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

TL;DR

This paper presents a parallel FPGA implementation of an optimized hybrid clustering and genetic algorithm for pilot assignment in massive MIMO systems, significantly reducing convergence time for real-time applications.

Contribution

It introduces a novel parallel FPGA-based implementation of a hybrid clustering and genetic algorithm for efficient pilot assignment in massive MIMO systems, achieving ultra-fast convergence.

Findings

29.3% reduction in convergence time (82s vs. 116s)

Achieved 3.5 ms convergence time with FPGA implementation

Enhanced suitability for low-latency 6G wireless networks

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)