Rudolf Mathar

Bio: Rudolf Mathar is an academic researcher from RWTH Aachen University. The author has contributed to research in topics: Communication channel & Wireless sensor network. The author has an hindex of 32, co-authored 441 publications receiving 5059 citations. Previous affiliations of Rudolf Mathar include Ruhr University Bochum & Augsburg College.

Estimating position and velocity of mobiles in a cellular radio network

Abstract: Determining the position and velocity of mobiles is an important issue for hierarchical cellular networks since the efficient allocation of mobiles to large or microcells depends on its present velocity. We suggest a method of tracing a mobile by evaluating subsequent signal-strength measurements to different base stations. The required data are available in the Global System for Mobile (GSM) system. The basic idea resembles multidimensional scaling (MDS), a well-recognized method in statistical data analysis. Furthermore, the raw data are smoothed by a linear regression setup that simultaneously yields an elegant, smoothed estimator of the mobile's speed. The method is extensively tested for data generated by the simulation tool GOOSE.

Optimum positioning of base stations for cellular radio networks

Abstract: Finding optimum base station locations for a cellular radio network is considered as a mathematical optimization problem. Dependent on the channel assignment policy, the minimization of interferences or the number of blocked channels, respectively, may be more favourable. In this paper, a variety of according analytical optimization problems are introduced. Each is formalized as an integer linear program, and in most cases optimum solutions can be given. Whenever by the complexity of the problem an exact solution is out of reach, simulated annealing is used as an approximate optimization technique. The performance of the different approaches is compared by extensive numerical tests.

Location tracking of mobiles in cellular radio networks

Abstract: Some useful services in cellular radio networks and also a class of handover algorithms require knowledge of the present position and velocity of mobiles. This paper deals with a method to track mobiles by on-line monitoring of field strength data of surrounding base stations at successive time points. Such data is available in present global system for mobile communication (GSM) systems each 0.48 s and also in code-division multiple-access (CDMA) systems for transmission control. Because of strong random fluctuations of the signals, appropriate smoothing is the key point of the procedure. We develop a locally linear prediction model of successive positions as a basis for Kalman filtering. This approach turns out to be extremely successful, achieving average mislocations of 70 m in simulated test runs. Further improvement is possible by using external geographical information.

Channel assignment in cellular radio networks

Abstract: The authors investigate algorithms based on simulated annealing to solve the channel assignment problem for cellular radio networks. The blocking probability of a network is chosen as the optimization criterion. In order to check the quality of the solutions obtained by simulated annealing, they examine some special types of networks which allow an effective calculation of optimal solutions by tailored algorithms. Their investigations show that simulated annealing is a very powerful tool for solving channel assignment problems. >

I and i

Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

Machine learning

Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

What Will 5G Be

Abstract: What will 5G be? What it will not be is an incremental advance on 4G. The previous four generations of cellular technology have each been a major paradigm shift that has broken backward compatibility. Indeed, 5G will need to be a paradigm shift that includes very high carrier frequencies with massive bandwidths, extreme base station and device densities, and unprecedented numbers of antennas. However, unlike the previous four generations, it will also be highly integrative: tying any new 5G air interface and spectrum together with LTE and WiFi to provide universal high-rate coverage and a seamless user experience. To support this, the core network will also have to reach unprecedented levels of flexibility and intelligence, spectrum regulation will need to be rethought and improved, and energy and cost efficiencies will become even more critical considerations. This paper discusses all of these topics, identifying key challenges for future research and preliminary 5G standardization activities, while providing a comprehensive overview of the current literature, and in particular of the papers appearing in this special issue.