Worldwide Interoperability for Microwave Access (WiMAX), based on the IEEE 802.16 standards, is a technology that offers low cost mobile broadband access to multimedia and internet applications for operators and end-users. Similarly to cellular phone or other Radio Frequency devices, WiMAX has to be considered as a possible source of electromagnetic pollution and so monitoring its emission could be necessary to verify compliance with the applicable emission limits. Generally, the monitoring of the electromagnetic pollution is performed by means of a suitable measurement chain constituted by an antenna connected to a traditional spectrum analyzer. The use of this kind of device to measure the power of digital modulated noise-like signals, such as WiMAX, requires to use proper measurement methods and to carefully set many instrument parameters to obtain reliable measurement results, otherwise a significant underestimate or overestimate of the human exposure can be obtained.

In this framework, this paper investigates the feasibility of using the traditional spectrum analyzer to perform the electromagnetic pollution measurements due to WiMAX devices. A large experimental campaign is carried out to identify the most proper measurement method and spectrum analyzer settings able to warrant reliable measurements.

In this paper, a filtering stage based on employing a Savitzky-Golay (SG) filter is proposed to be used in the spectrum sensing phase of a Cognitive Radio (CR) communication paradigm for Vehicular Dynamic Spectrum Access (VDSA). It is used to smooth the acquired spectra, which constitute the input for a spectrum sensing algorithm. The sensing phase is necessary, since VDSA is based on an opportunistic approach to the spectral resource, and the opportunities are represented by the user-free spectrum zones, to be detected through the sensing phase. Each filter typology presents peculiarities in terms of its computational cost, de-noising ability and signal shape reconstruction. The SG filtering properties are compared with those of the linear Moving Average (MA) filter, widely used in the CR framework. Important improvements are proposed.