The shortage of spectrum resource has become a key limitation of wireless communications. As the analysis of RSSI distribution is straightforward and less demanding in terms of system requirements, we believe it is highly valuable for recognition of wideband technologies on constrained devices in the context of dynamic spectrum access.ĭue to the quick growth of wireless communications, radio spectrum bands are either already claimed by licensed users or heavily loaded by applications in the unlicensed bands. Experimental evaluation indicates that more than 92% accuracy is achieved with the appropriate configuration. The usage of the RSSI distribution-based feature space is illustrated via a sample algorithm. An experimental study shows that even RSSI acquired at a sub-Nyquist sampling rate is able to provide sufficient features to differentiate technologies such as Wi-Fi, Long Term Evolution (LTE), Digital Video Broadcasting-Terrestrial (DVB-T) and Bluetooth. A distinction is made between technologies with a streaming or a non-streaming property, and appropriate feature spaces can be established either by deriving parameters such as packet duration from RSSI or directly using RSSI’s probability distribution. This work proves that multi-model distribution of the received signal strength indicator (RSSI) is related to the signals’ modulation schemes and medium access mechanisms, and RSSI from different technologies may exhibit highly distinctive features. However, due to the complexity of recognition algorithms and the strict condition of sampling speed, communication systems capable of recognizing signals other than their own type are extremely rare. Identifying concurrent technologies is an important step towards efficient spectrum sharing.

Driven by the fast growth of wireless communication, the trend of sharing spectrum among heterogeneous technologies becomes increasingly dominant.

Wifi spectrum analyzer