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.
