CAREER: Scalable Distributed MIMO: Towards Density-Proportional Capacity Scaling for Infrastructure Wireless Networks

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The proliferation of mobile devices is driving an ever-increasing demand on the capacity of infrastructure wireless networks, especially Wi-Fi. Current wireless networks can hardly meet this demand because they are interference limited -- per-user throughput decreases super-linearly with network density. Adding more access points (APs) may worsen the problem, because the APs themselves interfere with each other. The objective of this research is to overcome this fundamental limitation, and enable network capacity to scale proportionally with node density, by exploring a transformative architecture called Scalable Distributed MIMO (SDM). The principle behind SDM is to reorganize the APs into clusters. Within each cluster, the APs tightly synchronize and share data with each other. This enables them to cancel the mutual interference and scale network capacity with AP density. Different AP clusters contend for channel access in a self-organized manner, thus scaling capacity across an entire network. The long-term objective of SDM is to enable dense indoor infrastructure networks supporting Gbps per-user throughput, marking an important step towards the goals outlined in the National Broadband Plan. To realize the SDM principle, the proposed research synthesizes a comprehensive framework of tasks spanning five years, including performance modeling/analysis, network protocol/algorithm design, and implementation/experimentation on a software radio testbed. In particular, the PI plans to (i) systematically explore ways of deploying AP clusters to balance the tradeoffs between system complexity, scalability, and compatibility with legacy networks; (ii) design a new paradigm of cluster-centric network protocols that feature tight coordination between APs within a cluster and self-organization among different clusters; (iii) develop new communications algorithms that tame the coordination overhead while maximizing cluster capacity, and translate the capacity gain into improved end-user experience. The testbed used in the research tasks will be extended into a user-friendly educational platform that enhances the knowledge of wireless networks for students at different levels. The research and educational materials will be broadly disseminated for reproducibility of results.