Toward a Circuit Theory of Communication

Electromagnetic field theory provides the physics of radio communications, while information theory approaches the problem from a purely mathematical point of view. While there is a law of conservation of energy in physics, there is no such law in information theory. Consequently, when, in information theory, reference is made (as it frequently is) to terms like energy, power, noise, or antennas, it is by no means guaranteed that their use is consistent with the physics of the communication system. Circuit theoretic multiport concepts can help in bridging the gap between the physics of electromagnetic fields and the mathematical world of information theory, so that important terms like energy or antenna are indeed used consistently through all layers of abstraction. In this paper, we develop circuit theoretic multiport models for radio communication systems. To demonstrate the utility of the circuit theoretic approach, an in-depth analysis is provided on the impact of impedance matching, antenna mutual coupling, and different sources of noise on the performance of the communication system. Interesting insights are developed about the role of impedance matching and the noise properties of the receive amplifiers, as well as the way array gain and channel capacity scale with the number of antennas in different circumstances. One particularly interesting result is that, with arrays of lossless antennas that receive isotropic background noise, efficient multistreaming can be achieved no matter how densely the antennas are packed.

I. Introduction

The analysis and optimization of communication systems involves a host of technical and scientific disciplines. In the case of radio communications, these include electromagnetic field theory, radio-frequency engineering, and signal, coding, and information theories. The first two disciplines form part of the physical theory of communications, for the laws of nature, like the Maxwell equations or the major conservation laws, play a central role in their concepts and methods. In contrast, signal, coding, and information theories are essentially mathematical theories. As such, they are not based on the laws of nature but rather on definitions and mathematical logic. Only in conjunction with the physical disciplines can one attempt a complete theory where predictions can be put to the test by experiment. To this end, it is crucial that the mathematical and physical layers of abstraction are consistent with each other.