mlsanityData to 7 Oct 2026

An Adaptive CSI Feedback Model Based on BiLSTM for Massive MIMO-OFDM Systems

arXiv 2408.06359 ↗First posted 26 Jul 2024eess.SP cs.AI cs.LGcited by 1

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Abstract (arXiv)

Deep learning (DL)-based channel state information (CSI) feedback has the potential to improve the recovery accuracy and reduce the feedback overhead in massive multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems. However, the length of input CSI and the number of feedback bits should be adjustable in different scenarios, which can not be efficiently achieved by the existing CSI feedback models. Therefore, an adaptive bidirectional long short-term memory network (ABLNet) for CSI feedback is first designed to process various input CSI lengths, where the number of feedback bits is in proportion to the CSI length. Then, to realize a more flexible feedback bit number, a feedback bit control unit (FBCU) module is proposed to control the output length of feedback bits. Based on which, a target feedback performance can be adaptively achieved by a designed bit number adjusting (BNA) algorithm. Furthermore, a novel separate training approach is devised to solve the model protection problem that the UE and gNB are from different manufacturers. Experiments demonstrate that the proposed ABLNet with FBCU can fit for different input CSI lengths and feedback bit numbers; the CSI feedback performance can be stabilized by the BNA algorithm; and the proposed separate training approach can maintain the feedback performance and reduce the complexity of feedback model.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 16 of its 48 references that are in the map. First posted on arXiv 26 Jul 2024.

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16 of 48 references.

References · 16

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4 more
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Citing papers per year: 2026: 1.

1 paper.

  1. 2026Context-Aware Markov VAE for CSI Compression in Wireless Systemscited by 0