Interference Modeling And Control In Wireless Networks

With the successful commercialization of IEEE802.11 standard, wireless networks have become a tight-knit of our daily life. As wireless networks are increasingly applied to real- time and mission-critical tasks, how to ensuring real-time, reliable data delivery emerges as an important problem. However, wireless communication is subject to various dynamics and uncertainties due to the broadcast nature of wireless signal. In particular, co-channel interfer- ence not only reduces the reliability and throughput of wireless networks, it also increases the variability and uncertainty in data communication [64, 80, 77]. A basis of interference control is the interference model which \emph{predicts} whether a set of concurrent transmissions may interfere with one another. Two commonly used models, the \textit{SINR model} and the \textit{radio-K model}, are thoroughly studied in our work. To address the limitations of those models, we propose the physical-ratio-K(PRK) interference model as a reliablility-oriented instantiation of the ratio-K model, where the link-specific choice of K adapts to network and environmental conditions as well as application QoS requirements to ensure certain minimum reliability of every link. On the other hand, the interference among the transmissions, limits the number of con- current transmissions. We formulate the concept of \emph{interference budget} that, given a set of scheduled transmissions in a time slot, characterizes the additional interference power that can be tolerated by all the receivers without violating the application requirement on link reliability. We propose the scheduling algorithm \emph{iOrder} that optimizes link ordering by considering both interference budget and queue length in scheduling. Through both simulation and real-world experiments, we observe that optimizing link ordering can improve the performance of existing algorithms by a significant. Based on the strong preliminary research result on interference modeling and control, we will extend our method into distributed protocol designs. One future work will focus on imple- menting the \textit{PRK model} in a distributed protocols. We will also explore the benefits of using multiple channels in the interference control