Deep Reinforcement Learning for MU-MIMO Beamforming Training in mmWave WLAN
In IEEE 802.11ay wireless local area network (WLAN), a single access point (AP) performs multi-user multiple-input-multiple-output (MU-MIMO) beamforming training (BFT) to enable simultaneous directional communications with multiple stations (STAs). During MU-MIMO BFT, the AP transmits a significant...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/11052283/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | In IEEE 802.11ay wireless local area network (WLAN), a single access point (AP) performs multi-user multiple-input-multiple-output (MU-MIMO) beamforming training (BFT) to enable simultaneous directional communications with multiple stations (STAs). During MU-MIMO BFT, the AP transmits a significant number of action frames to multiple STAs, and the performance of MU-MIMO BFT depends on how the AP configures its transmit antenna arrays for the transmission of these action frames. In this paper, we develop an algorithm that utilizes a deep reinforcement learning model to learn from the configuration of transmit antenna arrays and the BFT feedback of the STAs in previous MU-MIMO BFT processes, enabling the accurate configuration of transmit antenna arrays for the transmission of action frames in the current MU-MIMO BFT process. Through performance evaluation, our proposed deep reinforcement learning scheme demonstrated improved performance in terms of latency and failure probability of MU-MIMO BFT compared to existing studies that require estimating the signal-to-noise ratios (SNRs) measured at the STAs during the transmission of action frames. |
|---|---|
| ISSN: | 2169-3536 |