Using Bandwidth-Space Partitioning to Improve Cell Coverage and Near-Far Unfair Access Problem in a Noise-Limited CDMA Cellular Network

This paper concerns CDMA cellular networks equipped with conventional matched filter receivers. For this type of cellular networks, two problems exist. One is the inverse relationship between the coverage and capacity, and the other one is the near-far unfair access problem. To resolve these two problems, a bandwidth-space partitioning technique is adopted. Several admission control schemes based on the bandwidth-space partitioning technique are proposed, and their performances are evaluated based on the simulation.Shih-Tsung Yang received his B.S. (1989) and M.S. (1991) degrees in electrical engineering from the National Taiwan University, and Ph.D. (1999) degree in electrical and computer engineering from University of Maryland, College Park.From 1999 to 2000, he was with SBC Technology Resourtces, Inc. where he was involved in the network architecture design for the ATM based networks. From November 2000 to May 2003, he was with Transilica/Microtune where he was involved in the design of Baseband and RF for the wireless communications. Since May 2003, Dr. Yang is now with Genesys Logic, Inc. where he is responsible for the baseband design of the WLAN and Ethernet.Dr. Yang’s research interests are in the area of Digital Communications, Communication networks and signal processing. E-mail: thomas.yang@genesyslogic.com.twAnthony Ephremides received his B.S. degree from the National Technical University of Athens (1967), and M.S. (1969) and Ph.D. (1971) degrees from Princeton University, all in Electrical Engineering. He has been at the University of Maryland since 1971, and currently holds a joint appointment as Professor in the Electrical Engineering Department and in the Institute of Systems Research (ISR) of which he is a founding member. He is co-founder of the NASA Center for Commercial Development of Space on Hybrid and Satellite Communications Networks established in 1991 at Maryland as an off-shoot of the ISR. He served as Co-Director of that Center from 1991 to 1994.He was a Visiting Professor in 1978 at the National Technical University in Athenas, Greece, and in 1979 at the EECS Department of the University of California, Berkeley, and at INRIA, France. During 1985-1986 he was on leave at MIT and ETH in Zurich, Switzerland. He was the General Chairman of the 1986 IEEE Conference on Decision and Control in Athens, Greece and of the 1991 IEEE International Symposium on Information Theory in Budapest, Hungary. He also organized two workshops on Information theory in 1984 (Hot Springs, VA) and in 1999 (Metsovo, Greece). He was the Technical Program Co-Chair of the IEEE INFOCOM in New York City in 1999 and of the IEEE International Symposium on Information theory in Sorrento, Italy in 2000. He has also been the Director of the Fairchild Scholars and Doctoral Fellows Program, an academic and research partnership program in Satellite Communications between Fairchild Industries and the University of Maryland. He won the IEEE Donald E. Fink Prize Paper Award (1992) and he was the first recipient of the Sigmobile Award of the ACM (Association of Computer Machinery) for contributions to wireless communications in 1997. He has been the President of the Information Theory Society of the IEEE (1987) and has served on its Board of Governors almost continuously from 1981 until the present. He was elected to the Board of Directors of the IEEE in 1989 and 1990. Dr. Ephremides has authored or co-authored over 100 technical journal papers and 300 technical conference presentations. He has also contributed chapters to several books and edited numerous special issues of scientific journals. He has also won awards from the Maryland Office of Technology Liaison for the commercialization of products and ideas stemming from his research. He has served on the Editorial Boards of the IEEE Transactions on Automatic Control, IEEE Transactions on Information theory, the Journal of Wireless Networks, and the International Journal of Satellite Communications.He has been the Dissertation Supervisor of over twenty Ph.D. students who now hold prominent positions in academia, industry, and research labs. He is the founder and President of Pontos, Inc., a Maryland company that provides technical consulting services, since 1980.Dr. Ephremides’ interests are in the areas of communication theory, communication systems and networks, queueing systems, signal processing, and satellite communications. His research has been continuously supported since 1971 by NSF, NASA, ONR, ARL, NRL, NSA, and Industry. E-mail: tony@eng.umd.edu     

Efficient use of route requests for loop-free on-demand routing in ad hoc networks

