Intelligent Reflecting Surface Configuration Using Adaptive Quantization and Neural Prior

Abstract

Intelligent Reflective Surface (IRS) is a promising technology for improving the data transmission rate in hard direct channel conditions. In this paper, we describe our solution to estimate the relevant channels and configure the IRS for efficient wireless communications, as part of the 2021 IEEE Signal Processing Cup (SP Cup) competition. First, we estimate the wireless channel and then find an IRS configuration that maximizes the rate of that channel. We begin with the provided far-from-optimal IRS configurations and apply an iterative optimization technique based on gradient descent and adaptive quantization. Further optimization is obtained by training a deep generative neural network to find a configuration that maximizes the rate function. Compared to the best provided configurations that provide a weighted average rate of 104.07 Mbit/s, the best configurations we discovered provide a significantly higher average rate of 120.70 Mbit/s. Non-IRS based solution provides an average rate of 4.38 Mbit/s.