Joint BS Deployment and Power Optimization for Minimum EMF Exposure with RL in Real-World Based Urban Scenario

Conventional base station (BS) deployments typically prioritize coverage, quality of service (QoS), or cost reduction, often overlooking electromagnetic field (EMF) exposure. Whereas EMF exposure triggers significant public concern due to its potential health implications, making it crucial to address when deploying BS in densely populated areas. To this end, this paper addresses minimizing average EMF exposure while maintaining coverage in a 3D urban scenario by jointly optimizing BS deployment and power. To address this, firstly, accurate EMF prediction is essential, as traditional empirical models lack the required accuracy, necessitating a deterministic channel model. A novel least-time shoot-and-bounce ray (SBR) ray-launching (RL) algorithm is therefore developed to overcome several limitations of current simulators and is validated with real-world measurements. Secondly, to further reduce computational complexity, unlike using a fixed grid size to discretize the target area, the adaptive grid refinement (AGR) algorithm is designed with a flexible grid to predict the overall EMF exposure. Finally, based on the EMF exposure predictions, the Nelder-Mead (NM) method is used in the joint optimization, and urban user equipment (UE) distributions are incorporated to better reflect real-world conditions. When evaluating the benefits of the whole process, the results are compared against using empirical channel models, revealing notable differences and underestimation of EMF exposure that highlight the importance of considering real-world scenario.

Bridging Simulation and Measurements through Ray-Launching Analysis: A Study in a Complex Urban Scenario Environment

With the rapid increase in mobile subscribers, there is a drive towards achieving higher data rates, prompting the use of higher frequencies in future wireless communication technologies. Wave propagation channel modeling for these frequencies must be considered in conjunction with measurement results. This paper presents a ray-launching (RL)-based simulation in a complex urban scenario characterized by an undulating terrain with a high density of trees. The simulation results tend to closely match the reported measurements when more details are considered. This underscores the benefits of using the RL method, which provides detailed space-time and angle-delay results.

Arbitrary Angle of Arrival in Radar Target Simulation

Automotive radar sensors play a key role in the current development of autonomous driving. Their ability to detect objects even under adverse conditions makes them indispensable for environment-sensing tasks in autonomous vehicles. The thorough and in-place validation of radar sensors demands for an integrative test system. Radar Target Simulators (RTS) are capable of performing over-the-air validation tests by creating artificial radar echos that are perceived as targets by the radar under test (RuT). Since the authenticity and credibility of these targets is based on the accuracy with which they are generated, their simulated position must be arbitrarily adjustable. In this paper, a new approach to synthesize virtual radar targets at an arbitrary angle of arrival is presented. The concept is based on the superposition of the returning signals of two adjacent RTS channels. A theoretical model describing the basic principle and its constraints is developed. A measurement campaign is conducted that verifies the practical functionality of the proposed scheme.