On-demand routing protocols flood a route request (RREQ) identified by a unique source-sequenced label (SSL) to build directed acyclic graphs (DAG) consisting of possible paths to destinations, and use reverse paths along such DAGs to send route replies from the destinations. We present the first framework that uses only the SSL of the RREQ to establish loop-free paths without requiring additional mechanisms or information, e.g., per destination-sequence numbers or source routing. We present the Labeled Successor Routing (LSR) protocol, which stores the SSL of the RREQ as a route request label (RRL) at the nodes that participated in the creation of the RREQ DAG. The RRLs allow the source of the RREQ to identify neighbors as loop-free successors to the destination, which reduces network-wide RREQ flooding. Simulations results for scenarios consisting of networks of 50 and 100 mobile nodes show that LSR performs comparably or better than the Dynamic Source Routing (DSR) protocol, the Ad hoc On-demand Distance Vector (AODV) routing protocol, and the Optimized Link State Routing (OLSR) protocol.     

Genetic programming of conventional features to detect seizure precursors

This paper presents an application of genetic programming (GP) to optimally select and fuse conventional features (C-features) for the detection of epileptic waveforms within intracranial electroencephalogram (IEEG) recordings that precede seizures, known as seizure precursors. Evidence suggests that seizure precursors may localize regions important to seizure generation on the IEEG and epilepsy treatment. However, current methods to detect epileptic precursors lack a sound approach to automatically select and combine C-features that best distinguish epileptic events from background, relying on visual review predominantly. This work suggests GP as an optimal alternative to create a single feature after evaluating the performance of a binary detector that uses: (1) genetically programmed features; (2) features selected via GP; (3) forward sequentially selected features; and (4) visually selected features. Results demonstrate that a detector with a genetically programmed feature outperforms the other three approaches, achieving over 78.5% positive predictive value, 83.5% sensitivity, and 93% specificity at the 95% level of confidence.     

Forming consensus in the networks of knowledge

Information granules, such as e.g., fuzzy sets, capture essential knowledge about data and the key dependencies between them. Quite commonly, we may envision that information granules (fuzzy sets) have become a result of fuzzy clustering and therefore could be succinctly represented in the form of some fuzzy partition matrices. Interestingly, the same data set could be represented from various standpoints and this multifaceted view yields a collection of different partition matrices being reflective of the higher-order granular knowledge about the data. The levels of specificity of the clusters the data are organized into could be quite different—the larger the number of clusters, the more detailed insight into the structure of data becomes available. Given the granularity of the resulting constructs (rather than plain data themselves), one could view a collection of partition matrices as a certain type of a network of knowledge. Considering a variety of sources of knowledge encountered across the network, we are interested in forming consensus between them. In a nutshell, this leads to the construction of certain fuzzy partition matrices which “reconcile” the knowledge captured by the individual partition matrices. Given that the granularity of the sources of knowledge under consideration could vary quite substantially, we develop a unified optimization perspective by introducing fuzzy proximity matrices that are induced by the corresponding partition matrices. In the sequel, the optimization is realized on a basis of these proximity matrices. We offer a detailed algorithm and illustrate its performance using a series of numeric experiments.     

Automatic generation of explanations: AGE

Explaining how engineering devices work is important to students, engineers, and operators. In general, machine generated explanations have been produced from a particular perspective. This paper introduces a system called automatic generation of explanations (AGE) capable of generating causal, behavioral, and functional explanations of physical devices in natural language. AGE explanations can involve different user selected state variables at different abstraction levels. AGE uses a library of engineering components as building blocks. Each component is associated with a qualitative model, information about the meaning of state variables and their possible values, information about substances, and information about the different functions each component can perform. AGE uses: (i) a compositional modeling approach to construct large qualitative models, (ii) causal analysis to build a causal dependency graph, (iii) a novel qualitative simulation approach to efficiently obtain the system's behavior on large systems, and (iv) decomposition analysis to automatically divide large devices into smaller subsystems. AGE effectiveness is demonstrated with different devices that range from a simple water tank to an industrial chemical plant.     

