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In xG networks, the available spectrum holes show different characteristics which vary over time. Since the xG users are equipped with the cognitive radio based physical layer, it is important to understand the characteristics of different spectrum bands. Spectrum analysis enables the characterization of different spectrum bands, which can be exploited to get the spectrum band appropriate to the user requirements. In order to describe the dynamic nature of xG networks, each spectrum hole should be characterized considering not only the time-varying radio environment and but also the primary user activity and the spectrum band information such as operating frequency and bandwidth. Hence, it is essential to define parameters such as interference level, channel error rate, path-loss, link layer delay, and holding time that can represent the quality of a particular spectrum band as follows:
• Interference: Some spectrum bands are more crowded compared to others. Hence, the spectrum band in use determines the interference characteristics of the channel. From the amount of the interference at the primary receiver, the permissible power of an xG user can be derived, which is used for the estimation of the channel capacity.
• Path loss: The path loss increases as the operating frequency increases. Therefore, if the transmission power of an xG user remains the same, then its transmission range decreases at higher frequencies. Similarly, if transmission power is increased to compensate for the increased path loss, then this results in higher interference for other users.
• Wireless link errors: Depending on the modulation scheme and the interference level of the spectrum band, the error rate of the channel changes.
• Link layer delay: To address different path loss,
wireless link error, and interference, different
types of link layer protocols are required at different
spectrum bands. This results in different
link layer packet transmission delay.
• Holding time: The activities of primary users can affect the channel quality in xG networks. Holding time refers to the expected time duration that the xG user can occupy a licensed band before getting interrupted. Obviously, the longer the holding time, the better the quality would be. Since frequent spectrum handoff can decrease the holding time, previous statistical patterns of handoff should be considered while designing xG networks with large expected holding time. Channel capacity, which can be derived from the parameters explained above, is the most important factor for spectrum characterization. Usually SNR at the receiver has been used for the capacity estimation. However, since SNR considers only local observations of xG users, it is not enough to avoid interference at the primary users. Thus, spectrum characterization is focused on the capacity estimation based on the interference at the licensed receivers. The interference temperature model can be exploited for this approach. The interference temperature limit indicates an upper bound or cap on the potential RF energy that could be introduced into the band. Consequently, using the amount of permissible interference, the maximum permissible transmission power of an xG user can be determined. In [63], a spectrum capacity estimation method has been proposed that considers the bandwidth and the permissible transmission power. Accordingly, the spectrum capacity, C, can be estimated as follows:
where B is the bandwidth, S is the received signal power from the xG user, N is the xG receiver noise power, and I is the interference power received at the xG receiver due to the primary transmitter. Estimating spectrum capacity has also been investigated in the context of OFDM-based cognitive radio systems . Accordingly, the spectrum capacity of the OFDM-based xG networks is defined as follows :
where X is the collection of unused spectrum segments, G(f) is the channel power gain at frequency f, S0 and N0 are the signal and noise power per unit frequency, respectively. The recent work on spectrum analysis, as discussed above, only focuses on spectrum capacity estimation. However, besides the capacity, other factors such as delay, link error rate, and holding time also have significant influence on the quality of services. Moreover, the capacity is closely related to both interference level and path loss. However, a complete analysis and modeling of spectrum in xG networks is yet to be developed. In order to decide on the appropriate spectrum for different types of applications, it is desirable and an open research issue to identify the spectrum bands combining all characterization parameters described above.
