NeTS: Medium: Collaborative Research: Big Data Enabled Wireless Networking: A Deep Learning Approach

无线网络变得越来越大，越来越复杂，每秒都会产生大量的运行时统计数据（如流量负载、资源使用等）。我们的目标不是将无线网络中的大数据视为不必要的负担，而是将其作为更好地了解用户需求和系统功能的绝佳机会，以便我们可以优化资源分配，更好地为移动用户服务。此外，云无线接入网络（c - ran）已成为下一代无线通信系统的关键使能技术。它们的集中式架构使得收集和分析各种运行时系统数据变得容易。该项目旨在探索如何利用强大的新机器学习技术，包括深度学习（DL）和深度强化学习（DRL），来抓住大数据提供的令人兴奋的机会，使未来的无线网络能够更好地为用户服务。本研究有望显著提高无线网络的资源利用率，降低无线网络的运营成本（如功耗），使无线网络运营商和移动用户受益匪浅，更重要的是有利于全球环境。除了无线网络之外，所提出的深度学习模型和算法可以在各种领域中找到其应用，包括视频内容分析，用户行为研究等。此外，拟议的项目预计将通过出版物、研讨会和讲习班以及国际和工业合作，促进公众对新兴5G无线通信、DL和DRL的理解。该项目的目标是开发一种新颖的深度学习方法，以实现具有大数据的未来无线网络的有效设计和运营。具体而言，我们将提出用于关键系统参数时空分析和预测的深度学习模型和算法，这些模型和算法可以为现有的资源分配算法提供准确和有用的输入信息，以更好地运行无线网络。此外，我们将开发一种新的基于drl的无线网络控制框架，通过在强大的深度神经网络指导下共同学习系统环境并做出决策，有效地分配其资源。为了实现上述目标，该项目被组织成三个连贯的重点：重点1基于深度学习的建模和预测；基于深度强化学习的动态资源分配以及推力3验证和性能评估。

Wireless networks are becoming larger and more complicated, generating a huge amount of runtime statistics data (such as traffic load, resource usages, etc.) every second. Instead of treating big data in wireless networks as an unwanted burden, we aim to leverage them as a great opportunity for better understanding user demands and system capabilities such that we can optimize resource allocation to better serve mobile users. In addition, Cloud Radio Access Networks (C-RANs) have become a key enabling technology for the next generation wireless communication systems. Their centralized architecture makes it easy to collect and analyze various runtime system data. This project aims to exploit how the powerful new machine learning techniques, including Deep Learning (DL) and Deep Reinforcement Learning (DRL), can be leveraged to grasp the exciting opportunity provided by big data to enable future wireless networks to better serve their users. The proposed research is expected to significantly improve resource utilization of wireless networks and reduce their operational costs (such as power consumption), which can substantially benefit wireless network carriers and mobile users, and more importantly, is good for global environment. Beyond wireless networking, the proposed DL models and algorithms may find its applications in a large variety of domains, including video content analysis, user behavior study, etc. Moreover, the proposed project is expected to advance public understanding of the emerging 5G wireless communications, DL and DRL via publications, seminars and workshops, and international and industrial collaborations. The objective of this project is to develop a novel deep learning approach to enable efficient design and operations of future wireless networks with big data. Specifically, we will propose DL models and algorithms for spatiotemporal analysis and prediction of key system parameters, which can provide accurate and useful input information for existing resource allocation algorithms to better operate a wireless network. Moreover, we will develop a novel DRL-based control framework for a wireless network to efficiently allocate its resources by jointly learning the system environment and making decisions under the guidance of a powerful deep neural network. To achieve the above object, the project is organized into three cohesive thrusts: Thrust 1 Deep Learning based Modeling and Prediction; Thrust 2 Deep Reinforcement Learning based Dynamic Resource Allocation; and Thrust 3 Validation and Performance Evaluation.