The current scenario has shown that, with the conventional spectrum access approach, the radio spectrum allocated to primary (licensed) users is hugely underutilized. While many spectrum methods have been proposed to utilize spectrum efficient manner, the spectrum access opportunistic way is happen to the most practical approach to attain near-optimal spectrum utilization by permitting secondary (unlicensed) users to sense and access available spectrum opportunistically. In this paper, we present decision making scheme in cognitive radio based on Interval type-2 fuzzy logic system. Here, classical type-1 and Interval type-2 fuzzy logic system has been compared in terms of possibility of spectrum access by the secondary user with effective and seamless communication between cognitive radio and primary user. The proposed fuzzy inference system has three input parameters such as spectrum utilization efficiency, degree of mobility and distance to primary user of cognitive radio, along with output parameter as the possibility of accessing the spectrum for secondary user based on linguistic knowledge of 27 rules. This paper mainly deals with design of decision making scheme using Interval type-2 fuzzy logic for minimizing the effect of uncertainty produced by the measurement

This paper presents directional and cooperative spectrum occupancy measurements in the 2.4 GHz industrial, scientific and medical band. Spectrum occupancy characterises the efficiency of spectrum use in terms of identifying the proportion of time that a given frequency channel is occupied. Directional spectrum occupancy measurements are carried out using two separately located measurement devices with directional antennas to capture the influence of the spatial dimension on the spectrum use. The measurements from the different antennas are further combined using decision fusion techniques to get cooperative spectrum occupancies that give a more accurate view of the actual spectrum use. The resulting directional and cooperative spectrum occupancies are valuable input to the development of future cognitive radio systems where unoccupied channels could be accessed opportunistically. The measurements results indicate that the spectrum occupancy can vary significantly in the same office environment depending on the measurement location and direction.

This paper presents a novel decision-making system for the selection of methods to obtain knowledge of spectrum availability in future mobile communication systems equipped with cognitive radio system (CRS) capabilities. The proposed decision-making scheme selects the methods to obtain knowledge of spectrum availability between control channels, databases and spectrum sensing based on the specific requirements of the frequency band at hand. The developed decision-making system considers realistic frequency bands and spectrum sharing scenarios, including bands with primary allocation to mobile service where the operator governs the spectrum use, bands with co-primary or secondary allocation to mobile service where the primary users have to be protected from harmful interference, and finally license-exempt bands, where different systems coexist in uncontrolled interference conditions. Specifically, a novel rule-based decision-making system with a learning mechanism is developed to select among different spectrum sensing techniques including matched filtering, correlation detection, feature detection, energy detection, and cooperative sensing. The decision making system is further applied to operator-governed opportunistic networks, which are dynamically created temporary extensions of the mobile infrastructure networks. Performance evaluation is done by assuming changing operational conditions so as to elucidate the gains of the proposed decision making system with respect to the case when the sensing approach is kept fixed.

La presente invention concerne un nouveau procede de prise de decision distribuee pour la detection adaptative d'un signal. L'invention concerne un systeme et un procede adaptatifs d'allocation de ressources de detection de signal utilisees pour decouvrir le statut d'utilisation d'un canal (occupe/inoccupe). Le systeme consiste a selectionner des procedes de detection de spectre appropries et des techniques de combinaison de decision adequates ainsi que les parametres associes pour satisfaire les exigences posees pour le systeme dans l'environnement operationnel specifique. En consequence, le systeme presente une bonne performance, une mise en œuvre simple, et peut etre applique dans des situations multiples variables dans le temps.

Opportunistic networks with cognitive management systems can improve the resource use in future wireless communication networks by forming local clusters that are temporary extensions of the infrastructure and governed by the operator. This paper presents a decision making system that selects the techniques for obtaining spectrum availability information in opportunistic networks. The proposed decision making system selects the most suitable technique(s) from cognitive control channels, databases, and spectrum sensing techniques. Moreover, a novel and simple rule-based expert system is developed to choose the spectrum sensing technique among energy detection, correlation-based detection, and waveform-based detection. The selection is made based on the required probability of detection, operational SNR, available time, and available a priori information. The developed rule-based decision making system is presented in the form of a decision tree to illustrate the dominating paths that influence the decisions. Situations where none of the considered spectrum sensing techniques can meet the given conditions are identified and new approaches are proposed including cooperative sensing and changing of the channel. Results are presented to verify the functioning of the proposed decision making system and to show the relative frequencies of the different selected sensing techniques. Significant improvements can be obtained when the decision making system is used compared to using a single sensing technique instead.

