Wireless Communications

The demand for wireless mobile communications services is growing at an explosive rate, with the anticipation that communication to a mobile device anywhere on the globe at all times will be available in the near future. An array of antennas mounted on vehicles, ships, aircraft, satellites, and base stations is expected to play an important role in fulfilling the increased demand of channel requirement for these services, as well as for the realization of the dream that a portable communications device the size of a wristwatch be available at an affordable cost for such services. This paper is the first of a two-part study. It provides a comprehensive treatment, at a level appropriate to nonspecialists, of the use of an antenna array to enhance the efficiency of mobile communications systems. It presents an overview of mobile communications as well as details of how an array may be used in various mobile communications systems, including land-mobile, indoor-radio, and satellite-based systems. It discusses advantages of an array of antennas in a mobile communications system, highlights improvements that are possible by using multiple antennas compared to a single antenna in a system, and provides details on the feasibility of antenna arrays for mobile communications applications.

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Applications of antenna arrays to mobile communications. I. Performance improvement, feasibility, and system considerations

Wireless communication is used in many different situations such as mobile telephony, radio and TV broadcasting, satellite communication, wireless LANs, and military operations. In each of these situations a frequency assignment problem arises with application specific characteristics. Researchers have developed different modeling ideas for each of the features of the problem, such as the handling of interference among radio signals, the availability of frequencies, and the optimization criterion.

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Models and solution techniques for frequency assignment problems

It has been over a decade since neural networks were first applied to solve combinatorial optimization problems. During this period, enthusiasm has been erratic as new approaches are developed and (sometimes years later) their limitations are realized. This article briefly summarizes the work that has been done and presents the current standing of neural networks for combinatorial optimization by considering each of the major classes of combinatorial optimization problems. Areas which have not yet been studied are identified for future research.

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Neural Networks for Combinatorial Optimization: a Review of More Than a Decade of Research

The channel assignment problem, i.e. the task of assigning the channels to the radio base stations in a spectrum-efficient way, is an NP-complete optimization problem occurring during design of cellular radio systems. Previously, this problem has been solved by graph coloring algorithms. An alternative approach is presented. The problem is solved using simulated annealing, which is a general approach to combinatorial optimization. The algorithm has been successfully applied to practical radio network planning situations. One major benefit of the approach consists in the enhanced flexibility it gives to the engineer. >

Channel assignment for cellular radio using simulated annealing

The channel assignment problem, i.e. the task of assigning channels to radio cells in a spectrum-efficient way, is solved by a neural network algorithm. This algorithm is inherently parallel and does not rely on a particular structure of the interference graph. The results obtained so far indicate that the algorithm can be used to obtain an optimum solution. It was applied successfully for inhomogeneous interference conditions and channel demand. Cochannel and cosite constraints were taken into account, and the extension to any other technical restrictions will be possible in an obvious way. The examples studied to date are of a relatively small size; the question remains of how the algorithm behaves when applied to larger and more complex examples. The disadvantages of the algorithm are its long calculation time compared to graph coloring algorithms and the difficulty of finding appropriate parameters. However, the algorithm was not optimized for speed, and the parameter search may be a question of experience. >

D. Kunz

Papers

Channel assignment for cellular radio using neural networks