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

Source: arXiv - 2511.20511v1

Overview

The paper tackles the pilot‑assignment problem in massive MIMO (multiple‑input multiple‑output) systems—a bottleneck that directly affects the latency of next‑generation wireless networks such as 6G, autonomous‑vehicle communications, and industrial IoT. By marrying a hybrid K‑means clustering + Genetic Algorithm (SK‑means GA) with a custom FPGA‑based parallel implementation (PK‑means GA), the authors achieve a dramatic reduction in convergence time, bringing pilot‑assignment computation down to a few milliseconds.

Key Contributions

• Hybrid algorithm (SK‑means GA): Combines K‑means clustering for smart initialization with a Genetic Algorithm to refine pilot assignments, cutting convergence time by ≈29 % compared with a vanilla GA (82 s vs. 116 s).
• FPGA‑centric parallelization (PK‑means GA): Implements the hybrid algorithm on a Xilinx FPGA using Vivado HLS, achieving sub‑4 ms convergence (≈3.5 ms).
• Hardware optimizations: Demonstrates the impact of loop unrolling, pipelining, and function inlining on HLS‑generated RTL, quantifying speed‑up factors for each technique.
• Real‑time feasibility study: Shows that the accelerated pilot‑assignment can meet the stringent latency budgets of 6G‑grade, low‑latency wireless services.

Methodology

1. Problem formulation: Pilot assignment is modeled as a graph‑coloring task where each user is a node and edges represent interference potential. The goal is to assign the smallest set of orthogonal pilots while minimizing co‑channel interference.
2. Hybrid SK‑means GA:
   • K‑means clustering groups users with similar channel statistics, providing a good initial “coloring” (pilot set).
   • Genetic Algorithm then evolves these initial solutions using selection, crossover, and mutation, searching for a near‑optimal assignment.
3. Parallel FPGA implementation (PK‑means GA):
   • The algorithm is expressed in C/C++ and fed to Vivado High‑Level Synthesis (HLS).
   • Critical loops (e.g., distance calculations in K‑means, fitness evaluation in GA) are unrolled and pipelined to exploit massive data‑level parallelism.
   • Function inlining reduces call overhead and enables deeper pipeline stages.
4. Evaluation: Simulations on realistic massive‑MIMO channel models compare the serial SK‑means GA, the parallel PK‑means GA, and a baseline GA in terms of convergence time, pilot‑reuse factor, and resulting channel‑estimation error.

Results & Findings

• The hybrid approach yields slightly better pilot reuse and marginally lower NMSE than a plain GA, confirming that smarter initialization matters.
• The FPGA implementation compresses the entire optimization loop into a few thousand clock cycles, making pilot reassignment feasible on a per‑frame basis (e.g., every 1 ms).
• Loop unrolling contributed up to 4× speedup, while pipelining added another 2.5×, and function inlining shaved off the remaining overhead.

Practical Implications

• Real‑time network slicing: Operators can dynamically re‑assign pilots as users move, without violating latency SLAs for ultra‑reliable low‑latency communications (URLLC).
• Edge‑compute integration: The PK‑means GA can be embedded directly into base‑station ASICs or edge‑FPGA accelerators, offloading the CPU and freeing up cycles for higher‑layer tasks (e.g., scheduling, beamforming).
• Scalable 6G deployments: As antenna counts rise (≥ 256 elements) and user densities increase, the parallel approach scales linearly with FPGA resources, preserving low latency.
• Developer‑friendly toolchain: Since the design lives in high‑level C/C++ and is synthesized with Vivado HLS, software engineers can iterate quickly, bridging the gap between algorithm research and hardware deployment.

Limitations & Future Work

• Hardware specificity: The reported speedups are tied to a Xilinx UltraScale+ device; porting to other FPGA families or ASICs may require retuning of unroll factors and pipeline depths.
• Static clustering granularity: K‑means uses a fixed number of clusters; adaptive clustering based on traffic load could further improve pilot reuse.
• Energy consumption: While latency is dramatically reduced, the paper does not quantify power/energy trade‑offs of the parallel accelerator—a key metric for green 6G infrastructure.
• Extension to multi‑cell scenarios: The current model assumes a single cell; future work could explore coordinated pilot assignment across neighboring cells using distributed FPGA clusters.

Authors

• Eman Alqudah
• Ashfaq Khokhar

Paper Information

• arXiv ID: 2511.20511v1
• Categories: cs.DC
• Published: November 25, 2025
• PDF: Download PDF