Year: 2024
Type: article
Abstract: Beam codebooks are integral components of future millimeter wave MIMO systems. Therefore, it is critical to optimize these codebooks for efficient and reliable communications. Prior work has focused on single-cell codebook learning problems and under stationary interference. In this work, we generalize the interference-aware codebook learning problem to networks with multiple cells/basestations. One of the key differences is that the underlying environment becomes non-stationary, as the behavior of one basestation may influence the learning of the others. Further, we avoid information exchange between different learning nodes which leads to a fully decentralized system with increased learning difficulties. To tackle the non-stationarity, the averaging of measurements is used to estimate the interference nulling performance of a particular beam, based on which a decision rule is provided. Furthermore, we theoretically justify the adoption of such estimator, and prove that it is a sufficient statistic for the underlying quantity of interest in an asymptotic sense. Finally, a novel reward function is proposed to decouple the learning of the multiple agents running at different nodes. Results show that the developed solution is capable of learning well-shaped codebook patterns for different networks and significantly suppress the interference without requiring any information exchange between basestations.
Authors: Yu Zhang, Ahmed Alkhateeb
Institution: Arizona State University
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
Funder: National Science Foundation
OpenAlex ID: W4403827282