Adaptive Compression of Massive MIMO Channel State Information with Deep Learning
<|reference_start|>Adaptive Compression of Massive MIMO Channel State Information with Deep Learning: This paper proposes the use of deep autoencoders to compress the channel information in a \review{massive} multiple input and multiple output (MIMO) system. Although autoencoders perform lossy compression, they still have adequate usefulness when applied to massive MIMO system channel state information (CSI) compression. To demonstrate their impact on the CSI, we measure the performance of the system under two different channel models for different compression ratios. We disclose a few practical considerations in using autoencoders for this propose. We show through simulation that the run-time complexity of this deep autoencoder is irrelative to the compression ratio and thus an adaptive compression rate is feasible with an optimal compression ratio depending on the channel model and the signal to noise ratio.<|reference_end|>
@article{mismar2024adaptive,
    title={Adaptive Compression of Massive MIMO Channel State Information with Deep
  Learning},
    author={Faris B. Mismar and Aliye "Ozge Kaya},
    journal={arXiv preprint arXiv:2406.14668},
    year={2024},
    doi={19.1109/LNET.2024.3475269},
    archivePrefix={arXiv},
    eprint={2406.14668},
    primaryClass={cs.NI}
}