Fuzzy Logic Estimator for Variant SNR Environments

Fuzzy Logic Estimator for Variant SNR Environments

Rosa Maria Alsina Pagès (Universitat Ramon Llull, Spain), Clàudia Mateo Segura (Universitat Ramon Llull, Spain) and Joan-Claudi Socoró Carrié (Universitat Ramon Llull, Spain)
Copyright: © 2009 |Pages: 9
DOI: 10.4018/978-1-59904-849-9.ch107
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The acquisition system is one of the most sensitive stages in a Direct Sequence Spread Spectrum (DS-SS) receiver (Peterson, Ziemer & Borth, 1995), due to its critical position in order to demodulate the received information. There are several schemes to deal with this problem, such as serial search and parallel algorithms (Proakis, 1995). Serial search algorithms have slow convergence time but their computational load is very low; on the other hand, parallel systems converge very quickly but their computational load is very high. In our system, the acquisition scheme used is the multiresolutive structure presented in (Moran, Socoró, Jové, Pijoan & Tarrés, 2001), which combines quick convergence and low computational load. The decisional system that evaluates the acquisition stage is a key process in the overall system performance, being a drawback of the structure. This becomes more important when dealing with time-varying channels, where signal to noise ratio (called SNR) is not a constant parameter. Several factors contribute to the performance of the acquistion system (Glisic & Vucetic, 1997): channel distorsion and variations, noise and interference, uncertainty about the code phase, and data randomness. The existence of all these variables led us to think about the possibility of using fuzzy logic to solve this complex acquisition estimation (Zadeh, 1973). A fuzzy logic acquisition estimator had already been tested and used in our research group to control a serial search algorithm (Alsina, Morán & Socoró, 2005) with encouraging results, and afterwards in the multiresolutive scheme (Alsina, Mateo & Socoró, 2007), and other applications to this field can be found in bibliography as (Bas, Pérez & Lagunas, 2001) or (Jang, Ha, Seo, Lee & Lee, 1998). Several previous works have been focused in the development of acquisition systems for non frequency selective channels with fast SNR variations (Moran, Socoró, Jové, Pijoan & Tarrés, 2001) (Mateo & Alsina, 2004).
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In 1964, Dr. Lofti Zadeh came out with the term fuzzy logic (Zadeh, 1965). The reason was that traditional logic could not answer to some questions with a simple yes or no. So, it handles the concept of partial truth. Fuzzy logic is one of the possibilities to imitate the working of a human brain, and so to try to turn artificial intelligence into real intelligence. Zadeh devised the technique as a method to solve problems for soft sciences, in particular those that involve human interaction.

Fuzzy logic has been proved to be a good option for control in very complex processes, when it is not possible to produce a mathematical model. Also fuzzy logic is recommendable for highly non-linear processes, and overall, when expert knowledge is desirable to be performed. But it is not a good idea to apply if traditional control or estimators give out satisfying results, or for problems that can be modelled in a mathematical way.

The most recent works in control and estimation using fuzzy logic applied to direct sequence spread spectrum communication systems are classified into three types. The first group uses fuzzy logic to improve the detection stage of the DS-CDMA1 receiver, and they are presented by Bas et al and Jang et al (Bas, Pérez, & Lagunas, 2001)(Jang, Ha, Seo, Lee, & Lee, 1998). The second group uses fuzzy logic to improve interference rejection, with works presented by Bas et al and by Chia-Chang et al (Bas, & Neira, 2003) (Chia-Chang, Hsuan-Yu, Yu-Fan, & Jyh-Horng, 2005). Finally, fuzzy logic techniques are also improving estimation and control in the acquisition stage of the DS-CDMA receiver, in works by Alsina et al (Alsina, Moran, & Socoró, 2005) (Alsina, Mateo, & Socoró, 2007).

Key Terms in this Chapter

Fuzzyfication: It is the process of defining the degree of membership of a crisp value for each fuzzy set.

Fuzzy Sets: Fuzzy sets are sets whose members have a degree of membership. They were introduced to be an extension of the classical sets, whose elements’ membership was assessed by binary numbers.

Degree of Truth: It denotes the extent to which a preposition is true. It is important to not be confused with the concept of probability.

Linguistic Variables: They take on linguistic values, which are words, with associated degrees of membership in each set.

Defuzzyfication: After computing the fuzzy rules, and evaluating the fuzzy variables, this is the process the system follows to obtain a new membership function for each output variable.

IF-THEN Rules: They are the typical rules used by expert fuzzy systems. The IF part is the antecedent, also named premise, and the THEN part is the conclusion.

Fuzzy Logic: Fuzzy logic was derived from Fuzzy Set theory, working with a reason that it is approximate rather than precise, deducted from the typical predicate logic.

Linguistic Term: It is a subjective category for a linguistic variable. Each linguistic term is associated with a fuzzy set.

Membership Function: It is the function that gives the subjective measures for the linguistic terms.

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