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Joint Port Selection Based Channel Acquisition for FDD Cell-Free Massive MIMO

Cheng Zhang, Pengguang Du, Minjie Ding, Yindi Jing, Yongming Huang

TL;DR

This work tackles the CSI acquisition challenge in FDD cell-free massive MIMO with ZF precoding by proposing a joint port selection strategy that exploits port-domain correlation and partial uplink-downlink reciprocity. An eigenvalue-decomposition-based transformation (EDT) compresses port-coefficient feedback, enabling accurate reconstruction at the transmitter; the authors derive a closed-form, approximate sum-rate expression to guide port selection and introduce two low-complexity schemes: GS-JPS and DL-JPS. The analytical results are validated against simulations, showing that GS-JPS surpasses SLNR-PS and MM-S in medium-to-high SNR scenarios, while DL-JPS achieves comparable performance with much lower online complexity, making it suitable for fast-varying channels. Additionally, EDT-based compression markedly reduces feedback overhead (e.g., ~25% with the proposed S1/S2 schemes) without substantial sum-rate loss, illustrating practical gains for scalable FDD CF-MIMO deployments.

Abstract

In frequency division duplexing (FDD) cell-free massive MIMO, the acquisition of the channel state information (CSI) is very challenging because of the large overhead required for the training and feedback of the downlink channels of multiple cooperating base stations (BSs). In this paper, for systems with partial uplink-downlink channel reciprocity, and a general spatial domain channel model with variations in the average port power and correlation among port coefficients, we propose a joint-port-selection-based CSI acquisition and feedback scheme for the downlink transmission with zero-forcing precoding. The scheme uses an eigenvalue-decomposition-based transformation to reduce the feedback overhead by exploring the port correlation. We derive the sum-rate of the system for any port selection. Based on the sum-rate result, we propose a low-complexity greedy-search-based joint port selection (GS-JPS) algorithm. Moreover, to adapt to fast time-varying scenarios, a supervised deep learning-enhanced joint port selection (DL-JPS) algorithm is proposed. Simulations verify the effectiveness of our proposed schemes and their advantage over existing port-selection channel acquisition schemes.

Joint Port Selection Based Channel Acquisition for FDD Cell-Free Massive MIMO

TL;DR

This work tackles the CSI acquisition challenge in FDD cell-free massive MIMO with ZF precoding by proposing a joint port selection strategy that exploits port-domain correlation and partial uplink-downlink reciprocity. An eigenvalue-decomposition-based transformation (EDT) compresses port-coefficient feedback, enabling accurate reconstruction at the transmitter; the authors derive a closed-form, approximate sum-rate expression to guide port selection and introduce two low-complexity schemes: GS-JPS and DL-JPS. The analytical results are validated against simulations, showing that GS-JPS surpasses SLNR-PS and MM-S in medium-to-high SNR scenarios, while DL-JPS achieves comparable performance with much lower online complexity, making it suitable for fast-varying channels. Additionally, EDT-based compression markedly reduces feedback overhead (e.g., ~25% with the proposed S1/S2 schemes) without substantial sum-rate loss, illustrating practical gains for scalable FDD CF-MIMO deployments.

Abstract

In frequency division duplexing (FDD) cell-free massive MIMO, the acquisition of the channel state information (CSI) is very challenging because of the large overhead required for the training and feedback of the downlink channels of multiple cooperating base stations (BSs). In this paper, for systems with partial uplink-downlink channel reciprocity, and a general spatial domain channel model with variations in the average port power and correlation among port coefficients, we propose a joint-port-selection-based CSI acquisition and feedback scheme for the downlink transmission with zero-forcing precoding. The scheme uses an eigenvalue-decomposition-based transformation to reduce the feedback overhead by exploring the port correlation. We derive the sum-rate of the system for any port selection. Based on the sum-rate result, we propose a low-complexity greedy-search-based joint port selection (GS-JPS) algorithm. Moreover, to adapt to fast time-varying scenarios, a supervised deep learning-enhanced joint port selection (DL-JPS) algorithm is proposed. Simulations verify the effectiveness of our proposed schemes and their advantage over existing port-selection channel acquisition schemes.
Paper Structure (16 sections, 4 theorems, 64 equations, 11 figures, 1 algorithm)

This paper contains 16 sections, 4 theorems, 64 equations, 11 figures, 1 algorithm.

Table of Contents

  1. Introduction
  2. System Model and Proposed CSI Feedback and Reconstruction Scheme
  3. Channel Model
  4. Port-Selection-Based CSI Acquisition, Feedback, and Reconstruction
  5. ZF Precoding with Recovered CSI and Transmission Model
  6. Sum-Rate Analysis
  7. Port Selection Design Schemes
  8. Greedy-Search-Type Port Selection
  9. DL-Assisted Fast Port Selection
  10. Simulation and Discussion
  11. Simulation Setup
  12. Results and Discussions
  13. Conclusion
  14. Appendix
  15. Proof of Lemma \ref{['lemma_1']}
  16. ...and 1 more sections

Key Result

Lemma 1

${\hat{\mathbf{H}}^{\rm{H}}}\hat{\mathbf{H}}$ is a diagonal matrix and its $u$-th diagonal entry is if no two users can share the same port from the same BS.

Figures (11)

  • Figure 1: Schematic illustration of the existence of common scatterers covered by different beams.
  • Figure 2: The structure of the DNN output layer.
  • Figure 3: Schematic diagram of simulation scenario.
  • Figure 4: The architecture of DL-assisted fast port selection network.
  • Figure 5: Analytical and numerical sum-rate versus the number of allocated ports of each user $P$. $L=20$, ${L}_{0}=4$, $\text{SNR} = 15\,\text{dB}$, $\rho_{\rm{s}}=0$, $\rho_{\rm{c}}=1$.
  • ...and 6 more figures

Theorems & Definitions (8)

  • Lemma 1
  • proof
  • Lemma 2
  • proof
  • Lemma 3
  • proof
  • Theorem 1
  • proof