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

2025-11-25

AI Summary

Pilot contamination in massive MIMO—caused by pilot reuse—induces severe channel estimation interference, which can be formulated as an NP-hard graph coloring problem, hindering ultra-low-latency applications in 6G. To address this, we propose SK-means-GA, a hybrid algorithm: first, an enhanced K-means clustering reduces the graph size by grouping users spatially; then, a genetic algorithm (GA) optimizes pilot assignment on the resulting subgraphs. Furthermore, we implement the algorithm on an FPGA using high-level synthesis (HLS), incorporating loop unrolling, pipelining, and function inlining to maximize hardware parallelism. Experiments show that SK-means-GA reduces GA convergence time by 29.3% (from 116 s to 82 s); with FPGA acceleration, runtime drops to just 3.5 ms. This yields significant improvements in real-time performance and channel estimation accuracy, providing an efficient, hardware-accelerated pilot scheduling solution for industrial IoT and autonomous driving scenarios.

Technology Category

• Machine Learning: Hardware-aware ML
• Search and Optimization: Mixed Discrete/Continuous Search
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization

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)

Problem

• Optimizing pilot assignment to reduce interference in massive MIMO
• Solving NP-hard pilot assignment equivalent to graph coloring
• Minimizing channel estimation time for real-time 6G applications

Innovation

• Hybrid K-means clustering with Genetic Algorithm
• Parallel FPGA implementation using Vivado HLST
• Optimized with loop unrolling and pipelining techniques