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Uykan, Zekeriya (Dogus Author) -- Full conference title: 2011 XXIII International Symposium on Information, Communication and Automation Technologies, ICAT 2011: 27-29 October 2011, Sarajevo, Bosnia Herzegovina. The technological evolution of the wireless cellular systems has been very rapid in last two decades. In the coming decade of "converging wireless networks/systems/ecosystems", there is an increasing demand on achieving very high data rates ubiquitously even with high mobile speeds as if we connected to a wired ADSL! Radio Resource Management (RRM) for the emerging wireless systems will be the key mechanism for achieving such high data rates. Indeed, RRM has already been a hot research area in both academia and industry for decades. And due to the complexity of the emerging wireless systems, an interdisciplinary approach and/or methodology is needed to tackle the new RRM challenges. In this paper, we provide a control engineering view onto some of the RRM challenges in emerging wireless networks, with a special emphasis on distributed power control. For example, we establish a link between power control design and dynamic neural networks, two different areas whose scope of interest, motivations and settings are completely different. Here, we emphasize the importance and the need of interdisciplinary approach. Some subjects to be addressed within the paper shall include future-generation cellular/ "noncellular" systems, radio resource management challenges, energy efficiency and distributed power control algorithms, variable-structure-systems based power control, channel/frequency allocation, spectral-clustering based channel allocation, Hopfield neural networks. bhT,m:tel - imate prijatelije, ENERGOINVEST-SUI - Sistemi upravljanja energijom, Microsoft

ÖZET Bu çalışmada, Bölüm-2'de Yapay Sinir Ağı'nın bir türü olan Radyanlı Tabanlı Fonksiyonlar Ağı (RTF A) tanıtılmış ve bu ağ için bugüne kadar literatürde geliştirilen öğrenme algoritmalarına bir seçenek olarak, Zekeriya Uykan ve Cüneyt Güzeliş tarafından dört farklı şekilde uygulanabilen yeni bir öğrenme yöntemi (Giriş-Çıkış uzayında öbekleme, GÇÖ) önerilmiştir. Bölüm-3'te doğrusal olmayan sistem tanımı ile ilgili, a) Polinom Yöntemi (Chen ve Billings, 1 989), b) RTFA Yöntemi tanıtılmış ve her iki yöntem için Dik En- Küçük Kareler Yöntemi (Orthogonal Least Squares) ile en uygun terimlerin nasıl seçildiği açıklanmıştır. Literatürde RTFA ile sistem tanımada kullanılan yöntemlere seçenek olarak GÇÖ Yöntemi uygulanmıştır. Ayrıca, sadece örnek giriş-çıkış çiftlerinden yararlanarak, RTFA'da merkezler belli olmak üzere istenen performansın (belirlenen amaç ölçütünün) sağlanabilmesi için saklı katmanda kullanılması gereken hücre sayısının belirlenmesinde herhangi bir öğrenme algoritması koşturmaksızın sadece Dik EKY'nin nasıl kullanıldığı açıklanmıştır. Bu yöntem, Çok-Katmanlı Algılayıcı'da saklı katmanlardaki hücre sayılarının belirlenmesinde kullanılan yöntemlere (arındırma, geliştirme) bir seçenek sunar. Bölüm-4'te Bulanık Kontrolör (BK) ve Nöral Kontrolör (NK) arasındaki ilişkiler araştırılmıştır. Bazı kısıtlamalar altında BK ve NK'nin matematiksel olarak eşdeğer olduğu gösterilmiştir. Ayrıca, BK'nin klasik (bulanık olmayan) kontrolörle ilişkisi araştırılmış ve klasik kontrolörün BK'nin özel bir durumu olduğu gösterilmiştir. Bu tür çalışmalarda `BK tasarımında ve BK sistemlerinin kararlılık analizinde klasik kontrol yöntemlerinden yararlanılabilir mi T sorularına cevap aranır. Literatürde bu konudaki yayınların azlığı, bu çalışmaların önemini arttırmaktadır. Bölüm-55te YSA yöntemleri kullanılarak BK tasarımının nasıl yapıldığı açıklanmıştır. Bu tür çalışmaların amacı, birbirinden farklı alanlar gibi görünen YSA ve Bulanık Mantık konularının beraberce ele alınabileceği ve böylelikle biri için geliştirilen bir yöntemin diğeri için de uygulanabileceği bir ortak çalışma alanı oluşturmaktır. SUMMARY SYSTEM IDENTIFICATION USING RBFN AND NEURO-FUZZY CONTROL After McCulloch and Pitts established the basic principles of the Neural Computation in 1943, Hebb's contribution was considered the basis for the development of many Artificial Neural Networks(ANN). The development of the Back Propagation Algorithm for training Feedforward Multi-Layer Neural Networks (MLNN) provided the wide use of ANN in many different areas. : signal processing, robotics, fault diagnosis, control, etc. On the other hand, another important theory, Fuzzy Logic (FL), occured in parallel to the ANN development. The basic principles of FL were introduced by Zadeh in 1965, having its first application to the area of Control Systems in 1975. However Polish mathemetician Lukasiewiecz developed multiple valued locig in 1920's and the key ideas of the theory were thought of by Black in 1937. An alternative to MLNN is the Radial Basis Function Network (RBFN). The RBF method has traditionally been used for strict interpolation in multidimensional space by Powell (1985), Micchelli (1986) and Broomhead and Lowe (1988). The contraction of a RBFN in its most basic form involves three different layers. The input layer is made up of source nodes (sensory units). The second layer is a hidden layer of high enough dimension. The output layer supplies the responce of the network. The transformation from the input space to the hidden-unit space is nonlinear, whereas the transformation from the hidden unit space to the output space is linear. >F(s) Figure- 1) Radial Basis Function Network. An RBF expansion with p-inputs and a scalar output implements a nonlinear mapping according to the nonlinear relationship as shown in (i). viF(X) = g^S-ci) (1) where {

