The studies of data, which result in sampled information in the form of finite fuzzy sets, give rise to the creation of polygons consisting of finite numbers of points tied together. Since the polygons are not formalized by some mathematical expressions, it would be desirable to find continuous functions approximating them rather thoroughly in spite of their irregular shapes. An approximation by the standard curves is sometimes too rough to be a reliable source of a further analysis of the polygons. To improve the accuracy of approximating we test a continuous function, which is composed of joined pi-class functions with seven parameters. The function, called by us “the sampled, truncated pi”, is very sensitive for each little deviation in the polygon’s shape, which allows us to classify it exactly without large errors usually accompanying a process of standard approximation.

The studies of Internet Protocol data give rise to the creation of polygons consisting of finite numbers of points tied together. Since the polygons are not formalized by some mathematical expressions, we suggest creating continuous functions, which approximate them thoroughly in spite of their irregular shapes. To warrant a high accuracy of approximating, otherwise impossible to obtain when using standard curves, we test a continuous function, which is composed of joined truncated pi-class functions with seven parameters. By operating with the functions representing polygons having unusual shapes, we attempt a classification of Internet traffic data, based on datagram sizes. We adopt rough sets to assign the members to an investigated Internet class even if their origin sometimes is unknown.

We explore the classical model of a two-player game to select the best strategies, where action is expected to maintain the values of a certain variable on the neutral level. By inserting fuzzy sets as payoff values in the game matrix, we facilitate the procedure of formulations of payoff expectations by players. Instead of making inconvenient decisions about the choice of accurate numerical entries of the matrix, the players are able to use words, which should simplify communication between them when designing the preliminaries of the game. The players also have the possibility of making a ranking of their favourite strategies. At the next stage of the play, we involve group decision-making in order to aggregate results coming from several paired games, when more than two players contradict each other.

A survey of fuzzy logic applications and principles in wireless communications is presented, with the aim of highlighting successful usage of fuzzy logic techniques in applied telecommunications and signal processing. To the best of our knowledge, this is the first such study of its kind. This paper will focus firstly on discerning prevalent fuzzy logic or fuzzy-hybrid approaches in the areas of channel estimation, channel equalization and decoding, and secondly outlining what conditions and situations for which fuzzy logic techniques are most suited for these approaches. Furthermore, after insights gained from isolating fuzzy logic techniques applied to real problems, this paper proposes areas for further research targeted to practice-oriented researchers.

Cognitive radio is an intelligent technology that helps in resolving the issue of spectrum scarcity. In a spectrum sharing network, where secondary user can communicate simultaneously along with the primary user in the same frequency band, one of the challenges is to obtain balance between two conflicting goals that are to minimize the interference to the primary users and to improve the performance of the secondary user. In our paper we have considered a primary link and a secondary link (cognitive link) in a fading channel. To improve the performance of the secondary user by maintaining the Quality of Service (Qos) to the primary user, we considered varying the transmit power of the cognitive user. For this we proposed ANFIS based opportunistic power control strategy with primary user's SNR and primary user's interference channel gain as inputs. By using fuzzy inference system, Qos of primary user is adhered and there is no need of complex feedback channel from primary receiver. The simulation results of the proposed strategy shows better performance than the one without power control.

Cognitive Radios are recognized as a novel approach to improve the utilization of a precious natural resource of wireless communications: the radio frequency spectrum. Historically, telecom regulators assigned fixed spectrum bands to the licensed wireless network operators. This spectrum management approach guarantees an interference free environment, except for some configuration faults or illegal usage. However, with the increasing demand for more bandwidth in the finite radio spectrum, the spectrum becomes underutilized. Hence, the concept of secondary operators have emerged, but with emphasis not to influence licensed operators. Consequently, the Cognitive Radio Network (CRN) architecture enters the market as an intelligent solution to these issues, with concentration on spectrum sensing procedures to achieve the regulatory constraint. The most successful sensing algorithms are those applying cooperation and scheduling to have better scanning information; however, those algorithms are developed based on the primary network activities, which are good in terms of reducing expected interference, albeit with more computational load on the CRN. In this chapter, a novel sensing scheduler algorithm is proposed. The idea is to utilize the CRN by fairly distributing the sensing task among the sensors and afterwards utilizing the radio spectrum shared with the primary networks.