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

TL;DR

This paper introduces an adaptive CSI feedback model using BiLSTM for massive MIMO-OFDM systems, capable of adjusting to various input sizes and feedback bit requirements, enhancing flexibility and performance.

Contribution

The paper proposes a novel adaptive BiLSTM-based CSI feedback model with a feedback bit control unit and a separate training approach for cross-manufacturer compatibility.

Findings

The model effectively adapts to different CSI input lengths.

The feedback bit control unit enables flexible feedback bit numbers.

The separate training approach maintains performance across manufacturers.

Abstract

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.