In this paper, a self optimization beamforming null control (SOBNC) scheme is proposed. There is a need of maintaining signal to interference plus noise ratio (SINR) threshold to control modulation and coding schemes (MCS) in recent technologies like Wi-Fi, Long Term Evolution (LTE) and Long Term Evolution Advanced (LTE-A). Selection of MCS depends on the SINR threshold that allows maintaining key performance index (KPI) like block error rate (BLER), bit error rate (BER) and throughput at certain level. The SOBNC is used to control the antenna pattern for SINR estimation and improve the SINR performance of the wireless communication systems. The nulling comes with a price; if wider nulls are introduced, i.e. more number of nulls are used, the 3dB beam-width and peak side lobe level (SLL) in antenna pattern changes critically. This paper proposes a method which automatically controls the number of nulls in the antenna pattern as per the changing environment based on adaptive-network based fuzzy interference system (ANFIS) to maintain output SINR level higher or equal to the required threshold. Finally, simulation results show a performance superiority of the proposed SOBNC compared with minimum mean square error (MMSE) based adaptive nulling control algorithm and conventional fixed null scheme.

An adaptive rate controller (ARC) based on an adaptive neural fuzzy inference system (ANFIS) is designed to autonomously adjust the data rate of a mobile heterogeneous network to adapt to the changing traffic load and the user speed for multimedia call services. The effect of user speed on the handoff rate is considered. Through simulations, it has been demonstrated that the ANFIS-ARC is able to maintain new call blocking probability and handoff failure probability of the mobile heterogeneous network below a prescribed low level over different user speeds and new call origination rates while optimizing the average throughput. It has also been shown that the mobile cognitive wireless network with the proposed CS-ANFIS-ARC protocol can support more traffic load than neural fuzzy call-admission and rate controller (NFCRC) protocol.

The single input rule modules connected fuzzy inference method (SIRMs method) by Yubazaki et al. can decrease the number of fuzzy rules drastically in comparison with the conventional fuzzy inference methods. Moreover, Seki et al. have proposed a functional-type SIRMs method which generalizes the consequent part of the SIRMs method to function. However, these SIRMs methods can not be applied to XOR (Exclusive OR). In this paper, we propose a "kernel-type SIRMs method" which uses the kernel trick to the SIRMs method, and show that this method can treat XOR. Further, a learning algorithm of the proposed SIRMs method is derived by using the steepest descent method, and compared with the one of conventional SIRMs method and kernel perceptron by applying to identification of nonlinear functions, medical diagnostic system and discriminant analysis of Iris data.

In this letter, multi user detection process in Code Division Multiple Access (CDMA) is performed by fuzzy inference system (FIS) and the bit error rate (BER) performance was compared with the single user bound, the matched filter receiver and neural network receiver. The BER performance of the matched filter receiver degrades as the number of the active users and the power level differences among the users increase. The neural network receiver needs the training process. Optimal receiver that has the best BER performance is too complex for practical application. The BER performance near the optimal case was obtained with the proposed receiver. The proposed receiver uses the FIS without training process and it has lower time complexity than the optimal receiver.

In this paper, we propose an adaptive multistage fuzzy-based partial parallel interference cancellation (FB-PPIC) multiuser detector for multi-carrier direct-sequence code-division multiple-access (MC-CDMA) communication systems over frequency selective fading channels. The partial cancellation tries to reduce the cancellation error in parallel interference cancellation (PIC) schemes due to the wrong interference estimations in the early stages and thus outperforms the conventional PIC (CPIC) under the heavy load for MC-CDMA systems. Therefore, in this paper, the adaptive cancellation weights are inferred from a proposed multistage fuzzy inference system (FIS) to perform effective PPIC multiuser detection under time-varying frequency selective fading channels in MC-CDMA systems. Simulation results show that the proposed adaptive four-stage FB-PPIC scheme outperforms both CPIC and constant weight PPIC (CW-PPIC) schemes, especially in near-far environments.

