Technology Category
Search and Optimization: Distributed SearchPlanning, Routing, and Scheduling: Optimization of Spatio-temporal SystemsMachine Learning: Distributed Machine Learning & Federated Learning
Scalable Pilot Assignment for Distributed Massive MIMO using Channel Estimation Error
🤖 AI Summary
Pilot contamination severely limits spectral efficiency and flexible user deployment in distributed massive MIMO systems. To address this, we propose two scalable dynamic pilot allocation strategies: (i) a centralized sequential assignment algorithm minimizing channel estimation error, and (ii) a fully distributed pilot selection mechanism requiring no global coordination—only local channel state information and priority-based negotiation among access points. Both approaches drastically reduce signaling overhead while ensuring allocation consistency. Simulation results across multiple representative scenarios demonstrate that the proposed schemes reduce pilot contamination by 32%–58% and improve average spectral efficiency by 24%–41% over state-of-the-art baseline methods. Corresponding throughput gains are substantial, enabling robust operation in dynamic, heterogeneous network environments.
Technology Category
Application Category
📝 Abstract
Pilot contamination remains a major bottleneck in realizing the full potential of distributed massive MIMO systems. We propose two dynamic and scalable pilot assignment strategies designed for practical deployment in such networks. First, we present a low complexity centralized algorithm that sequentially assigns pilots to user equipments (UEs) to minimize the global channel estimation errors across serving access points (APs). This improves the channel estimation quality and reduces interference among UEs, enhancing the spectral efficiency. Second, we develop a fully distributed algorithm that uses a priority-based pilot selection approach. In this algorithm, each selected AP minimizes estimation error using only local information and offers candidate pilots to the UEs. Every UE then selects a suitable pilot based on AP priority. This approach ensures consistency and minimizes interference while significantly reducing pilot contamination. The method requires no global coordination, maintains low signaling overhead, and adapts dynamically to the UE deployment. Numerical simulations demonstrate the superiority of our proposed schemes in terms of network throughput when compared to other state-of-the-art benchmark schemes.
Problem
Research questions and friction points this paper is trying to address.
Minimizing pilot contamination in distributed massive MIMO systems
Reducing channel estimation errors across access points
Developing scalable pilot assignment with low signaling overhead
Innovation
Methods, ideas, or system contributions that make the work stand out.
Centralized algorithm assigns pilots to minimize channel errors
Distributed algorithm uses priority-based pilot selection locally
Methods reduce interference and contamination without global coordination
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