Radio access technology classification for cognitive radio networks

In spectrum bands where spectrum sharing is allowed by national regulators, radio access technology recognition is an important technique for reducing interference and facilitating cooperation among cognitive radios. Unlicensed users (secondaries) need to be able to differentiate between transmissions of licensed users (primaries) and other unlicensed users. Furthermore, secondaries should only free a band when the licensed primary user starts to transmit. In this regard, secondary users´ transmission technology classification will have a vital role for coexistence/cooperation purposes in such shared spectrum bands. For the purpose of this work, a practical testbed made up of software defined radio transceivers and a set of computing units was put together. A classification neural network was trained in a supervised learning method. Testbed results demonstrate the efficiency of the classification in differentiating among different radio access transmissions.