RIS-Aided mmWave Network Planning Toward Connectivity Enhancement and Minimal Electromagnetic Field Exposure

Abstract

The utilization of millimeter wave (mmWave) technology has emerged as a key enabler for the advancement of future wireless networks. However, the widespread deployment of mmWave communication is impeded by the challenges posed by its harsh propagation characteristics. To overcome this limitation, the reconfigurable intelligent surface (RIS) is envisioned as a potential solution to enhance mmWave coverage by intelligently controlling signal reflections. In this paper, we study a RIS-aided multi-user downlink multiple-input single-output (MISO) mmWave network. We formulate the RIS-aided network planning as mixed-integer nonlinear programming (MINLP) by jointly optimizing the placement of infrastructure and link associations to maximize the connectivity while considering electromagnetic field (EMF) exposure constraints. To address this intractable problem, we transform it into mixed-integer linear programming (MILP) and then propose a power-efficient algorithm to solve it effectively. Numerical results show that substantial performance improvements are achieved by incorporating the RIS in mmWave networks. In particular, the proposed algorithm outperforms the conventional benchmark that does not employ the RIS, with up to 20% enhancement in connectivity and 14% reduction in EMF exposure.

Keywords

• Millimeter wave (mmWave)
• connectivity maximization
• electromagnetic field (EMF) exposure
• reconfigurable intelligent surface (RIS)
• network planning