Although the first array antennas were developed long ago (with practical results reported in the first decades of the previous century), it took considerable time and effort before the electronically phased array antennas reached the marketplace. Their full potential could initially not be exploited without the availability of advanced integrated circuits and computer technology. The developments of electromagnetic analysis methods and EM simulation tools have of course also played a significant role. A phased array is still costly, and it has to be proven that the extra cost is balanced by needed improvements in system functionality and performance. The modern trend is towards digital processing of the received array element signals, i.e. exploiting the spatial dimension in addition to the traditional time/frequency (range & doppler, coding...) dimensions. In this way the desired information can be extracted more efficiently and at the same time provide interference rejection, improved signal quality, higher resolution etc. A corresponding scheme is not possible on the transmit side, but the spatial dimension can also here give benefits. However, challenges remain in the electromagnetic design of array antennas, in particular broadband arrays (one to several octaves) with small array depths, and conformal arrays. Both areas cause complications to be mastered in the overall system design, e.g. the frequency dependence of components, signal dispersion, mutual coupling etc. Further developments are needed in modelling and optimization of array structures together with other subsystems: feed networks, receivers, transmitters, algorithms, coding schemes and so on. Thus, the modern antenna array is typically integrated with other subsystems, forming an antenna system where the critical parts are optimized together. Success in this important area is relying on the antenna array designer to work closely together with other expertise in order to reach the required optimum design goals and remain competitive in business. The development of new system simulation tools and subsystem interface descriptions will be necessary to facilitate the interdisciplinary efforts needed.

This paper presents selected highlights of the history, development, and application of phased array antennas. In the present days of the information age, with mature MMIC technology, available massive computer resources, and an increasing need for information collection, treatment, dissemination, and protection, phased array systems technology will be central in the development of the wireless systems of tomorrow.

This paper evaluates the gain that can be achieved by Multiple Input/Multiple Output (MIMO), hereafter referred to as the Relative MIMO Gain (RMG), with respect to the distance between the transmitter and the receiver, depending on the cell type.

The RMG is obtained from simulation results and since each run of the simulator evaluates one possible value for the RMG, multiple runs are needed to obtain a significant mean value. Although the mean value is a meaningful statistic, this paper addresses the distribution of the RMG expressed by its Cumulative Distribution Function (CDF). This CDF can then be used in other simulators to evaluate the effects of MIMO on the system, as has been done in a UMTS network simulator.

The paper is divided into the following sections: IST/TUL Channel Simulator, Scenarios, Relative MIMO Gain, Simulation Results and Conclusions.

In the IST/TUL Channel Simulator section the channel model implemented in the simulator is described, which is the Geometrically Based Single Bounce Channel Model (GBSBCM) developed at IST/TUL. This model defines an environment, where clusters of scatterers reflect the transmitted signal from the transmitter to the receiver. These scatterers add attenuation and an additional phase shift to the signal. Due to the different locations of these scatterers, multiple path components are received at the receiver, resulting in a Channel Impulse Response (CIR) of the channel. The simulator has been used to simulate the radio channel, from which the RMG and its statistics are obtained.

For the simulations, three different scenarios were used, which are described in the Scenarios section. Scenarios have been defined for the pico-, micro- and macro-cells, which is the Railway-Station-Scenario, City-Street-Scenario and Highway-Scenario, respectively. The pico- and macro-cell both use a circular region, where for the pico cell both transmitter and receiver are located inside the region. For the macro-cell only the transmitter or receiver are located inside the circular region. The micro-cell scenarios are simulated with an elliptical region, where both transmitter and receiver are located on the line connecting the foci of the ellipse.

The section entitled Relative MIMO Gain defines the upper and lower theoretical MIMO Gain, as well as the actual RMG. The capacity of a MIMO system is largely dependent on the correlation between the CIRs of the different antenna pairs. The upper bound is obtained when the CIRs between different antenna pairs are uncorrelated, while the lower bound is obtained when the CIRs of the antenna pairs are completely correlated.

This correlation of the CIRs between the antenna pairs is dependant on the simulated environment, and also on the distance between the transmitter and the receiver. From multiple simulation runs with the same scenario an average value and the CDF of the RMG can be obtained. By controlling the distance between the transmitter and receiver the relation between the RMG statistics and the distance can be evaluated.

The conclusions of this work will be drawn in the Conclusions section.