Neelakanta, Perambur S.

The research envisaged and reported in this thesis refers to finding comprehensive algorithms to determine the handoff probabilities of new and handoff calls encountered in mobile communications. The traditional expressions for these probabilities that are reported in the literature, are deduced only on the basis of call arrival statistics applied to RF links between base station (BS) and the mobile unit (MU). However, such radio links inevitably suffer from fading. These channels are normally modeled by appropriate probability density functions (pdfs) of the faded signal envelope. Rayleigh, Rician and Nakagami-m distributions are popularly considered in depicting such fading channel characteristics. The traditional (queueing-theoretic) based estimation of handoff probabilities does not account for the hysteresis-specific handoff statistics in the relevant fading channels. This is in contrary to the reality, inasmuch as fading is an inherent part of RF channels in mobile communications. The present study offers a tractable method of combining queuing-theoretic (call arrival) statistics and the hysteresis-crossing statistics of a RSS metric so as to obtain proper expressions for new and handoff call handoff probabilities. The (upper and lower) bound specified spread of the handoff probabilities indicates that care should be exercised in resource allocation efforts with a margin. To the best of the knowledge of the author, this research exercise is new and has not been reported elsewhere in open literature.

This study presents computer simulations directed to address the performance of a baseband Bluetooth system. These simulations help to understand the performance impairments experienced by the Bluetooth systems in the presence of external electromagnetic interference (EMI) and interference that arises from piconet neighbors. The simulation results show that the effect of small scale fading is an important factor that decides the performance of a Bluetooth system. Additionally, it is shown that the interference from piconet neighbors or the interference that stems from devices belonging to the same piconet can be reduced by using frequency-hoping and time synchronization improvised within the same piconets. The test simulations were performed using MatlabRTM and SimulinkRTM tools.

The objective of this research is to determine the macroscopic behavior of packet transit-times across the global Internet cloud using an artificial neural network (ANN). Specifically, the problem addressed here refers to using a "fast-convergent" ANN for the purpose indicated. The underlying principle of fast-convergence is that, the data presented in training and prediction modes of the ANN is in the entropy (information-theoretic) domain, and the associated annealing process is "tuned" to adopt only the useful information content and discard the posentropy part of the data presented. To demonstrate the efficacy of the research pursued, a feedforward ANN structure is developed and the necessary transformations required to convert the input data from the parametric-domain to the entropy-domain (and a corresponding inverse transformation) are followed so as to retrieve the output in parametric-domain. The fast-convergent or fast-computing ANN (FC-ANN) developed is deployed to predict the packet-transit performance across the Internet. (Abstract shortened by UMI.)

The research addressed and presented in this dissertation can be placed within the broad scope of telecommunications technoeconomics. The relevant efforts include the subject-matter of identifying the issues posed by emerging technologies, related revenue considerations and environmental issues in modern telecommunications practice specific to service providers' perspectives. The topic-wise problems studied and analyzed are as follows: (1) A comprehensive portrayal of managerial concerns and considerations on the technoeconomical perspectives vis-a-vis modern telecommunications; (2) Relevant analytical studies pertinent to: (1) "Greenfield starts" in a fresh, telecommunications deployment in a virgin service zone; (2) Embedded architectures; (3) Technology enhancements; (4) Environmental issues. The greenfield effort is analyzed to portray the feasibility of achieving technoeconomically optimal alternative designs. The embedded architecture refers to the prevailing infrastructure and their optimal usage is indicated via an arbitrated traffic-sharing technique. Concerning technology enhancements, "all-optical" technology is indicated as the ultimate goal. However, in the interim period, the optimal use of transitory technology such xDSL, MPLS, and others, is suggested and studied. Lastly, the environmental implications that coexist with technoeconomical impacts on modern telecommunications deployment are analyzed.

