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Joint Uplink-Downlink Fronthaul Bit Allocation in Fronthaul-Limited Massive MU-MIMO Systems

Yasaman Khorsandmanesh, Emil Bjornson, Joakim Jalden

TL;DR

This paper tackles fronthaul capacity constraints in AAS-based massive MU-MIMO systems by optimally splitting fronthaul bits between uplink CSI quantization and downlink precoder quantization to maximize the sum spectral efficiency. It adopts the Additive Quantization Noise Model (AQNM) to model fronthaul distortions and derives a hardening-bound SE expression, including a closed-form SINR for Maximum Ratio Transmission (MRT) that reveals how CSI and precoding quantization distortions interact. An exact line-search algorithm is proposed to solve the 1D bit-split optimization, and numerical results show SNR-dependent strategies: at low SNR, allocate more bits to CSI, whereas at higher SNR a balanced CSI-precoder split yields near-optimal performance. The findings provide practical guidance for designing fronthaul-limited centralized RAN deployments, enabling accurate, low-complexity optimization of bit allocation to maximize sum SE.

Abstract

This paper optimizes the fronthaul bit allocation in massive multi-user multiple-input multiple-output (MU-MIMO) systems operating with limited-capacity fronthaul links. We consider an advanced antenna system (AAS) controlled by a centralized baseband unit (BBU). In the AAS, multiple antenna elements together with their radio units are integrated into a single unit. In this setup, a key challenge is allocating fronthaul bits between uplink channel state information (CSI) quantization and downlink precoding matrix quantization. We formulate the problem of maximizing the sum spectral efficiency (SE) for a given fronthaul capacity. We develop an SE expression for this scenario based on the hardening bound. We compute the expression in closed form for maximum ratio transmission, which reveals the relative impact of the two types of quantization distortion. We then formulate a bit split optimization problem and propose an algorithm that exactly solves it. Numerical results demonstrate how the relative importance of assigning bits to CSI and precoding varies depending on the signal-to-noise ratio.

Joint Uplink-Downlink Fronthaul Bit Allocation in Fronthaul-Limited Massive MU-MIMO Systems

TL;DR

This paper tackles fronthaul capacity constraints in AAS-based massive MU-MIMO systems by optimally splitting fronthaul bits between uplink CSI quantization and downlink precoder quantization to maximize the sum spectral efficiency. It adopts the Additive Quantization Noise Model (AQNM) to model fronthaul distortions and derives a hardening-bound SE expression, including a closed-form SINR for Maximum Ratio Transmission (MRT) that reveals how CSI and precoding quantization distortions interact. An exact line-search algorithm is proposed to solve the 1D bit-split optimization, and numerical results show SNR-dependent strategies: at low SNR, allocate more bits to CSI, whereas at higher SNR a balanced CSI-precoder split yields near-optimal performance. The findings provide practical guidance for designing fronthaul-limited centralized RAN deployments, enabling accurate, low-complexity optimization of bit allocation to maximize sum SE.

Abstract

This paper optimizes the fronthaul bit allocation in massive multi-user multiple-input multiple-output (MU-MIMO) systems operating with limited-capacity fronthaul links. We consider an advanced antenna system (AAS) controlled by a centralized baseband unit (BBU). In the AAS, multiple antenna elements together with their radio units are integrated into a single unit. In this setup, a key challenge is allocating fronthaul bits between uplink channel state information (CSI) quantization and downlink precoding matrix quantization. We formulate the problem of maximizing the sum spectral efficiency (SE) for a given fronthaul capacity. We develop an SE expression for this scenario based on the hardening bound. We compute the expression in closed form for maximum ratio transmission, which reveals the relative impact of the two types of quantization distortion. We then formulate a bit split optimization problem and propose an algorithm that exactly solves it. Numerical results demonstrate how the relative importance of assigning bits to CSI and precoding varies depending on the signal-to-noise ratio.
Paper Structure (12 sections, 24 equations, 4 figures, 1 table, 1 algorithm)

This paper contains 12 sections, 24 equations, 4 figures, 1 table, 1 algorithm.

Table of Contents

  1. Introduction
  2. Prior Work
  3. Contributions
  4. System Model
  5. Uplink Channel Estimation and Quantization
  6. Precoder Design and Downlink Quantization
  7. Downlink Transmission
  8. Achievable Downlink SE: Hardening Bound
  9. Problem Formulation and Solution Approach
  10. Exact bit split by line search (finite search)
  11. Numerical Results
  12. Conclusion

Figures (4)

  • Figure 1: A massive MU-MIMO system where the AAS estimates uplink channels, forwards quantized CSI to the BBU via a limited-capacity fronthaul, and then the BBU sends the quantized precoding matrix for downlink data transmission towards the AAS.
  • Figure 2: Sum SE versus $B_{\rm H}$ for fixed $B_{\rm P}$ at $\text{SNR}=10$ dB.
  • Figure 3: Sum SE versus $B_{\rm H}$ at $\text{SNR}=-15$ dB.
  • Figure 4: Sum SE versus $B_{\rm H}$ at $\text{SNR}=10$ dB.