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Performance Analysis of Fronthaul Compression in Massive MIMO Receiver

Roman Bychkov, Andrey Dergachev, Alexander Osinsky, Dmitry Lakontsev, Andrey Ivanov

TL;DR

A compression scheme to reduce the bitrate of the fronthaul interface that connects BBU and RRU is proposed and the knowledge of propagation channel sparsity and the condition number of the channel matrix helps to achieve higher compression ratios without performance loss.

Abstract

Future generations of cellular systems presume to use an extremely high number of antennas to enable mm waves. Increasing the number of antennas requires a growth in connections between a remote radio head (RRH) and a baseband unit (BBU). Therefore, the traffic load between RRH and BBU has to grow, and the compression of interconnection between them becomes a serious problem. In this paper, we propose a compression scheme to reduce the bitrate of the fronthaul interface that connects BBU and RRU. Then we justify compression block size and mantissa length to guarantee the required error vector magnitude (EVM). The knowledge of propagation channel sparsity and the condition number of the channel matrix helps to achieve higher compression ratios without performance loss. Simulation results with a realistic propagation channel are provided to confirm theoretical derivations.

Performance Analysis of Fronthaul Compression in Massive MIMO Receiver

TL;DR

A compression scheme to reduce the bitrate of the fronthaul interface that connects BBU and RRU is proposed and the knowledge of propagation channel sparsity and the condition number of the channel matrix helps to achieve higher compression ratios without performance loss.

Abstract

Future generations of cellular systems presume to use an extremely high number of antennas to enable mm waves. Increasing the number of antennas requires a growth in connections between a remote radio head (RRH) and a baseband unit (BBU). Therefore, the traffic load between RRH and BBU has to grow, and the compression of interconnection between them becomes a serious problem. In this paper, we propose a compression scheme to reduce the bitrate of the fronthaul interface that connects BBU and RRU. Then we justify compression block size and mantissa length to guarantee the required error vector magnitude (EVM). The knowledge of propagation channel sparsity and the condition number of the channel matrix helps to achieve higher compression ratios without performance loss. Simulation results with a realistic propagation channel are provided to confirm theoretical derivations.
Paper Structure (16 sections, 1 theorem, 17 equations, 7 figures, 3 tables)

This paper contains 16 sections, 1 theorem, 17 equations, 7 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Our contribution
  3. Compression algorithm
  4. Functional split
  5. Compression and decompression
  6. MIMO detection
  7. Error prediction - constant mantissa length
  8. MIMO detection error caused by FH compression
  9. Error growth caused by a common exponent
  10. Trainable mantissa length
  11. Surrogate optimization
  12. Compression ratio
  13. Simulation results
  14. Fixed mantissa length - error prediction
  15. Flexible mantissa length - better compression
  16. ...and 1 more sections

Key Result

Theorem 1

Let $\Omega_{M \times N} \in \mathbb{C}^{M \times N}$ be a random Gaussian matrix with independent entries. Then for any matrices $A \in \mathbb{C}^{M' \times M}$ and $B \in \mathbb{C}^{N \times N'}$ where

Figures (7)

  • Figure 1: Cloud architecture: RRUs connected to BBU.
  • Figure 2: Receiver structure with the beamspace transformation.
  • Figure 3: Block of floating point values with a common exponent.
  • Figure 4: Power distribution for a single user (antenna and beamspace domains).
  • Figure 5: Roundoff error after MIMO detector depending on the condition number (antenna domain).
  • ...and 2 more figures

Theorems & Definitions (1)

  • Theorem 1: OurCholBound