Georgios I. Papadimitriou

Optical switching: switch fabrics, techniques, and architectures

The switching speeds of electronics cannot keep up with the transmission capacity offered by optics. All-optical switch fabrics play a central role in the effort to migrate the switching functions to the optical layer. Optical packet switching provides an almost arbitrary fine granularity but faces significant challenges in the processing and buffering of bits at high speeds. Generalized multiprotocol label switching seeks to eliminate the asynchronous transfer mode and synchronous optical network layers, thus implementing Internet protocol over wavelength-division multiplexing. Optical burst switching attempts to minimize the need for processing and buffering by aggregating flows of data packets into bursts. In this paper, we present an extensive overview of the current technologies and techniques concerning optical switching.

Using learning automata for adaptive push-based data broadcasting in asymmetric wireless environments

Push systems are not suitable for applications with a priori unknown, dynamic client demands. This paper proposes an adaptive push-based system. It suggests the use of a learning automaton at the broadcast server to provide adaptivity to an existing push system while maintaining its computational complexity. Using simple feedback from the clients, the automaton continuously adapts to the client population demands so as to reflect the overall popularity of each data item. Simulation results are presented that reveal the superior performance of the proposed approach in environments with a priori unknown, dynamic client demands.

Learning automata-based receiver conflict avoidance algorithms for WDM broadcast-and-select star networks

The increasing bandwidth demands of the emerging new generation of computer communication networks have led to the utilization of optical fiber as a transmission medium. A new receiver conflict avoidance algorithm for wavelength-division multiplexing (WDM) broadcast-and-select star networks is introduced. The proposed algorithm is based on the use of learning automata in order to reduce the number of receiver conflicts and, consequently, improve the performance of the network. According to the proposed scheme, each node of the network is provided with a learning automaton; the learning automaton decides which of the packets waiting for transmission will be transmitted at the beginning of the next time slot. The asymptotic behavior of the system, which consists of the automata and the network, is analyzed and it is proved that the probability of choosing each packet asymptotically tends to be proportional to the probability that no receiver conflict will appear at the destination node of this packet. Furthermore, extensive simulation results are presented, which indicate that significant performance improvement is achieved when the proposed algorithm is applied on the basic DT-WDMA protocol.

Learning automata-based polling protocols for wireless LANs

A learning automata-based polling (LEAP) protocol for wireless LANs, capable of operating efficiently under bursty traffic conditions, is introduced. We consider an infrastructure wireless LAN, where the access point (AP) is located at the center of a cell which comprises a number of mobile stations. According to the proposed protocol, the mobile station that is granted permission to transmit is selected by the AP by means of a learning automaton. The learning automaton takes into account the network feedback information in order to update the choice probability of each mobile station. It is proved that the learning algorithm asymptotically tends to assign to each station a portion of the bandwidth proportional to the stations needs. LEAP is compared to the randomly addressed polling and group randomly addressed polling protocols and is shown to exhibit superior performance under bursty traffic.

Guest editorial learning automata: theory, paradigms, and applications

L EARNING automata [1] have attracted a considerable interest in the last three decades. They are adaptive decision making devices that operate in unknown stochastic environments and progressively improve their performance via a learning process. They have been initially used by psychologists and biologists to describe the human behavior from both psychological and biological viewpoints. Learning automata have made a significant impact on all areas of engineering. They can be applied to a broad range of modeling and control problems, which are characterized by nonlinearity and a high degree of uncertainty. Learning automata have some key features, which make them applicable to a broad range of applications: they combine rapid and accurate convergence with a low computational complexity. Learning is defined as any permanent change in behavior as a result of past experience, and a learning system should therefore have the ability to improve its behavior with time, toward a final goal. In a purely mathematical context, the goal of a learning system is the optimization of a function not known explicitly [2]. Thirty years ago, Tsypkin [3] introduced a method to reduce the problem to the determination of an optimal set of parameters and then applied stochastic hill-climbing techniques. Tsetlin [4] started the work on learning automata during the same period. An alternative approach to applying stochastic hillclimbing techniques, introduced by Narendra and Viswanathan [5], is to regard the problem as one of finding an optimal action out of a set of allowable actions and to achieve this using stochastic automata. The difference between the two approaches is that the former updates the parameter space at each iteration while the latter updates the probability space. The stochastic automaton attempts a solution of the problem without any information on the optimal action. One action is selected at random, the response from the environment is observed, action probabilities are updated based on that response,

Learning-automata-based TDMA protocols for broadcast communication systems with bursty traffic

A learning automata-based time-division multiple-access protocol for broadcast networks, which is capable of operating efficiently under bursty traffic conditions, is introduced. According to the proposed protocol, the station which grants permission to transmit at each time slot is selected by means of learning automata. The learning automata update the choice probability of each station according to the network feedback information in such a way that it asymptotically tends to be proportional to the probability that this station is ready. In this manner, the number of idle slots is minimized and the network performance is significantly improved. Furthermore, the portion of the bandwidth assigned to each station is dynamically adapted to the stations needs.

Self-adaptive TDMA protocols for WDM star networks: a learning-automata-based approach

A learning-automata-based protocol for WDM passive star networks, which is capable of operating efficiently under bursty and correlated traffic, is introduced. According to the proposed protocol, the stations which grant permission to transmit at each time slot, are selected by means of learning automata. The choice probabilities of the selected stations are updated by taking into account the network feedback information. The probability updating scheme is designed in such a way, that the number of idle slots tends to be minimized, while the bandwidth of each wavelength Is allocated to the stations according to their needs.

A new approach to the design of reinforcement schemes for learning automata: Stochastic estimator learning algorithm

Abstract In this paper a new approach to the design of S-model ergodic reinforcement learning algorithms is introduced. The new scheme utilizes a stochastic estimator and is able to operate in non-stationary environments with high accuracy and a high adaptation rate. According to the stochastic estimator scheme, which is the first attempt in the field, the estimates of the mean rewards of actions are computed stochastically. So, they are not strictly dependent on the environmental responses. The dependence between the stochastic estimates and the deterministic estimators contents is more relaxed if the latter are not updated. In this way actions that have not been selected recently have the opportunity to be estimated as ‘optimal’, to increase their choice probability and consequently to be selected. Thus, the estimator is always recently updated and consequently able to adapt to environmental changes. The performance of the presented Stochastic Estimator Learning Automaton (SELA) is superior to all previous well-known S-model ergodic schemes. Furthermore it is proved that SELA is ϵ-optimal in every S-model random environment.

Hierarchical discretized pursuit nonlinear learning automata with rapid convergence and high accuracy

A new absorbing multiaction learning automaton that is epsilon-optimal is introduced. It is a hierarchical discretized pursuit nonlinear learning automaton that uses a new algorithm for positioning the actions on the leaves of the hierarchical tree. The proposed automaton achieves the highest performance (speed of convergence, central processing unit (CPU) time, and accuracy) among all the absorbing learning automata reported in the literature up to now. Extensive simulation results indicate the superiority of the proposed scheme. Furthermore, it is proved that the proposed automaton is epsilon-optimal in every stationary stochastic environment. >

Adaptive wireless networks using learning automata

Wireless networks operate in environments with unknown and time-varying characteristics. The changing nature of many of these characteristics will significantly affect network performance. This fact has a profound impact on the design of efficient protocols for wireless networks and as a result adaptivity arises as one of the most important properties of these protocols. Learning automata are artificial intelligence tools that have been used in many areas where adaptivity to the characteristics of the wireless environment can result in a significant increase in network performance. This article reviews state of the art approaches in using learning automata to provide adaptivity to wireless networking.