluded that the data injection method has great potential in improving signal detection accuracy and/or processing speed for multi-user detection in wireless receivers under dynamic channel conditions. The physical layer security of these mobile communication systems is also being addressed. The research is supported by Air Force Research Laboratory’s Information Directorate (AFRL/RI).