An Opportunistic Source Synthesis Method for Smart Electromagnetic Environments

TL;DR

This work introduces Opportunistic Source Synthesis (OSS) within the Smart ElectroMagnetic Environment (SEME) framework to opportunistically harness urban scattering and improve RoI-specific coverage without deploying additional field-manipulating devices. By modeling the BTS array as a smart source with fixed amplitudes and programmable phases, and using the Embedded-plus-Environment Pattern (EPEP) database to predict the region-specific received power, the authors propose an optimization engine based on Particle Swarm Optimization (PSO) to minimize the mismatch to a target distribution $P_{rx}^{tar}$ over the RoI $\Omega$. The key contributions are (i) the OSS formulation that leverages Inverse Source concepts for large-scale outdoor environments and (ii) the EPEP-based acceleration that enables computationally feasible optimization. Numerical results in realistic urban scenarios demonstrate accurate EPEP predictions (average errors around 0.1–0.2 dB) and substantial improvements in RoI power statistics and minimum coverage, highlighting the practical potential of OSS for SEME-enabled outdoor networks. The work paves the way for smart BTSs that adapt to propagation conditions to meet QoS targets with reduced need for additional infrastructure, and suggests future extensions to indoor domains, alternative objective functions, and integration with other SEME technologies.

Abstract

In the framework of the "Smart ElectroMagnetic Environment" (SEME), an innovative strategy leveraging Equivalence Source concepts is introduced for enhancing the performance of large-scale outdoor wireless communication systems. The proposed Opportunistic Sources Synthesis (OSS) approach is aimed at unconventionally synthesizing the primary source (i.e., the base transceiver station (BTS) antenna array), so that the complex scattering phenomena induced in the surrounding scatterers are profitably exploited to enhance the received power within user-defined regions of interest (RoIs). To yield a computationally feasible synthesis process, an innovative "Embedded-plus-Environment Patterns" (EPEPs) method is introduced. A set of representative numerical examples, concerned with realistic large-scale outdoor scenarios, is presented to assess the effectiveness and the efficiency of the proposed optimization-driven approach for a realistic SEME implementation.