TY - GEN
T1 - Learning Beam Codebooks with Neural Networks
T2 - 21st IEEE International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2020
AU - Zhang, Yu
AU - Alrabeiah, Muhammad
AU - Alkhateeb, Ahmed
N1 - Funding Information:
This work is supported by the National Science Foundation under Grant No. 1923676.
Publisher Copyright:
© 2020 IEEE.
PY - 2020/5
Y1 - 2020/5
N2 - Scaling the number of antennas up is a key characteristic of current and future wireless communication systems. The hardware cost and power consumption, however, motivate large-scale MIMO systems, especially at millimeter wave (mmWave) bands, to rely on analog-only or hybrid analog/digital transceiver architectures. With these architectures, mmWave base stations normally use pre-defined beamforming codebooks for both initial access and data transmissions. Current beam codebooks, however, generally adopt single-lobe narrow beams and scan the entire angular space. This leads to high beam training overhead and loss in the achievable beamforming gains. In this paper, we propose a new machine learning framework for learning beamforming codebooks in hardware-constrained large-scale MIMO systems. More specifically, we develop a neural network architecture that accounts for the hardware constraints and learns beam codebooks that adapt to the surrounding environment and the user locations. Simulation results highlight the capability of the proposed solution in learning multi-lobe beams and reducing the codebook size, which leads to noticeable gains compared to classical codebook design approaches.
AB - Scaling the number of antennas up is a key characteristic of current and future wireless communication systems. The hardware cost and power consumption, however, motivate large-scale MIMO systems, especially at millimeter wave (mmWave) bands, to rely on analog-only or hybrid analog/digital transceiver architectures. With these architectures, mmWave base stations normally use pre-defined beamforming codebooks for both initial access and data transmissions. Current beam codebooks, however, generally adopt single-lobe narrow beams and scan the entire angular space. This leads to high beam training overhead and loss in the achievable beamforming gains. In this paper, we propose a new machine learning framework for learning beamforming codebooks in hardware-constrained large-scale MIMO systems. More specifically, we develop a neural network architecture that accounts for the hardware constraints and learns beam codebooks that adapt to the surrounding environment and the user locations. Simulation results highlight the capability of the proposed solution in learning multi-lobe beams and reducing the codebook size, which leads to noticeable gains compared to classical codebook design approaches.
KW - Machine learning
KW - beamforming codebooks
KW - environment awareness
KW - large-scale MIMO
KW - neural networks
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U2 - 10.1109/SPAWC48557.2020.9154320
DO - 10.1109/SPAWC48557.2020.9154320
M3 - Conference contribution
AN - SCOPUS:85090393530
T3 - IEEE Workshop on Signal Processing Advances in Wireless Communications, SPAWC
BT - 2020 IEEE 21st International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2020
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 26 May 2020 through 29 May 2020
ER -