# Smart Pilot Assignment for IoT in Massive MIMO Systems: A Path Towards Scalable IoT Infrastructure

arXiv: https://arxiv.org/abs/2404.10188 · HTML: https://mlsanity.com/paper/2404.10188/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 6 references that are in the map.
- First posted on arXiv 16 Apr 2024.

## Builds on (most cited first, 1 of 1)

- [Towards Massive MIMO 2.0: Understanding spatial correlation, interference suppression, and pilot contamination](https://mlsanity.com/paper/1904.03406/) (2019)

## Built on by (most cited first, 1 of 1)

- [Pilot Contamination in Massive MIMO Systems: Challenges and Future Prospects](https://mlsanity.com/paper/2404.19238/) (2024)

## Abstract (arXiv)

5G sets the foundation for an era of creativity with its faster speeds, increased data throughput, reduced latency, and enhanced IoT connectivity, all enabled by Massive MIMO (M-MIMO) technology. M-MIMO boosts network efficiency and enhances user experience by employing intelligent user scheduling. This paper presents a user scheduling scheme and pilot assignment strategy designed for IoT devices, emphasizing mitigating pilot contamination, a key obstacle to improving spectral efficiency (SE) and system scalability in M-MIMO networks. We utilize a user clustering-based pilot allocation scheme to boost IoT device scalability in M-MIMO systems. Additionally, our smart pilot allocation minimizes interference and enhances SE by treating pilot assignment as a graph coloring problem, optimizing it through integer linear programming (ILP). Recognizing the computational complexity of ILP, we introduced a binary search-based heuristic predicated on interference threshold to expedite the computation, while maintaining a near-optimal solution. The simulation results show a significant decrease in the required pilot overhead (about 17%), and substantial enhancement in SE (about 8-14%).