In this two-part paper, we consider a system consisting of bidirectional wireless links that interfere with each other, which has been the of focus of intensive research recently in emerging wireless systems like Device-to-Device (D2D) communications underlaying cellular networks, heterogeneous networks, and small-cell networks. The problem is allocating the time resources (transmission orders) and frequency resources (channels) among the transmitters such that the overall network interference is minimized. Here, transmission order (TO) is a novel dimension for optimization. In Part I, we analyze the TO optimization problem in the presence of channel allocation (CA), i.e., joint CA and the TO optimization problem from a game theoretic perspective, and prove that the joint optimization problem can be formulated as an exact potential game, which has at least one "pure strategy Nash Equilibrium" for any subset of the complete CA-TO action set and for any initial CA and TO conditions. We also show that the proposed joint CA and TO game is equal to the max-cut of a novel TO-dependent holistic system interference graph. In Part II, we present novel joint CA-TO algorithms and their performance analysis in D2D communications underlays. IEEE Communications Society

This is the second in a two-part series of papers on transmission order (TO) optimization in the presence of channel allocation (CA), i.e., joint optimization of the TO selection and CA problem, for interfering bidirectional wireless links. Part I of this paper thoroughly analyzes the joint optimization problem from a game theoretic perspective for a general deterministic setting. Here in Part II, we present novel distributed and centralized CA-TO algorithms, together with their performance analysis, for Device-to-Device (D2D) communications underlaying cellular networks based on the findings in Part I of this paper. Here, TO is a novel dimension for optimization. In Part II, we propose and analyze novel two distributed and one centralized joint CA-TO algorithms. Our investigations show that: i) our algorithms contain many of the existing TO algorithms and CA algorithms as its special cases and can thus be considered as a general framework for the joint CA and TO optimization. The computer simulations for TDD-based D2D communications underlaying cellular network show that the proposed distributed and centralized joint CA-TO algorithms remarkably outperform the reference algorithms. IEEE Communications Society

Being inspired by the Hopfield neural networks (Hopfield (1982) and Hopfield and Tank (1985)) and the nonlinear sigmoid power control algorithm for cellular radio systems in Uykan and Koivo (2004), in this paper, we present a novel discrete recurrent nonlinear system and extend the results in Uykan (2009), which are for autonomous linear systems, to nonlinear case. The proposed system can be viewed as a discrete-time realization of a recently proposed continuous-time network in Uykan (2013). In this paper, we focus on discrete-time analysis and provide various novel key results concerning the discrete-time dynamics of the proposed system, some of which are as follows: (i) the proposed system is shown to be stable in synchronous and asynchronous work mode in discrete time; (ii) a novel concept called Pseudo-SINR (pseudo-signal-to-interference-noise ratio) is introduced for discrete-time nonlinear systems; (iii) it is shown that when the system states approach an equilibrium point, the instantaneous Pseudo-SINRs are balanced; that is, they are equal to a target value. The simulation results confirm the novel results presented and show the effectiveness of the proposed discrete-time network as applied to various associative memory systems and clustering problems.

