Estimation and Characterization of Frequency Hopping Interferers Using Spectral Sensing Techniques

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Identifying and distinguishing among the individual frequency-hopping spread spectrum (FHSS) emitters in a band of interest is one of the most important aspects of spectrum sensing in cognitive radio. Characterization of FHSS interferers through spectrum sensing enables intelligent interference management and more secure communication in a cognitive radio network. One important attribute that can be measured to enable this characterization of the transmission environment is carrier frequency offset, inherent in all transmitters, such as Bluetooth. FHSS signals are known to be difficult to intercept, so the prior work was focused on methods for FHSS signal detection and interception. However, the methods for interception do not address two important cognitive radio spectrum sensing goals: determining the exact number of FHSS transmitters and their unique frequency offset fingerprints.

In practice, obtaining sufficiently accurate carrier frequency offset measurements can be challenging given constraints on sampling rate and DFT size. Individual emitters will often exhibit offsets separated by only a few KHz, which can make it difficult to measure carrier frequency offset sufficiently accurately to perform emitter differentiation based on a single transmitted pulse from each emitter. The frequency resolution can be increased if data from multiple pulses from the same emitter are combined. This is in some sense a classic estimation problem, though one which in the spectrum sensing context is made substantially more complex due to time and frequency variations of the transmissions made by the multiple emitters.

In our work, we utilize an approach in which temporal information is used to resolve this potential ambiguity, and thus to enhance the resolution of the frequency analysis, thereby increasing the ability to accurately characterize frequency offset. The method leverages the facts that 1) the start times of transmissions are uniformly distributed, and 2) the frequency hopping interval for standards such as Bluetooth is several orders of magnitude larger than the time resolution available in low cost detectors. Thus, observations of pulse start times can be utilized to form a list of likely different emitters, and the averaging over multiple pulses can then be performed. This in turn enables accurate frequency offset information which can then be used to identify future transmissions of devices, even when such transmissions occur at different time offsets.

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