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CSI Sensing from Heterogeneous User Feedbacks: A Constrained Phase Retrieval Approach

Lei Li, Xing Zeng, Ya-Feng Liu, Yanqing Xu, Tsung-Hui Chang

TL;DR

A novel CSI sensing scheme that can significantly reduce the feedback overhead by exploiting the spatial consistency of wireless channels among nearby UEs, and a constrained PR (CPR) formulation that characterizes the feasible region of CSI by the PMI information is proposed.

Abstract

This paper investigates the downlink channel state information (CSI) sensing in 5G heterogeneous networks composed of user equipments (UEs) with different feedback capabilities. We aim to enhance the CSI accuracy of UEs only affording the low-resolution Type-I codebook. While existing works have demonstrated that the task can be accomplished by solving a phase retrieval (PR) formulation based on the feedback of precoding matrix indicator (PMI) and channel quality indicator (CQI), they need many feedback rounds. In this paper, we propose a novel CSI sensing scheme that can significantly reduce the feedback overhead. Our scheme involves a novel parameter dimension reduction design by exploiting the spatial consistency of wireless channels among nearby UEs, and a constrained PR (CPR) formulation that characterizes the feasible region of CSI by the PMI information. To address the computational challenge due to the non-convexity and the large number of constraints of CPR, we develop a two-stage algorithm that firstly identifies and removes inactive constraints, followed by a fast first-order algorithm. The study is further extended to multi-carrier systems. Extensive tests over DeepMIMO and QuaDriGa datasets showcase that our designs greatly outperform existing methods and achieve the high-resolution Type-II codebook performance with a few rounds of feedback.

CSI Sensing from Heterogeneous User Feedbacks: A Constrained Phase Retrieval Approach

TL;DR

A novel CSI sensing scheme that can significantly reduce the feedback overhead by exploiting the spatial consistency of wireless channels among nearby UEs, and a constrained PR (CPR) formulation that characterizes the feasible region of CSI by the PMI information is proposed.

Abstract

This paper investigates the downlink channel state information (CSI) sensing in 5G heterogeneous networks composed of user equipments (UEs) with different feedback capabilities. We aim to enhance the CSI accuracy of UEs only affording the low-resolution Type-I codebook. While existing works have demonstrated that the task can be accomplished by solving a phase retrieval (PR) formulation based on the feedback of precoding matrix indicator (PMI) and channel quality indicator (CQI), they need many feedback rounds. In this paper, we propose a novel CSI sensing scheme that can significantly reduce the feedback overhead. Our scheme involves a novel parameter dimension reduction design by exploiting the spatial consistency of wireless channels among nearby UEs, and a constrained PR (CPR) formulation that characterizes the feasible region of CSI by the PMI information. To address the computational challenge due to the non-convexity and the large number of constraints of CPR, we develop a two-stage algorithm that firstly identifies and removes inactive constraints, followed by a fast first-order algorithm. The study is further extended to multi-carrier systems. Extensive tests over DeepMIMO and QuaDriGa datasets showcase that our designs greatly outperform existing methods and achieve the high-resolution Type-II codebook performance with a few rounds of feedback.
Paper Structure (23 sections, 58 equations, 12 figures, 2 tables, 3 algorithms)

This paper contains 23 sections, 58 equations, 12 figures, 2 tables, 3 algorithms.

Table of Contents

  1. Introduction
  2. Related Works
  3. Contributions
  4. System Model and Problem Formulation
  5. Proposed CSI Sensing Scheme
  6. Dimension Reduction
  7. Basis Construction by Spatial Consistency
  8. Hybrid Precoding Design
  9. CPR Formulation
  10. Efficient Algorithm Designs
  11. PD-EVD Algorithm
  12. Two-Stage MECS-SGDA Algorithm
  13. Extension to Multi-carrier Systems
  14. Simulation Results
  15. Performance on DeepMIMO Dataset
  16. ...and 8 more sections

Figures (12)

  • Figure 1: Illustration of a wireless network consisting of UEs with different CSI feedback capabilities.
  • Figure 2: CSI sensing in a heterogeneous network, where the TU can only afford the low-resolution CSI feedback via the PMIs of Type-I codewords and CQIs, and nearby RUs return high-resolution CSI via the PMIs of Type-II codewords.
  • Figure 3: The averaged performance comparison of 'Prob. (\ref{['eq:PR']}), GS' and 'Prob. (\ref{['eq:PR_g']}), GS'. 'T1' and 'T2' stand for the performance of the Type-I and Type-II codewords, respectively.
  • Figure 4: The averaged performance comparison of the conventional random precoding and that of the proposed hybrid precoding.
  • Figure 5: The performance of the hybrid precoding scheme under different dimensions of ${\bm g}$.
  • ...and 7 more figures

Theorems & Definitions (1)

  • Remark 1