Uykan, Zekeriya (Dogus Author) -- Jantti, Riku (Dogus Author) Device-to-device (D2D) communications underlaying cellular networks is a promising concept that has several advantages over the traditional cellular networks. In the TDD system, the frame structure defines the order of uplink and downlink transmission slots. Typically, a TDD system is synchronized, and the same transmission order (TO) is used in all cells. In a direct D2D link, we have the freedom of selecting the TO of the devices freely. To our best knowledge, no paper has explicitly examined the TO optimization problem in D2D communications underlaying cellular network so far. In this paper, we focus exactly on this problem: once the proper co-channel D2D pairs are determined in the network, how do we minimize the network interference by optimally determining the TOs in all D2D links (together with co-channel cellular links) in the network, which is an NP-complete problem. In this paper, we formulate the TO optimization problem from a graph theoretic point of view: 1) we show that the TO optimization problem is equal to a constraint balanced min-cut graph partitioning problem of our defined augmented graph; and 2) we propose and analyze a distributed and a centralized efficient asynchronous clustering algorithm for solving the TO optimization probleme quivalently for themin-cut of our proposed augmented graph. Computer simulations for the TDD-based D2D underlaying cellular network show that the proposed distributed and centralized algorithms, called ABCAMiC and CABCAMiC, respectively, 1) remarkably outperform the reference case where all TOs are fixed, 2) converge within a relatively small number of steps and generally converge in only a few epochs even for a large number of cellular and D2D users, and 3) the expected performance of the (partly/fully) distributed ABCAMiC is almost equal to that of the centralized solution CABCAMiC, which generally gives near-global optimal solution to the TO optimization problem.

Uykan, Zekeriya (Dogus Author) The channel allocation problem in cellular radio systems is NP-complete, and thus its general solution is not known for even the 2-channel case. It is well known that the link gain system matrix (or received-signal power system matrix) of the radio network is (and may be highly) asymmetric, and that as the Hopfield neural network is applied to optimization problems, its weight matrix should be symmetric. The main contribution of this paper is as follows: turning the channel allocation problem into a maxCut graph partitioning problem, we propose a simple and effective continuous-time Hopfield neural network-based solution by determining its symmetric weight matrix from the asymmetric received-signal-power-system matrix. Computer simulations confirm the effectiveness and superiority of the proposed solution as compared to standard algorithms for various illustrative cellular radio scenarios for the 2-channel case.

The Hopfield neural network (HNN) has been widely used in numerous different optimization problems since the early 1980s. The convergence speed of the HNN (already in high gain) eventually plays a critical role in various real-time applications. In this brief, we propose and analyze a generalized HNN which drastically improves the convergence speed of the network, and thus allows benefiting from the HNN capabilities in solving the optimization problems in real time. By examining the channel allocation optimization problem in cellular radio systems, which is NP-complete and in which fast solution is necessary due to time-varying link gains, as well as the associative memory problem, computer simulations confirm the dramatic improvement in convergence speed at the expense of using a second nonlinear function in the proposed network.

In this study we propose a discrete-time Hopfield Neural Network based clustering algorithm for text clustering for cases L = 2(q) where L is the number of clusters and q is a positive integer. The optimum general solution for even 2-cluster case is not known. The main contribution of this paper is as follows: We show that i) sum of intra-cluster distances which is to be minimized by a text clustering algorithm is equal to the Lyapunov (energy) function of the Hopfield Network whose weight matrix is equal to the Laplacian matrix obtained from the document-by-document distance matrix for 2-cluster case; and ii) the Hopfield Network can be iteratively applied to text clustering for L = 2(k). Results of our experiments on several benchmark text datasets show the effectiveness of the proposed algorithm as compared to the k-means.

In this paper, we focus on so-called nonlinear and unfeasible FMA (Foschini-Miljanic Algorithm) case, where the system matrix is unfeasible and the transmit powers are limited. We stabilize the nonlinear dynamic system by a normalization term, as in Oja's approach for his model. We draw various conclusions regarding the linear and nonlinear cases: i) In linear case, we show that the SINRs in the stabilized linear wireless network converge to some constants which are inversely related to the dominant eigenvalue of normalized link gain matrix. ii) In nonlinear case, we show that the proposed nonlinear network, which includes also Oja's principal component analyzer as a special case, can be applied to solving (combinatorial) optimization problems. The performance of the proposed network is examined in channel allocation problem in cellular radio systems, which is NP complete, and in content-addressable memory design problem. The simulation results confirm the effectiveness and the superiority of the proposed nonlinear network as compared to the Hopfield Neural Network for the very same weight matrices in optimization problems. IEEE Computat Intelligence Soc; United Dev Co PSC; Qatar Petrochem Co; ExxonMobil; Texas A&M Univ atar & Asia Pacif Neural Network Assembly; European Neural Network Soc; Japanese Neural Network Soc; Qatar Petr; Int Neural Network Soc.