Acquiring downlink channel state information (CSI) at the base station is vital for optimizing performance in massive
Multiple input multiple output (MIMO) Frequency-Division Duplexing (FDD) systems. While deep learning architectures have been successful in facilitating UE-side CSI feedback and gNB side recovery, the undersampling issue prior to CSI feedback is often overlooked. This issue, which arises from low-density pilot placement in current standards, results in significant aliasing effects in outdoor channels and consequently limits CSI recovery performance. To this end, this work introduces a new CSI upsampling framework at the gNB as a post-processing solution to address the gaps caused by undersampling. Leveraging the physical principles of discrete Fourier transform shifting theorem and multipath reciprocity, our framework effectively uses uplink CSI to mitigate aliasing effects. We further develop a learning based
method that integrates the proposed algorithm with the Iterative Shrinkage-Thresholding Algorithm Net (ISTA-Net) architecture, enhancing our approach for non-uniform sampling recovery. Our numerical results show that both our rule-based and deep learning methods significantly outperform traditional interpolation techniques and current state-of-the-art approaches in terms of performance.
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Downlink Precoding for FBMC-based Massive MIMO with Imperfect Channel Reciprocity
In this paper, a practical precoding method for the downlink of filter bank multicarrier-based (FBMC-based) massive multiple-input multiple-output (MIMO) is developed. The proposed method includes a two-stage precoder consisting of a fractionally spaced prefilter (FSP) per subcarrier for flattening/equalizing the channel across the subcarrier band, followed by a conventional precoder whose goal is to concentrate the signals of different users at their spatial locations. This way, each user receives only the intended information. In this paper, we take note that channel reciprocity may not hold perfectly in practical scenarios due to the mismatch of radio chains in uplink and downlink. Additionally, channel state information (CSI) at the base station may not be perfectly known. This, together with imperfect channel reciprocity can lead to detrimental effects on the downlink precoder performance. We theoretically analyze the performance of the proposed precoder in the presence of imperfect CSI and channel reciprocity calibration errors. This leads to an effective method for compensating these effects. Finally, we numerically evaluate the performance of the proposed precoder. Our results show that the proposed precoder leads to an excellent performance when benchmarked against OFDM.
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- Award ID(s):
- 1824558
- NSF-PAR ID:
- 10388579
- Date Published:
- Journal Name:
- ICC 2022 - IEEE International Conference on Communications
- Page Range / eLocation ID:
- 1324 to 1329
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/ICC45855.2022.9839271