This paper proposes a new approach to fuzzy inference system for modeling nonlinear systems based on measured input and output data. In the suggested fuzzy inference system, the number of fuzzy rules and parameter values of membership functions are automatically decided by using the extended kernel method. The extended kernel method individually performs linear transformation and kernel mapping. Linear transformation projects input space into linearly transformed input space. Kernel mapping projects linearly transformed input space into high dimensional feature space. Especially, the process of linear transformation is needed in order to solve difficulty determining the type of kernel function which presents the nonlinear mapping in according to nonlinear system. The structure of the proposed fuzzy inference system is equal to a Takagi-Sugeno fuzzy model whose input variables are weighted linear combinations of input variables. In addition, the number of fuzzy rules can be reduced under the condition of optimizing a given criterion by adjusting linear transformation matrix and parameter values of kernel functions using the gradient descent method. Once a structure is selected, coefficients in consequent part are determined by the least square method. Simulated results of the proposed technique are illustrated by examples involving benchmark nonlinear systems.

Sophisticated and robust resource management is an essential issue in future wireless systems which will provide a variety of application services. In this paper, we employ an adaptive-network-based fuzzy inference system (ANFIS) to control the resource allocation for mobile multimedia networks. ANFIS, possessing the advantages of expert knowledge of fuzzy logic system and learning capability of neural networks, can provide a systematic approach to finding appropriate parameters for the Sugeno fuzzy model. The fuzzy resource allocation controller (FRAC) is designed in a two-layer architecture and selects properly the capacity requirement of new call request, the capacity reservation for future handoffs, and the air interface performance as input linguistic variables. Therefore, the statistical multiplexing gain of mobile multimedia networks can be maximized in the FRAC. Simulation results indicate that the proposed FRAC can keep the handoff call blocking rate low without jeopardizing the new call blocking rate. Also, the FRAC can indeed guarantee quality of service (QoS) contracts and achieve higher system performance according to network dynamics, compared with the guard channel scheme and ExpectedMax strategy.

In two- or more-dimensional systems where the components of the sample data are strongly correlated, it is not proper to divide the input space into several subspaces without considering the correlation. In this paper, we propose the usage of the method of principal component in order to uncorrelate and remove any redundancy from the input space of the adaptive neuro-fuzzy inference system (ANFIS). This leads to an effective partition of the input space to the fuzzy model and significantly reduces the modeling error. A computer simulation for two frequently used benchmark problems shows that ANFIS with the uncorrelation process performs better than the original ANFIS under the same conditions.

This paper presents an algorithm for viewpoint-based similarity discernment of linguistic concepts on Semantic Network Array Processor (SNAP). The viewpoint-based similarity discernment plays a key role in retrieving similar propositions. This is useful for advanced knowledge processing areas such as analogical reasoning and case-based reasoning. The algorithm assumes that a knowledge base is constructed for SNAP, based on information acquired from the WordNet linguistic database. The algorithm identifies paths on the knowledge base between each given concept and a given viewpoint concept, then computes a similarity degree between the two concepts based on the number of nodes shared by the paths. A small scale knowledge base was constructed and an experiment was conducted on a SNAP simulator that demonstrated the feasibility of this algorithm. Because of SNAP's scalability, the algorithm is expected to work similarly on a large scale knowledge base.

This paper proposes high-speed similitude retrieval schemes for a viewpoint-based similarity discrimination system (VB-SDS) and presents analytical and experimental performance evaluations. The VB-SDS, which contains a huge set of semantic definitions of commonly used words and computes semantic similarity between any two words under a certain viewpoint, promises to be a very important module in analogical and case-based reasoning systems that provide solutions under uncertainty. By computing and comparing similarities for all words contained in the system, the most similar word for a given word can be retrieved under a given viewpoint. However, the time this consumes makes the VB-SDS unsuitable for inference systems. The proposed schemes reduce search space based on the upper bound of a similarity calculation function to increase retrieval speed. An analytical evaluation shows the schemes can achieve a thousand-fold speedup and confirmed through experimental results for a VB-SDS containing about 40,000 words.