公开(公告)号：US20250007579A1

公开(公告)日：2025-01-02

申请号：US18578896

申请日：2022-07-12

Applicant: ARIZONA BOARD OF REGENTS ON BEHALF OF ARIZONA STATE UNIVERSITY

Inventor： Ahmed ALKHATEEB ,  Yu ZHANG ,  Muhammad ALRABEIAH

IPC: H04B7/06

Abstract: Reinforcement learning of beam codebooks for millimeter wave and terahertz multiple-input-multiple-output (MIMO) systems is provided. Millimeter wave (mmWave) and terahertz (THz) MIMO systems rely on predefined beamforming codebooks for both initial access and data transmission. These predefined codebooks, however, are commonly not optimized for specific environments, user distributions, and/or possible hardware impairments. To overcome these limitations, this disclosure develops a deep reinforcement learning framework that learns how to optimize the codebook beam patterns relying only on receive power measurements. The developed model learns how to adapt the beam patterns based on the surrounding environment, user distribution, hardware impairments, and array geometry. Further, this approach does not require any knowledge about the channel, radio frequency (RF) hardware, or user positions.

公开(公告)号：US20250125852A1

公开(公告)日：2025-04-17

申请号：US18912922

申请日：2024-10-11

Applicant: ARIZONA BOARD OF REGENTS ON BEHALF OF ARIZONA STATE UNIVERSITY

Inventor： Ahmed ALKHATEEB ,  Yu ZHANG

IPC: H04B7/06 ,  H04W24/02

Abstract: A system and method for training the machine learning models of the multiple-input multiple-output (MIMO) communication systems. The system and method cluster the training data based on the device position. Then, separate models are trained based on the data for each position. The system and method can be applied to the design of machine learning-based beamforming, precoding, channel compression, channel estimation, and codebook design among other applications. The system uses the implicit or explicit user position information to select the right zone-specific model and its parameters.