Advances in cognitive radio networks: A survey <s> A. Interference Temperature <s> Cognitive radio is viewed as a novel approach for improving the utilization of a precious natural resource: the radio electromagnetic spectrum. The cognitive radio, built on a software-defined radio, is defined as an intelligent wireless communication system that is aware of its environment and uses the methodology of understanding-by-building to learn from the environment and adapt to statistical variations in the input stimuli, with two primary objectives in mind: /spl middot/ highly reliable communication whenever and wherever needed; /spl middot/ efficient utilization of the radio spectrum. Following the discussion of interference temperature as a new metric for the quantification and management of interference, the paper addresses three fundamental cognitive tasks. 1) Radio-scene analysis. 2) Channel-state estimation and predictive modeling. 3) Transmit-power control and dynamic spectrum management. This work also discusses the emergent behavior of cognitive radio. <s> BIB001 </s> Advances in cognitive radio networks: A survey <s> A. Interference Temperature <s> Interference temperature has been proposed by the FCC as a metric for interference analysis. The purpose of the metric is to demystify and remove the subjective context that has been the basis of interference analysis within the regulatory agencies. The development of an interference metric is critical if more intensive, dynamic use of the spectrum is desired. There has been very little specified as to the origins, use, and challenges of an interference metric. This paper provides an introductory analysis of the interference temperature metric. <s> BIB002 </s> Advances in cognitive radio networks: A survey <s> A. Interference Temperature <s> Cognitive radio based dynamic spectrum access network is emerging as a technology to address spectrum scarcity. In this study, we assume that the channel is licensed to some primary (licensed) operator. We consider a sensor network with cognitive radio capability that acts as a secondary (unlicensed) network and uses the channel in underlay mode. The secondary network uses interference temperature model to ensure that the interference to the primary devices remain below a predefined threshold. We use hidden Markov model (HMM) to model the interference temperature dynamics of a primary channel. The HMM is trained using Baum-Welch procedure. The trained HMM is shown to be statistically stable. Secondary nodes use this trained HMM to predict the interference temperature of the channel in future time slots and computes the value of channel availability metric (CAM) for the channel. CAM is used by secondary nodes to select a primary channel for transmission. Results of application of such trained HMMs in channel selection in multi-channel wireless network are presented. <s> BIB003
In opportunistic spectrum access, secondary users need to detect primary users' appearance and decide which portion of the spectrum is available. Such a decision can be made according to different metrics. Traditional approach is to limit the transmitter power of interfering devices, i.e., the transmitted power should be no more than a prescribed noise floor at a certain distance from the transmitter. However, due to the increased mobility and variability of radio frequency (RF) emitters, constraining the transmitter power becomes more problematic, since unpredictable new source of interference may appear. To address this issue, FCC Spectrum Policy Task Force  has proposed a new metric on interference assessment, the interference temperature, to enforce an interference limit perceived by receivers. The interference temperature is a measure of the RF power available at a receiving antenna to be delivered to a receiver, reflecting the power generated by other emitters and noise sources BIB002 . More specifically, it is defined as the temperature equivalent to the RF power available at a receiving antenna per unit bandwidth  , i.e.,  where is the average interference power in Watts centered at , covering bandwidth measured in Hertz, and Boltzmann's constant is 1.38 Joules per degree Kelvin. With the concept of interference temperature, FCC further established an interference temperature limit, which provides a maximum amount of tolerable interference for a given frequency band at a particular location. Any unlicensed secondary transmitter using this band must guarantee that their transmission plus the existing noise and interference must not exceed the interference temperature limit at a licensed receiver. Since any transmission in the licensed band is viewed to be harmful if it would increase the noise floor above the interference temperature limit, it is necessary that the receiver have a reliable spectral estimate of the interference temperature. This requirement can be fulfilled by using the multitaper method to estimate the power spectrum of the interference temperature with a large number of sensors BIB001 . The multitaper method can solve the tradeoff between bias and variance of an estimator and provide a near-optimal estimation performance. The large number of sensors can account for the spatial variation of the RF energy from one location to another. Subspace-based method has also been proposed to gain knowledge of the quality and usage of a spectrum band  , where information about the interference temperature is obtained by eigenvalue decomposition. If a regulatory body sets an interference temperature limit for a particular frequency band with bandwidth , then the secondary transmitters has to keep the average interference below . Therefore, the interference temperature serves as a cap placed on potential RF energy that could appear on that band, and there are some previous efforts about how to implement efficient spectrum allocation with the interference temperature limit. Spectrum shaping has been proposed to improve spectrum efficiency  in CR networks. More specifically, using interference fitting, a CR senses the shape of the interference power spectrum and create spectra inversely shaped to the current interference environment to take advantage of gaps between the noise floor and the cap of the interference temperature limit. Interference temperature dynamics in a CR network were investigated in BIB003 using a hidden Markov model (HMM), where the trained HMM can be used as a sequence generator for secondary nodes to predict the interference temperature of the channel in the future and aid their channel selection for transmission. A comprehensive analysis has been presented in  , which quantifies how interference temperature limits should be selected and how those choices affect the range of licensed signals. It is shown that the capacity achieved is a simple function of the number of nodes, the average bandwidth, and the fractional impact to the primary signal's coverage area. However, as observed by  , the achievable capacity from the interference temperature model is low, compared to the amount of interference it can cause to primary users.  