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Efficient Parallel Implementation of the Pilot Assignment Problem in Massive MIMO Systems

Eman Alqudah, Ashfaq Khokhar

TL;DR

The paper tackles pilot contamination in multi-cell massive MIMO by formulating the pilot assignment problem as a sum-SE maximization and proposing a hybrid SK-means GA framework. A parallel, FPGA-accelerated PK-means GA variant enables real-time pilot allocation on Vivado HLS with significant speedups. Results show a 29% reduction in convergence time for SK-means GA and millisecond-level convergence (3.5 ms) for the FPGA implementation. These findings demonstrate the viability of low-latency, scalable pilot scheduling for future 5G/6G networks and IoT deployments.

Abstract

The assignment of the pilot sequence is a critical challenge in massive MIMO systems, as sharing the same pilot sequence among multiple users causes interference, which degrades the accuracy of the channel estimation. This problem, equivalent to the NP-hard graph coloring problem, directly impacts real-time applications such as autonomous driving and industrial IoT, where minimizing channel estimation time is crucial. This paper proposes an optimized hybrid K-means clustering and Genetic Algorithm (SK-means GA) to improve the pilot assignment efficiency, achieving a 29.3% reduction in convergence time (82s vs. 116s for conventional GA). A parallel implementation (PK-means GA) is developed on an FPGA using Vivado High-Level Synthesis Tools (HLST) to further enhance the run-time performance, accelerating convergence to 3.5 milliseconds. Within Vivado implementation, different optimization techniques such as loop unrolling, pipelining, and function inlining are applied to realize the reported speedup. This significant improvement of PK-means GA in execution speed makes it highly suitable for low-latency real-time wireless networks (6G)

Efficient Parallel Implementation of the Pilot Assignment Problem in Massive MIMO Systems

TL;DR

The paper tackles pilot contamination in multi-cell massive MIMO by formulating the pilot assignment problem as a sum-SE maximization and proposing a hybrid SK-means GA framework. A parallel, FPGA-accelerated PK-means GA variant enables real-time pilot allocation on Vivado HLS with significant speedups. Results show a 29% reduction in convergence time for SK-means GA and millisecond-level convergence (3.5 ms) for the FPGA implementation. These findings demonstrate the viability of low-latency, scalable pilot scheduling for future 5G/6G networks and IoT deployments.

Abstract

The assignment of the pilot sequence is a critical challenge in massive MIMO systems, as sharing the same pilot sequence among multiple users causes interference, which degrades the accuracy of the channel estimation. This problem, equivalent to the NP-hard graph coloring problem, directly impacts real-time applications such as autonomous driving and industrial IoT, where minimizing channel estimation time is crucial. This paper proposes an optimized hybrid K-means clustering and Genetic Algorithm (SK-means GA) to improve the pilot assignment efficiency, achieving a 29.3% reduction in convergence time (82s vs. 116s for conventional GA). A parallel implementation (PK-means GA) is developed on an FPGA using Vivado High-Level Synthesis Tools (HLST) to further enhance the run-time performance, accelerating convergence to 3.5 milliseconds. Within Vivado implementation, different optimization techniques such as loop unrolling, pipelining, and function inlining are applied to realize the reported speedup. This significant improvement of PK-means GA in execution speed makes it highly suitable for low-latency real-time wireless networks (6G)

Paper Structure

This paper contains 17 sections, 8 equations, 5 figures, 3 tables, 2 algorithms.

Table of Contents

  1. Introduction
  2. Related Work
  3. Pilot Assignment Problem (PAP)
  4. System Model
  5. Uplink Spectral Efficiency
  6. Problem Formulation
  7. Proposed Pilot Sequences Allocation Scheme
  8. Sequential K-means GA and Chromosome Encoding
  9. Fitness Evaluation Function
  10. Parallel K-means Clustered GA (PK-means GA)
  11. Real-Time Parallel Integrated Pilot Allocation Scheme
  12. Results and Discussion
  13. Simulation Setup
  14. Performance Evaluation and Comparison
  15. Hardware Resource Utilization
  16. ...and 2 more sections

Figures (5)

  • Figure 1: Coherence intervals in time-frequency space.
  • Figure 2: Clustered multi-cell, multi-user massive MIMO system.
  • Figure 3: CDF of best fitness values for Random Assignment, Traditional GA, and SK-means GA.
  • Figure 4: Computation time vs. number of users for all algorithms.
  • Figure 5: Computation time vs. number of users for different numbers of BS antennas $M$.