NeTS: Medium: Multicell MIMO for Scalable High-Density Wireless Networks

公众对数据的需求一直在以巨大的速度增长，而且增长没有显示出减弱的迹象。 特别地，用户越来越需要来自诸如智能电话、平板电脑和膝上型电脑的无线设备的数据。 为了适应这种需求，部署在高需求区域的无线接入点（AP）的数量大幅增加，这导致附近无线通信之间的显著干扰，这降低了性能并可能导致未满足的用户需求。 该研究正在研究基于多输入多输出（MIMO）通信的高级编码、信号处理和调度技术的开发，以显著减少竞争传输之间的干扰，从而大幅增加可容纳的整体用户需求。 该项目将通过向无线用户开放新型高需求应用（如增强现实和虚拟现实）来造福社会，并将在面对不断增长的数据需求时保持这些应用和其他应用的性能。 该项目还将通过整合先进信号处理发展和无线网络的统一课程来加强教育，而不是将这些作为单独学科的传统方法。 本研究在覆盖家庭网络和小型企业的2-6个AP的单个集群的AP协调的框架内，调查用于多小区MIMO的新的非线性处理技术。 为了满足大型企业无线网络的需求，该项目还在研究一种分层方法，该方法采用创新技术来处理集群之间的边缘节点干扰问题，以扩展多个AP集群的性能。 该项目将在多AP无线网络的性能扩展领域开发算法、技术和协议扩展。 这些贡献包括：（1）用于最大化AP集群中的下行链路性能的新颖的非线性预编码方法，（2）用于选择集群中用于并发通信的用户组的新算法和基于来自VLSI电路划分的经典算法的用于迭代地改进用户分组的算法，（3）分段移动的和固定用户并按需分配移动的时隙的时隙协议，（4）提供跨AP集群的一致的接入点定向以便消除集群边界处的客户端之间的干扰的方法，（5）在集群之间执行的松散协调协议，其将允许相邻集群处理它们之间的干扰，而没有跨网络交换全部信道状态信息的昂贵开销，以及（6）用于对多个提供商网络的客户端协调接入的新的物理层感知技术。

The public's demand for data has been growing at an enormous rate and growth is showing no signs of diminishing. In particular, users are increasingly demanding data from wireless devices such as smart phones, tablets, and laptops. To accommodate this demand, the number of wireless access points (APs) being deployed in high-demand areas has increased substantially and this has resulted in significant interference between nearby wireless communications, which reduces performance and can result in unmet user demands. This research is studying the development of advanced coding, signal processing, and scheduling techniques based on multiple-input multiple-output (MIMO) communication to significantly reduce interference between competing transmissions, thereby substantially increasing the overall user demand that can be accommodated. The project will benefit society by opening up new types of high-demand applications, such as augmented and virtual realities, to wireless users and will maintain performance for these and other applications in the face of ever-increasing data demands. The project will also enhance education through unified courses that integrate advanced signal processing developments and wireless networking instead of the traditional approach, which treats these as separate disciplines. This research is investigating new nonlinear processing techniques for multicell MIMO within a framework of AP coordination for single clusters of 2-6 APs, which covers home networks and small enterprises. To address the demands of large enterprise wireless networks, the project is also researching a hierarchical approach, which employs innovative techniques to deal with the problem of edge-node interference between clusters to scale performance across multiple AP clusters. The project will develop algorithms, techniques, and protocol extensions in the area of performance scaling for multi-AP wireless networks. Among the contributions are: (1) a novel non-linear precoding approach for maximizing downlink performance in an AP cluster, (2) a new algorithm for selecting groups of users for concurrent communication in a cluster and an algorithm, based on a classic algorithm from VLSI circuit partitioning, for iteratively improving the user groupings, (3) a slotted protocol that segments mobile and stationary users and allocates mobile slots on demand, (4) an approach to providing consistent access point orientation across AP clusters so as to eliminate interference between clients at cluster boundaries, (5) a loose coordination protocol executed between clusters that will allow neighboring clusters to handle interference between them without the costly overhead of exchanging full channel state information across the network, and (6) new physical-layer-aware techniques for client-coordinated access to multiple providers networks.