This paper presents a novel architecture and approach for obtaining spectrum availability information in future cognitive radio systems (CRS). CRS can opportunistically access spectrum by identifying unoccupied channels while keeping higher priority systems on the same channel free from harmful interference. Knowledge of the current state of the spectrum use is of utmost importance for CRS operations. There are different techniques for obtaining spectrum availability information including e. g. cognitive pilot channels, databases, spectrum sensing techniques, and combinations thereof. For spectrum sensing there are different algorithms and cooperative combining techniques with different characteristics and capabilities in terms of e. g. performance, complexity, and requirement of a priori information. This paper presents a unified architecture for selecting methods for obtaining spectrum availability information taking into account the operational environment and underlying policies. In addition, a novel low complexity heuristic decision making method is presented for selecting the spectrum sensing technique taking into account different capabilities and requirements while being adaptable to the changing environment.

This paper reviews applications of fuzzy logic to telecommunications and proposes a novel fuzzy combining scheme for cooperative spectrum sensing in cognitive radio systems. A summary of previous applications of fuzzy logic to telecommunications is given outlining also potential applications of fuzzy logic in future cognitive radio systems. In complex and dynamic operational environments, future cognitive radio systems will need sophisticated decision making and environment awareness techniques that are capable of handling multidimensional, conflicting and usually non-predictable decision making problems where optimal solutions can not be necessarily found. The results indicate that fuzzy logic can be used in cooperative spectrum sensing to provide additional flexibility to existing combining methods.

When a knowledge-based system is under development, the main interest is pointed to the information that is installed in the system, which means how to elicitate the information from domain experts and from other knowledge sources, and how to be sure about the reliability of the information. Fewer interests have appeared towards understanding the whole process of developing knowledge-based systems. In practice, this means combining knowledge engineering and software engineering tasks to build up a development process for knowledge-based systems.

Controlling the temperature of a zinc roaster furnace is a difficult task, because of several affecting variables and overall demands to control the temperature exactly. The nature of affecting variables is typically unmeasurable, or unpredictable, which means that they cannot be used directly when controlling the temperature. The furnace and measuring devices tend to get dirty when in use, which increases delays in the process, which in turn places additional demands on the control system. After failing to solve the control problem with a normal PI controller, a knowledge-based fuzzy controller was developed to keep the temperature as close to the set-point as possible. The fuzzy controller was initially put into use in 1994, and has been in daily use since then.

Meta-rule based adaptation methods are presented and discussed from the viewpoint of using fault diagnosis information in adapting a control system. Using fault diagnosis information in the parameterization of controllers is presented as a way to improve the performance of controllers in cases where faults, damages or a changing environment affect the behavior of a process or a device. The meta-rule approach, which is commonly used in control systems, is extended to cover information produced by a fault diagnosis system. The background of the methods presented is knowledge engineering with fuzzy logic and other intelligent techniques.

The spiral model for the development process is found to be the most suitable approach in constructing knowledge-based expert systems. One possible pitfall of using the spiral model is the number of rounds needed in developing an expert system. The pitfall can be avoided by using as reliable as possible methods in knowledge acquisition. Unfortunately there seem to be few methods for validating expert knowledge. In this study we present methods to validate the acquired knowledge by using a fuzzy process model. A fuzzy process model is built using the expert knowledge, and the knowledge is analyzed using data measured from a real process and the FLS tuning method

We first introduce a non-adaptive fuzzy logic controller (FLC) for the control of live steam temperature in a coal-fired power plant. For further enhancing performance, we introduce a self-tuning method for the FLC that modifies the scaling factor of one FLC output. To make the FLC more portable to other similar plants and more robust we add another self-tuning mechanism that runs online and modifies the membership functions of the fuzzy rule set. We have used the meta-rule approach in the tuning mechanisms. The performance of the FLC is compared to a cascade PI controller