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.

翻译：导频污染仍然是实现分布式大规模MIMO系统全部潜力的主要瓶颈。我们提出了两种动态且可扩展的导频分配策略，专为此类网络的实际部署而设计。首先，我们提出一种低复杂度的集中式算法，该算法按顺序为用户设备分配导频，以最小化服务接入点间的全局信道估计误差。这提高了信道估计质量并减少了用户设备间的干扰，从而提升了频谱效率。其次，我们开发了一种完全分布式的算法，采用基于优先级的导频选择方法。在该算法中，每个被选中的接入点仅利用本地信息最小化估计误差，并向用户设备提供候选导频。随后，每个用户设备根据接入点优先级选择合适的导频。该方法确保了一致性并最小化了干扰，同时显著降低了导频污染。该方案无需全局协调，保持了较低的信令开销，并能动态适应用户设备的部署。数值仿真表明，与其他先进的基准方案相比，我们提出的方案在网络吞吐量方面具有优越性。