mmCell: Enabling Next Generation Cellular Networking via mmWave

蜂窝网络面临两个基本问题：(I)无线频谱稀缺，(Ii)现有集中式体系结构的低效。解决稀缺性问题的办法是超越目前的亚6 GHz频率，利用极高的频段(30-300 GHz)，即毫米波(毫米波)频段[3]。现有架构的低效可以通过去中心化来解决。在传统架构中，基站将所有通信从发送器转发到接收器。消除这种中间人大大提高了效率，并允许发送器和接收器之间的直接通信，即设备到设备(D2D)通信。尽管上述问题的解决方案看起来很直观，但在这些解决方案之前仍有重大挑战。一方面，毫米波频段受到严重的信号衰减的影响，这使得远程和非视距通信--这两种通信对蜂窝网络至关重要--非常具有挑战性。另一方面，D2D通信需要复杂的干扰控制、资源分配和隐私/安全保障，这在现有的网络设计中是很难支持的。在MMcell中，我们提出了一条解决这些根本问题的现实途径。我们针对蜂窝网络中的MmWave D2D通信问题提出了解决方案，特别关注了MAC层的发现、调度、能量效率和移动性。在这个提案中，我们利用博弈论和机器学习等分析工具来进行系统建模和优化。超越理论，我们将通过最先进的软件定义无线电(SDR)平台对分析解决方案进行实证验证。特别是，这项提议旨在促进对蜂窝网络的MmWave D2D支持。

Cellular networks suffer from two fundamental problems: (i) wireless spectrum scarcity, and (ii) inefficiency of the existing centralized architecture. The scarcity problem is tackled by going beyond the current sub-6GHz frequencies and exploiting extremely high-frequency bands (30-300 GHz), i.e., millimeter Wave (mmWave) band [3]. The inefficiency of the existing architecture can be solved by decentralization. In the legacy architecture, the base station relays all communications from the transmitter to the receiver. Eliminating this man-in-the-middle enhances the efficiency significantly and allows direct communication between the transmitter and the receiver, i.e., Device-to-Device (D2D) communication.Although the solutions to the above problems seem intuitive, there are significant challenges to ahead of such solutions. On the one hand, mmWave bands suffer from severe signal attenuation making long range and non-line-of-sight communications -both essential to cellular networks- very challenging. On the other hand, D2D communication demands sophisticated interference control, resource allocation, and privacy/security provisioning which is hard to support in the existing network design. In mmCell, we present a realistic pathway to address these fundamental problems. We propose solutions to the problems of mmWave D2D communication in cellular networks with special focus MAC layer aspects, e.g., discovery, scheduling, energy efficiency, and mobility. In this proposal, we leverage analytical tools such as game theory and machine learning for system modeling and optimization. Going beyond theory, we will empirically validate the analytical solutions via the state-of-the-art Software-Defined Radio (SDR) platforms. In particular, this proposal aims to facilitate mmWave D2D support for cellular networks.