The research proposed and elaborated in this dissertation is concerned with the development of new and smart techniques for subchannel allocation in the asymmetric digital subscriber lines (ADSLs). The ADSL refers to a class of access technology adopted currently in modern telecommunications to make use of the available channel capacity on the twisted copper-wires, which exist in the "last-mile" between the central office and subscribers. This available spectrum on the voice grade copper-lines is judiciously used to transport broadband data over the last mile regime. For this purpose, the channel capacity on the access lines is segmented in subchannels and the traffic to be transported is placed on the subchannels matching the bit-rates of the traffic to the subchannel capacity (as dictated by Hartley-Shannon law). The available subchannels for downstream and upstreams are of different extents (640 kbps for upstream and 9 Mbps for downstream); and, hence are qualified as asymmetric transports. Relevant to the subchannel allocation as above, the specific research, carried out can be enumerated as follows: (1) Development of a subchannel allocation metric (SAM) on the basis of information-theoretic considerations and duly accounting for noise/interference effects on the access lines and BER-based information-impairments on the trunks (feeding the access lines); (2) Use of SAM as an algorithmic support to train an artificial neural network (ANN), which is facilitated at the ADSL modem performing subchannel allocation. A new version of ANN training (and subchannel allocation prediction) strategies is developed by implementing the ANN operation in the entropy-plane. This technique allows a fast convergence of the ANN compatible for telecommunication transports. The incorporation of ANN in the modem renders the subchannel allocation smart; (3) Fuzzy considerations are also included in the ANN indicated above and operation of ADSL modem is then tuned to function as an intelligent neuro inference engine in its efforts towards subchannel allocation; (4) ATM support on ADSL lines is investigated and a scheme for allocating the permanent and switched virtual circuits (supporting ATM specified traffic) on the subchannels of access lines is developed. Relevant call-blocking probabilities are assessed; (5) Lastly, the EMI/RFI, and crosstalks on access lines are studied in the framework of practical considerations and mitigatory efforts are suggested thereof. Simulated results using data commensurate with practical aspects of ADSL transport are furnished and discussed. Background literature is comprehensively presented chapterwise and scope for future work is identified via open questions in the concluding chapter.

The research addressed and reported in this dissertation primarily refers to the scope of characterizing modern telecommunication services as complex systems. The qualifying attributes, which allow such a characterization are three-folded: (i) Size of the network supporting massive traffics; (ii) heterogeneous characteristics of the traffics constituted by a mix of data, voice and video transmissions; and (iii) quality of service (QOS) considerations as met by a variety resources. Commensurate with the scope of the research indicated above, the underlying principles of information-theoretics are adopted as the background concept of the studies performed and a complexity-metric is defined via entropy considerations. Hence, the following aspects of modern telecommunications are studied: The first one refers to using entropy as a metric to assess the traffic characteristics in ATM telecommunications. Relevant heterogeneous traffic is modeled and analyzed in terms of the complexity-metric. Impairment considerations (such as cell-losses) due to queueing and/or finite-buffer sizes are estimated via information-loss specifications. The results are compared with those of conventional queueing-theoretics based analysis. The second consideration uses the complexity-metric to implement the so-called call admission control (CAC) in ATM transmissions. The complexity-metric is considered as a decision-theoretic parameter and a fuzzy inference engine is constructed to facilitate a real-time CAC. The third contribution of this research is pertinent to the development of an artificial neural network (ANN) implemented to perform CAC using the complexity-metric as the training parameter characterizing the input calls, which compete to get admission into the network. The real-time performance of the ANN in such CAC implementations is demonstrated. The fourth effort of this research is directed to portray the cybernetic perspectives of a complex system. Again, the interacting structure of the technology and economics of telecommunication systems is considered and the associated complexity is elucidated in terms of the entropy profile of the subsystems. Hence, optimized (or suboptimal) alternative designs of a network based on technoeconomical considerations are obtained. This dissertation also includes relevant literature survey and background details. It concludes with a discussion on the results and inferences on the research carried out. Further, the scope for future study is identified and open-questions are enumerated.

The research proposed and elaborated in this dissertation is concerned with the development of new decision algorithms for hard handoff strategies in mobile communication systems. Specifically, the research tasks envisaged include the following: (1) Use of information-theoretics based statistical distance measures as a metric for hard handoff decisions; (2) A study to evaluate the log-likelihood criterion towards decision considerations to perform the hard handoff; (3) Development of a statistical model to evaluate optimum instants of measurements of the metric used for hard handoff decision. The aforesaid objectives refer to a practical scenario in which a mobile station (MS) traveling away from a serving base station (BS-I) may suffer communications impairment due to interference and shadowing affects, especially in an urban environment. As a result, it will seek to switch over to another base station (BS-II) that facilitates a stronger signal level. This is called handoff procedure. (The hard handoff refers to the specific case in which only one base station serves the mobile at the instant of handover). Classically, the handoff decision is done on the basis of the difference between received signal strengths (RSS) from BS-I and BS-II. The algorithms developed here, in contrast, stipulate the decision criterion set by the statistical divergence and/or log-likelihood ratio that exists between the received signals. The purpose of the present study is to evaluate the relative efficacy of the conventional and proposed algorithms in reference to: (i) Minimization of unnecessary handoffs ("ping-pongs"); (ii) Minimization of delay in handing over; (iii) Ease of implementation and (iv) Minimization of possible call dropouts due to ineffective handover envisaged. Simulated results with data commensurate with practical considerations are furnished and discussed. Background literature is presented in the introductory chapter and scope for future work is identified via open questions in the concluding chapter.

