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(Phys.org) —In today's wireless communication systems, the wireless signals are non-chaotic, meaning they have a well-defined period and frequency. Non-chaotic wireless signals are used in many applications, such as satellite communications, GPS navigation, cell phones, and Wi-Fi devices. However, as many people know first-hand, wireless systems usually have inferior performance compared to wired systems. The problem is due to physical impediments that the wireless signal faces in open space caused by the atmosphere, water, mountains, buildings, and other different media. Now in a new study, researchers have investigated how wireless communication could be implemented with chaotic signals, and found that chaotic signals could overcome some of these physical constraints and lead to superior performance...

http://phys.org/news/2013-05-chaos-wireless.html
http://prl.aps.org/pdf/PRL/v110/i18/e184101

Yes, this is intriguing indeed.

I think that we can explain this in plain words by following the procedure how to compute and visualize chaos. We can write some equations, simple equations that are a function of time and which plot/trace a line on the computer screen over time (probably really fast). This line could be circling around the screen for ages and never quite repeat its trajectory. We call it an orbit. For example, equations could be these http://en.wikipedia.org/wiki/Lorenz_system. So, at the beginning, a circling trajectory is passing close to itself, but over time the separation is changing. See http://en.wikipedia.org/wiki/Lyapunov_exponent. We can have several trajectories in a study, because we can put a point on the screen whose coordinates represent initial conditions and let it run around in circles. Quickly, it approaches the usual pathways, although perhaps not exactly. So we could study two trajectories that are getting closer, or are parallel or are differing. The orbits could represent two chaotic systems that are perhaps synchronized. Now, if we can spawn more orbits, more colored lines to circle around more or less together on the same screen, then we have greater topological entropy, which puts the upper bound on the amount of information that can be encoded by using chaotic emitter and receiver. "For a system given by an iterated function, the topological entropy represents the exponential growth rate of the number of distinguishable orbits of the iterates." http://en.wikipedia.org/wiki/Topological_entropy

"Traditionally, engineers sought to minimize noise and distortion in system designs. Today, Aerospace scientists are seeking to explicitly harness these effects for useful engineering purposes..."
http://www.aerospace.org/publications/crosslink-magazine/crosslink-spring-2011/nonlinear-dynamics-and-chaos-from-concept-to-application/
http://fourier.eng.hmc.edu/e84/silva.pdf