Deep Sensing for Future Spectrum and Location Awareness 5G Communications

Spectrum sensing based dynamic spectrum sharing is one of the key innovative techniques in future 5G communications. When realistic mobile scenarios are concerned, the location of primary user (PU) is of great significance to reliable spectrum detections and cognitive network enhancements. Given the dynamic disappearance of its emission signals, the passive locations tracking of PU, nevertheless, remains dramatically different from existing positioning problems. In this investigation, a new joint estimation paradigm, namely deep sensing, is proposed for such challenging spectrum and location awareness applications. A major advantage of this new sensing scheme is that the mutual interruption between the two unknown quantities is fully considered and, therefore, the PU´s emission state is identified by estimating its moving positions jointly. Taking both PU´s unknown states and its evolving positions into account, a unified mathematical model is formulated relying on a dynamic state-space approach. To implement the new sensing framework, a random finite set (RFS) based Bernoulli filtering algorithm is then suggested to recursively estimate unknown PU states accompanying its time-varying locations. Meanwhile, the sequential importance sampling is used to approximate intractable posterior densities numerically. Furthermore, an adaptive horizon expanding mechanism is specially designed to avoid the mis-tracking aroused by the intermittent disappearance of PU. Experimental simulations demonstrate that, even with mobile PUs, spectrum sensing can be realized effectively by tracking its locations incessantly. The location information, as an extra gift, may be utilized by cognitive performance optimizations.