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ZF Beamforming Tensor Compression for Massive MIMO Fronthaul

Libin Zheng, Zihao Wang, Minru Bai, Zhenjie Tan

TL;DR

The paper addresses fronthaul bandwidth bottlenecks for downlink ZF beamforming in massive MU-MIMO by introducing a two-stage tensor compression scheme. It combines sparse Tucker decomposition to separate a sparse core from a low-rank transform and a second-stage encoding using complex givens decomposition and run-length encoding to compress the factors and core efficiently. The authors establish an accelerated proximal block coordinate descent algorithm with convergence guarantees to solve the nonconvex STD model and demonstrate through large-scale simulations that substantial CR can be achieved with minimal rate loss, notably achieving around 11% CR at roughly 5% RL in a strong scenario. The approach enables more concurrent streams on eCPRI fronthaul, offering practical benefits for 5G/B5G deployments and scalable MU-MIMO systems, with potential extensions to adaptive parameter selection and integration with other compression techniques.

Abstract

In the rapidly evolving landscape of 5G and beyond 5G (B5G) mobile cellular communications, efficient data compression and reconstruction strategies become paramount, especially in massive multiple-input multiple-output (MIMO) systems. A critical challenge in these systems is the capacity-limited fronthaul, particularly in the context of the Ethernet-based common public radio interface (eCPRI) connecting baseband units (BBUs) and remote radio units (RRUs). This capacity limitation hinders the effective handling of increased traffic and data flows. We propose a novel two-stage compression approach to address this bottleneck. The first stage employs sparse Tucker decomposition, targeting the weight tensor's low-rank components for compression. The second stage further compresses these components using complex givens decomposition and run-length encoding, substantially improving the compression ratio. Our approach specifically targets the Zero-Forcing (ZF) beamforming weights in BBUs. By reconstructing these weights in RRUs, we significantly alleviate the burden on eCPRI traffic, enabling a higher number of concurrent streams in the radio access network (RAN). Through comprehensive evaluations, we demonstrate the superior effectiveness of our method in Channel State Information (CSI) compression, paving the way for more efficient 5G/B5G fronthaul links.

ZF Beamforming Tensor Compression for Massive MIMO Fronthaul

TL;DR

The paper addresses fronthaul bandwidth bottlenecks for downlink ZF beamforming in massive MU-MIMO by introducing a two-stage tensor compression scheme. It combines sparse Tucker decomposition to separate a sparse core from a low-rank transform and a second-stage encoding using complex givens decomposition and run-length encoding to compress the factors and core efficiently. The authors establish an accelerated proximal block coordinate descent algorithm with convergence guarantees to solve the nonconvex STD model and demonstrate through large-scale simulations that substantial CR can be achieved with minimal rate loss, notably achieving around 11% CR at roughly 5% RL in a strong scenario. The approach enables more concurrent streams on eCPRI fronthaul, offering practical benefits for 5G/B5G deployments and scalable MU-MIMO systems, with potential extensions to adaptive parameter selection and integration with other compression techniques.

Abstract

In the rapidly evolving landscape of 5G and beyond 5G (B5G) mobile cellular communications, efficient data compression and reconstruction strategies become paramount, especially in massive multiple-input multiple-output (MIMO) systems. A critical challenge in these systems is the capacity-limited fronthaul, particularly in the context of the Ethernet-based common public radio interface (eCPRI) connecting baseband units (BBUs) and remote radio units (RRUs). This capacity limitation hinders the effective handling of increased traffic and data flows. We propose a novel two-stage compression approach to address this bottleneck. The first stage employs sparse Tucker decomposition, targeting the weight tensor's low-rank components for compression. The second stage further compresses these components using complex givens decomposition and run-length encoding, substantially improving the compression ratio. Our approach specifically targets the Zero-Forcing (ZF) beamforming weights in BBUs. By reconstructing these weights in RRUs, we significantly alleviate the burden on eCPRI traffic, enabling a higher number of concurrent streams in the radio access network (RAN). Through comprehensive evaluations, we demonstrate the superior effectiveness of our method in Channel State Information (CSI) compression, paving the way for more efficient 5G/B5G fronthaul links.
Paper Structure (21 sections, 5 theorems, 67 equations, 7 figures, 4 tables, 2 algorithms)

This paper contains 21 sections, 5 theorems, 67 equations, 7 figures, 4 tables, 2 algorithms.

Table of Contents

  1. Introduction
  2. Preliminaries
  3. Problem Formulation
  4. Two Stage Compression Approach
  5. Sparse Tucker Decomposition (STD) Model
  6. Factor Compress
  7. Accelerated Block Coordinate Descent Algorithm for STD
  8. Algorithm for STD model
  9. Convergence Analysis of Algorithm \ref{['algorithm BPL1']}
  10. Numerical Results
  11. Evaluation indicators and Simulation Settings
  12. Convergence
  13. Model Validity
  14. Comparison with other methods
  15. Characteristics of compression
  16. ...and 6 more sections

Key Result

Lemma 1

Assume the sequence generated by Algorithm algorithm BPL1 which is denoted as $\{\mathcal{X}^{k}\}_{k\in\mathbb{N}}$ is bounded. There exists $\eta_{\mathcal{G}}>0, \eta_{\mathcal{S}}>0, \eta_i>0, \gamma_i>0, i=1,2,3$ and $\rho_1>0$ such that the following assertions hold.

Figures (7)

  • Figure 1: Tucker Decomposition
  • Figure 2: Compression framework
  • Figure 3: (a) Weight Matrix under DFT. (b) Core tensor under Tucker Decomposition. (c) Relative errors of the tensor produced by tucker decomposition for different tucker ranks and the original tensor, the dashed lines are contours for relative errors of 0.01, 0.02, 0.05 and 0.1, respectively.
  • Figure 4: Factor Compression Framework
  • Figure 5: Plot of relative error between compressed and original weights with iteration steps for 300 generated weight datas.
  • ...and 2 more figures

Theorems & Definitions (10)

  • Lemma 1
  • Lemma 2
  • Lemma 3
  • Theorem 1
  • Definition 1
  • Lemma 4
  • proof
  • proof
  • proof
  • proof