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Robust Hybrid Precoding for Millimeter Wave MU-MISO System Via Meta-Learning

Yifan Guo

TL;DR

Simulation results demonstrate that GGML can significantly enhance spectral efficiency, and speed up the convergence by 8 times faster compared to traditional approaches, and could even outperform fully digital weighted minimum mean square error (WMMSE) precoding with the same number of antennas.

Abstract

Thanks to the low cost and power consumption, hybrid analog-digital architectures are considered as a promising energy-efficient solution for massive multiple-input multiple-output (MIMO) systems. The key idea is to connect one RF chain to multiple antennas through low-cost phase shifters. However, due to the non-convex objective function and constraints, we propose a gradient-guided meta-learning (GGML) based alternating optimization framework to solve this challenging problem. The GGML based hybrid precoding framework is \textit{free-of-training} and \textit{plug-and-play}. Specifically, GGML feeds the raw gradient information into a neural network, leveraging gradient descent to alternately optimize sub-problems from a local perspective, while a lightweight neural network embedded within the meta-learning framework is updated from a global perspective. We also extend the proposed framework to include precoding with imperfect channel state information. Simulation results demonstrate that GGML can significantly enhance spectral efficiency, and speed up the convergence by 8 times faster compared to traditional approaches. Moreover, GGML could even outperform fully digital weighted minimum mean square error (WMMSE) precoding with the same number of antennas.

Robust Hybrid Precoding for Millimeter Wave MU-MISO System Via Meta-Learning

TL;DR

Simulation results demonstrate that GGML can significantly enhance spectral efficiency, and speed up the convergence by 8 times faster compared to traditional approaches, and could even outperform fully digital weighted minimum mean square error (WMMSE) precoding with the same number of antennas.

Abstract

Thanks to the low cost and power consumption, hybrid analog-digital architectures are considered as a promising energy-efficient solution for massive multiple-input multiple-output (MIMO) systems. The key idea is to connect one RF chain to multiple antennas through low-cost phase shifters. However, due to the non-convex objective function and constraints, we propose a gradient-guided meta-learning (GGML) based alternating optimization framework to solve this challenging problem. The GGML based hybrid precoding framework is \textit{free-of-training} and \textit{plug-and-play}. Specifically, GGML feeds the raw gradient information into a neural network, leveraging gradient descent to alternately optimize sub-problems from a local perspective, while a lightweight neural network embedded within the meta-learning framework is updated from a global perspective. We also extend the proposed framework to include precoding with imperfect channel state information. Simulation results demonstrate that GGML can significantly enhance spectral efficiency, and speed up the convergence by 8 times faster compared to traditional approaches. Moreover, GGML could even outperform fully digital weighted minimum mean square error (WMMSE) precoding with the same number of antennas.

Paper Structure

This paper contains 27 sections, 2 theorems, 32 equations, 8 figures, 1 table, 2 algorithms.

Table of Contents

  1. Introduction
  2. Related Works
  3. Contributions and Organization
  4. Notations
  5. System Model And Problem Formulation
  6. System Model
  7. Problem Formulation
  8. Perfect CSI
  9. Imperfect CSI
  10. Meta Learning Based Precoding Design
  11. KAN Architecture
  12. GGML Overall Framework
  13. Gradient Based Optimization
  14. Local Perspective Based Optimization
  15. Global Perspective Based Optimization
  16. ...and 12 more sections

Key Result

Lemma 1

For matrices $\mathbf{X}\in\mathbb{C}^{n\times m}$ and $\mathbf{Y}\in\mathbb{C}^{n\times m}$, the following inequality holds:

Figures (8)

  • Figure 1: The architecture of hybrid precoding.
  • Figure 2: A block diagram of the proposed meta-learning algorithm for hybrid precoding.
  • Figure 3: Schematic diagram of the proposed GGML-ImCSI for robust downlink hybrid precoding.
  • Figure 4: Average spectral efficiency versus the iterations.
  • Figure 5: Average CPU time versus the BS antenna.
  • ...and 3 more figures

Theorems & Definitions (3)

  • Lemma 1: adapted from Lemma 7.1 in palomar2003unified
  • Proposition 2
  • proof