The state-of-the-art restructuring of power industries is changing the fundamental nature of retail electricity business. As a result, the so-called Integrated Resource Planning (IRP) strategies implemented on electric utilities are also undergoing modifications. Such modifications evolve from the imminent considerations to minimize the revenue requirements and maximize electrical system reliability vis-a-vis capacity-additions (viewed as potential investments). IRP modifications also provide service-design bases to meet the customer needs towards profitability. The purpose of this research as deliberated in this dissertation is to propose procedures for optimal IRP intended to expand generation facilities of a power system over a stretched period of time. Relevant topics addressed in this research towards IRP optimization are as follows: (1) Historical prospective and evolutionary aspects of power system production-costing models and optimization techniques; (2) A survey of major U.S. electric utilities adopting IRP under changing socioeconomic environment; (3) A new technique designated as the Segmentation Method for production-costing via IRP optimization; (4) Construction of a fuzzy relational database of a typical electric power utility system for IRP purposes; (5) A genetic algorithm based approach for IRP optimization using the fuzzy relational database.

The objectives of this research as deliberated in this dissertation are two-folded: (i) To study the nonlinear activity in the neural complex (real and artificial) and (ii) to analyze the learning processe(s) pertinent to an artificial neural network in the information-theoretic plane using cross-entropy error-metrics. The research efforts envisaged enclave the following specific tasks: (i) Obtaining a general solution for the Bernoulli-Riccati equation to represent a single parameter family of S-shaped (sigmoidal) curves depicting the nonlinear activity in the neural network. (ii) Analysis of the logistic growth of output versus input values in the neural complex (real and artificial) under the consideration that the boundaries of the sets constituting the input and output entities are crisp and/or fuzzy. (iii) Construction of a set of cross-entropy error-metrics (known as Csiszar's measures) deduced in terms of the parameters pertinent to a perceptron topology and elucidation of their relative effectiveness in training the network optimally towards convergence. (iv) Presenting the methods of symmetrizing and balancing the aforesaid error-entropy measures (in the information-theoretic plane) so as to make them usable as error-metrics in the test domain. (v) Description and analysis of the dynamics of neural learning process in the information-theoretic plane for both crisp and fuzzy attributes of input values. Relevant to these topics portraying the studies on nonlinear activity and cross-entropy considerations vis-a-vis neural networks, newer and/or exploratory inferences are made, logical conclusions are enumerated and relative discussions are presented along with the scope for future research to be pursued.

This dissertation is concerned primarily with the analytical modeling of a class of electromagnetic composite materials using the concepts of stochastical mixture theory, principles of electromagnetics and neuromimetic considerations. The global behavior of the test composite is ascertained in terms of the constitutive relations of the material parameters (having stochastical attributions) and the intramaterial hierarchy is modeled as massively interconnected, interacting units depicting such systems as mimetics of neural networks. Pertinent research efforts enclave the following specific tasks: (i) Modeling a multi-constituent electromagnetic composite medium in terms of the characteristics of its individual constituents and their spatial (random or orderly) dispositions. (ii) Assessment of nonspherical particulate effects (in terms of the stochastical attributes) on the global response of such composite materials. (iii) Evaluation of interparticle interactions and their implicit effects on the effective electromagnetic properties of the composite media. (iv) Assaying the transitional behavior of the test composites and, (v) modeling electromagnetic composites as neuromimetics correlating their effective material characteristics to the corresponding state-transitional response of a massively interconnected neural network. Results arising from these theoretical considerations are compared with data compiled via experimental studies performed (where feasible) or otherwise correlated with theoretical and/or experimental results available elsewhere in the literature. Specific experimental efforts carried out refer to piezoelectric rubber composites and their application in controlling acoustic beamforming via electrical 'pinch off' (which mimics the inhibitory response in a neuronal cell); as well as exclusive experimental tasks to verify the transitional lossy behavior model developed presently using a set of fast-ion conductor composites and dielectric-plus-conductor mixtures. Lastly, inferential conclusions are presented and discussed with an outline on the scope of extensions to the present work.