Bayesian Optimization Framework for Channel Simulation-Based Base Station Placement and Transmission Power Design

TL;DR

An adaptive experimental design framework for a channel-simulation-based base station (BS) design that supports the joint optimization of transmission power and placement and a solver for the problem based on Bayesian optimization (BO), a black-box optimization method is developed.

Abstract

This study proposes an adaptive experimental design framework for a channel-simulation-based base station (BS) design that supports the joint optimization of transmission power and placement. We consider a system in which multiple transmitters provide wireless services over a shared frequency band. Our objective is to maximize the average throughput within an area of interest. System operators can design the system configurations prior to deployment by iterating them through channel simulations and updating the parameters. However, accurate channel simulations are computationally expensive; therefore, it is preferable to configure the system using a limited number of simulation iterations. We develop a solver for the problem based on Bayesian optimization (BO), a black-box optimization method. The numerical results demonstrate that our proposed framework can achieve 18-22% higher throughput performance than conventional placement and power optimization strategies.

Figures (4)

• Figure 1: System model.
• Figure 2: Workflows in the pure BO and proposed framework.
• Figure 3: Effects of the number of iterations.
• Figure 4: Effects of channel simulation error at $T=50$.