In the first phase of a more than two-year study funded by InterDigital, Claudio da Silva’s team of wireless researchers have made great strides in developing more reliable and efficient spectrum sensing techniques that will be needed to meet the ever-expanding demand for wireless technologies.
Their techniques can be used in cognitive radio systems, devices that first identify underutilized spectrum with the use of spectrum databases and/or spectrum sensing and then, following pre-defined rules, dynamically access the “best” frequency bands on an opportunistic and non-interfering basis.”
During the first phase of the study, “by exploiting location-dependent signal propagation characteristics, we have developed efficient sensing algorithms that enable a set of devices to work together to determine spectrum opportunities,” said William Headley, one of the Ph.D. students working on this project.
For the second year of the study, the focus is changing to the design of spectrum sensing algorithms that are robust to both man-made noise and severe multipath fading. "The vast majority of sensing algorithms were developed for channels in which the noise is a Gaussian process," said Gautham Chavali, a Ph.D. student who also works on this project. “However, experimental studies have shown that the noise that appears in most radio channels is highly non-Gaussian,” Chavali added.