While we continue to identify spectrum bands for future 5G use, we have failed to take steps to take these airwaves to market.
The authors in [83] used HMM for blind source separation to identify spectrum holes.
Signal processing plays a major role in designing cognitive wireless networks, especially in spectrum sensing to identify spectrum opportunities and in the design of cognitive spectrum access to exploit the identified spectrum holes.
In order to identify spectrum holes, SUs should detect whether PU is occupying its band [5, 6].
In order to identify spectrum holes, secondary users (SUs) should detect whether the spectrum band is occupied by primary user (PU).
Therefore, wireless devices must not only identify spectrum holes for opportunistic data transmission but also search for occupied spectrum band to harvest RF energy.
After nonactive spectrum has been identified, spectrum utilization becomes an important consideration, when considering the overall efficiency of the CR system.
A CR at a particular place and time needs to sense the wireless environment in order to identify spectrum holes for opportunistic use.
As is known, in cognitive radio networks, CUs first perform the spectrum sensing to identify spectrum holes and then communicate with each other over the detected spectrum holes[2, 3].
For each of the 83 proteoforms, we selected only one identified spectrum with the largest number of monoisotopic masses to remove similar spectra.