A two-pass approach for handling out-of-vocabulary words in a large vocabulary recognition task

This paper addresses the problem of recognizing a vocabulary of over 50,000 city names in a telephone access spoken dialogue system. We adopt a two-stage framework in which only major cities are represented in the first stage lexicon. We rely on an unknown word model encoded as a phone loop to detect OOV city names (referred to as ‘rare city’ names). We use SpeM, a tool that can extract words and word-initial cohorts from phone graphs from a large fallback lexicon, to provide an N-best list of promising city name hypotheses on the basis of the phone graph corresponding to the OOV. This N-best list is then inserted into the second stage lexicon for a subsequent recognition pass.Experiments were conducted on a set of spontaneous telephone-quality utterances; each containing one rare city name. It appeared that SpeM was able to include nearly 75% of the correct city names in an N-best hypothesis list of 3000 city names. With the names found by SpeM to extend the lexicon of the second stage recognizer, a word accuracy of 77.3% could be obtained. The best one-stage system yielded a word accuracy of 72.6%. The absolute number of correctly recognized rare city names almost doubled, from 62 for the best one-stage system to 102 for the best two-stage system. However, even the best two-stage system recognized only about one-third of the rare city names retrieved by SpeM. The paper discusses ways for improving the overall performance in the context of an application.     

Desktop inkjet printer as a tool to print conducting polymers

In order to exploit mechanical flexibility of organic-based electronic devices, conducting polymer anodes, such as polyaniline or poly(3,4-ethylenedioxy)-thiophene-poly(styrene sulfonate) (PEDOT-PSS), have been extensively studied. Along with the use of solution based processing techniques, conducting polymers can simplify the device fabrication procedure and yield themselves easily to printing techniques. In this paper, we present the results of utilizing desktop inkjet printer as a tool for direct printing and patterning of conducting polymer. Design of printable patterns and adjustment of printing parameters can be performed using any software such as Power Point. PEDOT-PSS suspension can be loaded into an inkjet cartridge and deposited on a given substrate in any designed pattern. The gray-scale color scheme can be employed to control the layer thickness and sheet resistivity of the inkjet printed layers. These layers are then used as anodes in organic light-emitting devices (OLEDs).     

Genetically optimized fuzzy polynomial neural networks with fuzzy set-based polynomial neurons

In this paper, we propose and investigate a new category of neurofuzzy networks—fuzzy polynomial neural networks (FPNN) endowed with fuzzy set-based polynomial neurons (FSPNs) We develop a comprehensive design methodology involving mechanisms of genetic optimization, and genetic algorithms (GAs) in particular. The conventional FPNNs developed so far are based on the mechanisms of self-organization, fuzzy neurocomputing, and evolutionary optimization. The design of the network exploits the FSPNs as well as the extended group method of data handling (GMDH). Let us stress that in the previous development strategies some essential parameters of the networks (such as the number of input variables, the order of the polynomial, the number of membership functions, and a collection of the specific subset of input variables) being available within the network are provided by the designer in advance and kept fixed throughout the overall development process. This restriction may hamper a possibility of developing an optimal architecture of the model. The design proposed in this study addresses this issue. The augmented and genetically developed FPNN (gFPNN) results in a structurally optimized structure and comes with a higher level of flexibility in comparison to the one we encounter in the conventional FPNNs. The GA-based design procedure being applied at each layer of the FPNN leads to the selection of the most suitable nodes (or FSPNs) available within the FPNN. In the sequel, two general optimization mechanisms are explored. First, the structural optimization is realized via GAs whereas the ensuing detailed parametric optimization is carried out in the setting of a standard least square method-based learning. The performance of the gFPNN is quantified through experimentation in which we use a number of modeling benchmarks—synthetic and experimental data being commonly used in fuzzy or neurofuzzy modeling. The obtained results demonstrate the superiority of the proposed networks over the models existing in the references.     

Radio resource management for a mobile network with TD-CDMA

In the paper, we proposed a resource allocation method employing maximal compatible cliques to make use of channel bandwidth as efficiently as possible in a TD-CDMA system. Furthermore, the strength of beacon signals was also measured to decide whether or not to reserve resources for hand-off users, in order to reduce their dropping probabilities. Finally, we conducted several experiments to validate that our method could make use of the finite resources efficiently, thereby revealing better blocking probabilities and dropping probabilities than other methods.     

Hierarchical routing in multi-domain optical networks

Given the increasing adoption of DWDM networking technology, there is now a growing need to address distributed inter-domain lightpath provisioning issues. Although inter-domain provisioning has been well studied for packet/cell-switching networks, the wavelength dimension presents many additional challenges. To address this challenge, a detailed hierarchical routing GMPLS-based framework for provisioning all-optical and opto-electronic multi-domain DWDM networks is presented. The scheme adapts various topology abstraction schemes to improve routing scalability and lower inter-domain blocking probabilities. Related inter-domain lightpath RWA and signaling schemes are also tabled. Performance analysis results are also presented to demonstrate the effectiveness of the proposed mechanisms along with directions for future research work.