Moses Ekpenyong

Bio: Moses Ekpenyong is an academic researcher from University of Uyo. The author has contributed to research in topics: Speech synthesis & Wireless network. The author has an hindex of 6, co-authored 77 publications receiving 186 citations. Previous affiliations of Moses Ekpenyong include University of Edinburgh.

On Propagation Path Loss Models For 3-G Based Wireless Networks: A Comparative Analysis

Abstract: This paper studies comparatively, the most commonly used path loss models among others, for UMTS based cellular systems, with the goal of reporting through computer simulation, the most reliable one, suitable for efficient coverage planning. We experiment these path loss models using empirical data for macro-cellular (urban) environments. We observe that the Lee path loss model has an improved coverage performance compared to the COST-231 and ECC-33 path loss models respectively. The simulator could generically be adapted for other propagation environments. Third Generation (3G) wireless networks are based on the Universal Mobile Telecommunication System (UMTS) technology and are currently being installed in many countries with the aim of improving upon past technologies and fulfilling users' requirements. The current deployment of UMTS networks is not in many cases ubiquitous and is only concentrated in the congested urban business areas. They are used to provide either the special higher rate data services or increased capacity for handling the voice traffic in specific locations and are therefore complementary and supplemental to the GSM networks. The GSM networks are anticipated to stay around and even continue to grow and expand for at least the next half decade given the huge investments already made by the operators in the GSM infrastructure networks and their fine capability to handle voice, though not with the same spectral efficiency as the Wireless Code Division Multiple Access (WCDMA). This means that the island deployment of UMTS networks will be the trend for some time to come, and hence the requirement for the seamless roaming, handover, and inter-operation with the existing GSM networks to provide service coverage continuity and load sharing. Therefore, the elaborate interoperability and coordination mechanisms and features provided by the equipment need to be exploited by network planners to effectively result in the pooling of the resources, and hence produce the most efficient utilization of the limited expensive radio spectrums. The high throughput and capacity demand of the services anticipated for the 3G networks and the interference-limiting environment of the UMTS based systems require highly skilled radio planning practices and the use of spectral efficiency measures. Signal propagation models are used extensively in network planning, particularly for conducting feasibility studies and performing initial system development (1). The planning of cellular networks requires an understanding of basic concepts concerning the use of Radio signals. The path traveled by the signal from one point to another through or along a medium is called propagation (2). In cellular networks, a signal is propagated to and from a base station. When a signal is transmitted through space, it gets weaker with the distance traveled, resulting in the received power being significantly less than the original transmitted power. This phenomenon is referred to as propagation loss. The propagation path between the transmitter and the receiver may vary from simple line-of-sight (LOS) to very complex one due to diffraction, reflecting and scattering, (3). To estimate the performance of wireless channels, propagation models (4) are often used. The radio wave propagation or path loss models, the properties of the base station and the properties of the mobile station are required to calculate the radio coverage for a chosen base station. Path loss models represent a set of mathematical equations and algorithms which are applied for radio signal

Pattern Recognition and Machine Learning

Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

On the capacity of a cellular CDMA system

Abstract: It is shown that, particularly for terrestrial cellular telephony, the interference-suppression feature of CDMA (code division multiple access) can result in a many-fold increase in capacity over analog and even over competing digital techniques. A single-cell system, such as a hubbed satellite network, is addressed, and the basic expression for capacity is developed. The corresponding expressions for a multiple-cell system are derived. and the distribution on the number of users supportable per cell is determined. It is concluded that properly augmented and power-controlled multiple-cell CDMA promises a quantum increase in current cellular capacity. >

The Self-Organizing Map

Abstract: An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article.

Knowledge in Action: Logical Foundations for Specifying and Implementing Dynamical Systems

Abstract: When I started out as a newly hatched PhD student, back in the day, one of the first articles I read and understood (or at least thought that I understood) was Ray Reiter’s classic article on default logic (Reiter, 1980).This was some years after the famous ‘non-monotonic logic’ issue of Artificial Intelligence in which that article appeared, but default logic was still one of the leading approaches, a tribute to the simplicity and power of the theory. As a result of reading the article, I became fascinated by both default logic and, more generally, non-monotonic logics. However, despite my fascination, these approaches never seemed terribly useful for the kinds of problem that I was supposed to be studying—problems like those in medical decision making—and so I eventually lost interest. In fact non-monotonic logics seemed to me, and to many people at the time I think, not to be terribly useful for anything. They were interesting, and clearly relevant to the long-term goals of Artificial Intelligence as a discipline, but not of any immediate practical importance. This verdict, delivered at the end of the 1980s, continued, I think, to be true for the next few years while researchers working in non-monotonic logics studied problems that to outsiders seemed to be ever more obscure. However, by the end of the 1990s, it was becoming clear, even to folk as short-sighted as I, that non-monotonic logics were getting to the point at which they could be used to solve practical problems. Knowledge in action shows quite how far these techniques have come. The reason that non-monotonic logics were invented was, of course, in order to use logic to reason about the world. Our knowledge of the world is typically incomplete, and so, in order to reason about it, one has to make assumptions about things one does not know. This, in turn, requires mechanisms for both making assumptions and then retracting them if and when they turn out not to be true. Non-monotonic logics are intended to handle this kind of assumption making and retracting, providing a mechanism that has the clean semantics of logic, but which has a non-monotonic set of conclusions. Much of the early work on non-monotonic logics was concerned with theoretical reasoning, that is reasoning about the beliefs of an agent—what the agent believes to be true. Theoretical reasoning is the domain of all those famous examples like ‘Typically birds fly. Tweety is a bird, so does Tweety fly?’, and the fact that so much of non-monotonic reasoning seemed to focus on theoretical reasoning was why I lost interest in it. I became much more concerned with practical reasoning—that is reasoning about what an agent should do—and non-monotonic reasoning seemed to me to have nothing interesting to say about practical reasoning. Of course I was wrong. When one tries to formulate any kind of description of the world as the basis for planning, one immediately runs into applications of non-monotonic logics, for example in keeping track of the state of a changing world. It is this use of non-monotonic logic that is at the heart of Knowledge in action. Building on the McCarthy’s situation calculus, Knowledge in action constructs a theory of action that encompasses a very large part of what an agent requires to reason about the world. As Reiter says in the final chapter,