NeTS: Small: Designing an Advanced Mobility Management and Utilization Framework for Enabling mmWave Multi-Band Ultra-Dense Cellular Networks of Future

利用毫米波频谱可以解决移动的蜂窝网络中两个长期存在的相互关联的问题：频谱稀缺和干扰。因此，正在寻求使用毫米波和5GHz以下频谱部署密集的小小区网络，作为缓解当前无线容量问题的一种方式。虽然这种多频带超密集网络的出现可以解决这两个问题，但它产生了一个新的具有挑战性的问题：在由具有完全不同传播特性的宽范围频带上的不同大小的小区组成的这种密集网络中有效且无缝地管理用户移动性。由于处理用户移动性在移动的网络中是必不可少的，因此确保新兴蜂窝网络架构中的无缝和有效移动性的可行性要求从当前管理用户移动性的方式进行转变。该项目的总体目标是通过开发高级移动性管理和利用框架（AM-MUF），将移动性管理从被动转变为主动过程，从而引发这一转变。该项目提供了强大的劳动力培训，提供开展拟议研究所需的备受追捧的多学科技能，同时确保妇女和其他代表性不足的群体的参与，以及K-12外联。该项目还利用与蜂窝生态系统的主要国家和国际利益相关者的合作：1）使用来自实时网络的数据验证移动性预测模型; 2）在全尺寸室外5G测试平台上评估拟议的解决方案; 3）在真实的网络上进行现场试验; 4）通过基于试验台的结果演示，促进5G标准化机构对项目成果的适应。AM-MUF将通过三个相互关联的研究方向开发：1）首先，该项目将开发实际可实现的模型，用于预测超密集多频带网络中用户移动性的一系列属性; 2）其次，预测模型将用于开发敏捷和可扩展的下一代解决方案，算法和协议，用于基于主动移动性的鲁棒优化和基于主动移动性的负载平衡; 3）第三，该项目将得出：在包括小区密度和移动性场景的不同系统配置下预测算法的准确性的基本限制;以及对于给定预测准确性所开发的解决方案的性能界限。为了实现这一雄心勃勃的目标，研究人员将利用一种系统化的方法，包括分析建模、系统级仿真、基于合成数据的训练和测试、基于真实的数据的验证、基于全尺寸5G测试床的评估以及在真实的网络上的现场试验。

Harnessing millimeter-wave spectrum can solve the two long-standing, interlocked problems in mobile cellular networks: spectrum scarcity and interference. Therefore, deploying dense small-cell networks using millimeter wave and sub-5GHz spectrum is being pursued as a way to mitigate current wireless capacity problems. While advent of such multi-band ultra-dense networks may solve these two problems, it gives birth to a new challenging problem: managing user mobility efficiently and seamlessly in such a dense network consisting of cells of varying sizes on a wide range of frequency bands with entirely different propagation characteristics. As handling user mobility is essential in a mobile network, ensuring the viability of seamless and efficient mobility in emerging cellular network architectures calls for a shift from the way user mobility is currently managed. The overarching goal of this project is to trigger this shift by transforming mobility management from being a reactive to a proactive process by developing an Advanced Mobility Management and Utilization Framework (AM-MUF). This project offers strong workforce training in a highly sought-after multi-disciplinary skill set needed to conduct proposed research, while ensuring participation of women and other underrepresented groups, and K-12 outreach. The project also leverages collaboration with key national and international stakeholders in cellular ecosystems to: 1) validate the mobility prediction models with data from a live network; 2) evaluate proposed solutions on a full scale outdoor 5G testbed; 3) conduct field trials on a real network; 4) promote adaptation of the project outcomes by 5G standardization bodies through a testbed-based demonstration of results.AM-MUF will be developed through three interlinked research thrusts: 1) First, the project will develop practically implementable models for predicting a range of attributes of user mobility in ultra-dense multi-band networks; 2) Second, the prediction models will be leveraged to develop agile and scalable next generation solutions, algorithms and protocols for proactive mobility-based robust optimization and proactive mobility-based load balancing; 3) Third, the project will derive: the fundamental limits of accuracy of the prediction algorithms under different system configurations including cell density and mobility scenarios; and performance bounds of the developed solutions for a given prediction accuracy. To achieve this ambitious goal, the researchers will leverage a systematic methodology consisting of analytical modeling, system level simulations, synthetic data based training and testing, real data based validation, a full scale 5G test-bed based evaluations and field trials on a real network.