CIF: Small: Collaborative Research: Synergistic Exploitation of Network Dynamics and Knowledge Heterogeneity in Wireless Networks

无线网络和服务的无处不在和加速增长严重依赖于无线频谱的可用性和有效利用。对频谱日益增长的需求已经将当前的商用无线网络推向了极限，并强调了对无线系统设计的变革性方法的需求。为了满足这一需求，无线网络正迅速向高密度、用户部署、异构基础设施发展，其特征是积极的频谱重用。这种进化架构可以实现高数据速率，尽管它们必须在严重干扰的情况下运行。在传统的系统设计中，干扰被视为一种负外部性，其最终目标是抑制或减轻干扰。该项目通过协同利用反馈、网络动态和网络知识异质性，开发了新的方法来拥抱干扰。在这项工作中利用的关键角度是利用干扰作为副信息。该项目将描述反馈提供的增益，并分析这种增益对网络拓扑的可扩展性和依赖性。此外，研究人员还研究了网络拓扑动态变化的影响，并设计了利用这种变化来提高整体网络性能的算法。研究人员还研究了多流多天线无线系统中异构信道知识的最佳利用，考虑了网络信道知识在空间和时间域表现出可变性的场景，并开发了利用这种可变性的算法。

The ubiquity and accelerated growth of wireless networks and services is critically dependent on the availability and efficient use of wireless spectrum. Increasing demand for spectrum is already pushing the current commercial wireless networks to their limits and accentuates the need for transformative approaches for wireless system design. In order to meet this demand, wireless networks are rapidly evolving towards a highly dense, user-deployed, heterogeneous infrastructure characterized by aggressive spectral reuse. Such evolutionary architectures can realize high data rates although they must operate in the presence of severe interference. In traditional system design, interference is viewed as a negative externality with the end goal being its suppression or mitigation. This project develops new approaches for embracing interference through synergistic exploitation of feedback, network dynamics and network knowledge heterogeneity. The key angle leveraged in this work is to exploit interference as side information. The project will characterize the gain provided by feedback, and analyze the scalability and dependence of such gains on network topology. In addition, the researchers investigate the impact of dynamical variations in network topology and devise algorithms that harness such variations to enhance the overall network performance. The researchers also investigate the optimal utilization of heterogeneous channel knowledge for multi-flow multi-antenna wireless systems, by considering scenarios in which network channel knowledge exhibits variability in spatial and temporal domains and developing algorithms to exploit such variability.