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Adaptive Local Combining with Decentralized Decoding for Distributed Massive MIMO

Mohd Saif Ali Khan, Karthik RM, Samar Agnihotri

TL;DR

The paper addresses scalability and performance bottlenecks in distributed Massive MIMO uplink by introducing a fully decentralized decoding architecture that bypasses LSFD. It empowers each AP to compute interference-suppressing local weights and adapt its combining strategy using pilot-dependent processing, via generalized PFZF and PWPFZF schemes with decentralized pilot-group optimization. Closed-form SE expressions and a proximal gradient-based KPI (pilot grouping) enable tractable design, while analysis shows significant reductions in fronthaul and computation compared with LSFD and fixed-threshold methods, with only modest SE trade-offs. Collectively, the approach offers a practical, scalable solution for ultra-dense D-mMIMO deployments without sacrificing QoS.

Abstract

A major bottleneck in uplink distributed massive multiple-input multiple-output networks is the sub-optimal performance of local combining schemes, coupled with high fronthaul load and computational cost inherent in centralized large scale fading decoding (LSFD) architectures. This paper introduces a decentralized decoding architecture that fundamentally breaks from the LSFD, by allowing each access point (AP) to calculate interference-suppressing local weights independently and apply them to its data estimates before transmission. Furthermore, two generalized local zero-forcing (ZF) frameworks, generalized partial full-pilot ZF (G-PFZF) and generalized protected weak PFZF (G-PWPFZF), are introduced, where each AP adaptively and independently determines its combining strategy through a local sum spectral efficiency (SE) optimization that classifies user equipments (UEs) as strong or weak, eliminating the fixed thresholds used in the PFZF and PWPFZF schemes. To enhance scalability, pilot-dependent combining vectors instead of user-dependent ones are introduced and are shared among users with the same pilot. The closed-form SE expressions corresponding to the proposed schemes are derived. Numerical results show that the proposed schemes consistently outperform fixed-threshold counterparts, while the introduction of local weights yields lower overheads and computation costs with lower performance penalty compared to them.

Adaptive Local Combining with Decentralized Decoding for Distributed Massive MIMO

TL;DR

The paper addresses scalability and performance bottlenecks in distributed Massive MIMO uplink by introducing a fully decentralized decoding architecture that bypasses LSFD. It empowers each AP to compute interference-suppressing local weights and adapt its combining strategy using pilot-dependent processing, via generalized PFZF and PWPFZF schemes with decentralized pilot-group optimization. Closed-form SE expressions and a proximal gradient-based KPI (pilot grouping) enable tractable design, while analysis shows significant reductions in fronthaul and computation compared with LSFD and fixed-threshold methods, with only modest SE trade-offs. Collectively, the approach offers a practical, scalable solution for ultra-dense D-mMIMO deployments without sacrificing QoS.

Abstract

A major bottleneck in uplink distributed massive multiple-input multiple-output networks is the sub-optimal performance of local combining schemes, coupled with high fronthaul load and computational cost inherent in centralized large scale fading decoding (LSFD) architectures. This paper introduces a decentralized decoding architecture that fundamentally breaks from the LSFD, by allowing each access point (AP) to calculate interference-suppressing local weights independently and apply them to its data estimates before transmission. Furthermore, two generalized local zero-forcing (ZF) frameworks, generalized partial full-pilot ZF (G-PFZF) and generalized protected weak PFZF (G-PWPFZF), are introduced, where each AP adaptively and independently determines its combining strategy through a local sum spectral efficiency (SE) optimization that classifies user equipments (UEs) as strong or weak, eliminating the fixed thresholds used in the PFZF and PWPFZF schemes. To enhance scalability, pilot-dependent combining vectors instead of user-dependent ones are introduced and are shared among users with the same pilot. The closed-form SE expressions corresponding to the proposed schemes are derived. Numerical results show that the proposed schemes consistently outperform fixed-threshold counterparts, while the introduction of local weights yields lower overheads and computation costs with lower performance penalty compared to them.
Paper Structure (15 sections, 2 theorems, 60 equations, 10 figures, 3 tables, 1 algorithm)

This paper contains 15 sections, 2 theorems, 60 equations, 10 figures, 3 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Contributions
  3. System Model
  4. Channel Estimation
  5. Uplink Transmission and Spectral Efficiency
  6. Adaptive Combining and Local Weights Analysis
  7. Generalized Partial Full-Pilot Zero Forcing Combining Scheme
  8. Generalized Protected Weak Partial Full-Pilot Zero Forcing Combining Scheme
  9. Performance Evaluation
  10. Computation cost and Fronthaul cost
  11. Performance Evaluation
  12. Conclusion and Future Work
  13. Derivation of closed-form SINR expression for G-PFZF combining
  14. Proof of Lipschitz Continuity of $\nabla f(\boldsymbol{\delta}_m)$
  15. Derivation of closed-form SINR expression for G-PWPFZF combining

Key Result

Theorem 1

bjornson2017massivezhang2021local : A lower bound on the uplink ergodic SE for UE $t$ is given by where $L_u =(\frac{1- \frac{L_p}{L_c}}{2} )$ and $\textsc{SINR}_t$ is where,

Figures (10)

  • Figure 1: Uplink processing architectures: (a) Fully centralized, (b) LSFD with coordination, (c) Simple decoding, (d) Proposed decentralized decoding.
  • Figure 2: Comparison of computational and fronthaul costs for different decoding and combining schemes.
  • Figure 3: Distribution of strong pilot decisions.
  • Figure 4: The uplink sum SE comparison for various combining schemes.
  • Figure 5: The uplink sum SE comparison for various combining schemes.
  • ...and 5 more figures

Theorems & Definitions (2)

  • Theorem 1
  • Lemma 1