Entropy-Based Spectral Processing on the IEEE 802.11A OFDM Waveform

The problem of non-matched filter signal detection, identification and/or characterization is of significant interest to military planners who require increased situational awareness of radio frequency (RF) systems operating in a given area. Situational awareness can also provide a management tool for interference avoidance by allowing the integration of new RF-based devices without affecting the performance of existing ones. This research introduces an entropy-based spectral processing technique for passively identifying and characterizing communication signals. The proposed technique is based on well-established concepts of sequence entropy or concentration occurring within a specified transformation space of the signal of interest. As demonstrated here, performance of the entropy-based spectral processing technique is dictated by input variables which partition the signal interest, transform the partitioned signal to the spectral domain, and calculate an entropy-based metric for each transformed partition. The process produces a spectral-entropy response for the signal of interest. A proof-of-concept demonstration was conducted by applying the proposed technique to both a simulated and an experimentally collected IEEE 802.11a OFDM waveform. Features which emerge within the spectral-entropy response are visually correlated with known deterministic and random components of the 802.11a waveform at different signal-to-noise ratios (SNR). These components are readily identifiable through their comparatively low spectral-entropy response (high concentration) at SNR's approaching -5.0 dB