Scalable Pilot Assignment for Distributed Massive MIMO using Channel Estimation Error

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.