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Pilot Assignment for Distributed Massive MIMO Based on Channel Estimation Error Minimization

Mohd Saif Ali Khan, Karthik RM, Samar Agnihotri

TL;DR

The paper tackles pilot contamination in distributed cell-free massive MIMO by proposing two scalable pilot assignment schemes. The centralized approach (EEM-PA) greedily minimizes a holistic channel-estimation error metric to guide pilot allocation, while the fully distributed approach (DPB-PA) uses a priority-based, locally driven pilot selection mechanism with no inter-AP coordination. A novel estimation-error metric for centralized PA and a low-overhead distributed protocol enable dynamic operation with reduced signaling and complexity, and numerical results show substantial uplink throughput gains and robustness across varying network densities and pilot lengths. Together, these schemes offer practical pathways to scalable deployment of large-scale D-mMIMO networks, accommodating both centralized and distributed architectures and dynamic UE arrivals.

Abstract

Pilot contamination remains a major bottleneck in realizing the full potential of distributed massive MIMO systems. We propose two dynamic and scalable pilot assignment schemes designed for practical deployment in such networks. First, we present a low-complexity centralized scheme that sequentially assigns pilots to user equipments (UEs) to minimize the global channel estimation errors across serving access points (APs). This improves the channel estimation quality and reduces interference among UEs, enhancing the spectral efficiency. Second, we develop a fully distributed scheme that uses a priority-based pilot selection approach. In this scheme, each selected AP minimizes the channel estimation error using only local information and offers candidate pilots to the UEs. Every UE then selects a suitable pilot based on its AP priority. This approach ensures consistency and minimizes interference while significantly reducing pilot contamination. The method requires no global coordination, maintains low signaling overhead, and adapts dynamically to the UE deployment. Numerical simulations demonstrate the superiority of the proposed schemes in terms of network throughput when compared to the existing state-of-the-art schemes.

Pilot Assignment for Distributed Massive MIMO Based on Channel Estimation Error Minimization

TL;DR

The paper tackles pilot contamination in distributed cell-free massive MIMO by proposing two scalable pilot assignment schemes. The centralized approach (EEM-PA) greedily minimizes a holistic channel-estimation error metric to guide pilot allocation, while the fully distributed approach (DPB-PA) uses a priority-based, locally driven pilot selection mechanism with no inter-AP coordination. A novel estimation-error metric for centralized PA and a low-overhead distributed protocol enable dynamic operation with reduced signaling and complexity, and numerical results show substantial uplink throughput gains and robustness across varying network densities and pilot lengths. Together, these schemes offer practical pathways to scalable deployment of large-scale D-mMIMO networks, accommodating both centralized and distributed architectures and dynamic UE arrivals.

Abstract

Pilot contamination remains a major bottleneck in realizing the full potential of distributed massive MIMO systems. We propose two dynamic and scalable pilot assignment schemes designed for practical deployment in such networks. First, we present a low-complexity centralized scheme that sequentially assigns pilots to user equipments (UEs) to minimize the global channel estimation errors across serving access points (APs). This improves the channel estimation quality and reduces interference among UEs, enhancing the spectral efficiency. Second, we develop a fully distributed scheme that uses a priority-based pilot selection approach. In this scheme, each selected AP minimizes the channel estimation error using only local information and offers candidate pilots to the UEs. Every UE then selects a suitable pilot based on its AP priority. This approach ensures consistency and minimizes interference while significantly reducing pilot contamination. The method requires no global coordination, maintains low signaling overhead, and adapts dynamically to the UE deployment. Numerical simulations demonstrate the superiority of the proposed schemes in terms of network throughput when compared to the existing state-of-the-art schemes.
Paper Structure (10 sections, 9 equations, 5 figures, 1 table, 3 algorithms)

This paper contains 10 sections, 9 equations, 5 figures, 1 table, 3 algorithms.

Table of Contents

  1. Introduction
  2. System Model
  3. Channel Estimation
  4. Uplink Data Transmission
  5. Pilot Assignment
  6. Centralized Pilot Assignment
  7. Distributed Pilot Assignment
  8. Complexity and Overhead Comparison
  9. Numerical Simulations
  10. Conclusion

Figures (5)

  • Figure 1: Priority-Based Pilot Selection Flowchart
  • Figure 2: The uplink sum SE vs number of UEs ($T$) plot for different PA schemes.
  • Figure 3: The uplink sum SE vs pilot symbol length ($L_p$) plot for different PA schemes, when number of antennas per AP, $A=16$.
  • Figure 4: The uplink sum SE vs AP-UE association threshold $\beta_{th}$.
  • Figure 5: Cumulative distribution function (CDF) versus Per-user SE for different PA schemes.