Deep sensing for future 5G communications with mobile primary users

Bin Li, Yijiang Nan, Chenglin Zhao, Arumugam Nallanathan

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review


A promising joint estimation paradigm, namely deep sensing, is proposed for more challenging spectrum-location awareness 5G applications. A major innovation of the new sensing algorithm is that the mutual interruption between two unknown quantities, i.e. unknown primary states and its moving locations, is fully considered. A unified system model is formulated relying on the dynamic state-space approach, by taking two coupling hidden states into accounts. A random finite set (RFS) inspired Bayesian algorithm is suggested to estimate unknown PU states recursively accompanying its time-varying locations. To avoid the mis-tracking aroused by the intermittent disappearance of PU, an adaptive horizon expanding (AHE) mechanism is designed. Experiments also validate the proposed scheme.
Original languageEnglish
Title of host publicationIEEE
Subtitle of host publication International Conference on Digital Signal Processing (DSP), 2015
Pages521 - 525
Publication statusPublished - 21 Jul 2015


Dive into the research topics of 'Deep sensing for future 5G communications with mobile primary users'. Together they form a unique fingerprint.

Cite this