Enhanced Non-Orthogonal Multiple Access (NOMA) for 5G Wireless Systems based on Deep Learning

基于深度学习的 5G 无线系统增强型非正交多址接入 (NOMA)

• 批准号: RGPIN-2019-04727
• 负责人: Kim, IlMin
• 金额: $ 3.35万
• 依托单位: Queen's University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=689272
• 关键词: Enhanced; Non; Orthogonal; Multiple; Access

In the proposed research, we will develop new and effective mobile communication techniques by utilizing deep learning technology. The wireless industry is currently facing many challenges in developing fifth generation (5G) wireless systems: extremely high data rates, extremely short transmission delay, and exponentially increasing mobile data traffic. For example, in 5G, network-level data rate should be 10-20 Gbps (10-20 times higher compared to the current 4G) and the latency (the end-to-end transmission delay) should be one millisecond (one-fifth compared to the current 4G). Furthermore, it is expected that Canadian mobile data traffic will grow 500% from 2016 to 2022 at a compound annual growth rate of 38%. In order to address the challenges, many researchers have studied a fundamentally different way of communications in which multiple users are simultaneously allowed to transmit (or receive) their data by non-orthogonally using the system resources (e.g., time, frequency, codes). This technique is referred to as the non-orthogonal multiple access (NOMA), which can theoretically provide much higher data rate and shorter delay. However, the NOMA system design and optimization is very challenging, because the system is highly nonlinear and very complicated. In order to overcome the critical issues of the NOMA systems, this research proposes to take a new and intelligent approach by exploiting state-of-the-art deep learning techniques. Deep learning is a branch of machine learning that has the capability of learning useful representation for data by extracting complicated distributed features automatically, resulting in very powerful nonlinear systems. Deep learning is an important component of artificial intelligence (AI), which has been shown to help advance a wide variety of research areas. By utilizing state-of-the-art deep learning techniques, we will develop NOMA systems that can autonomously operate and support very high data rates. Specifically, using deep learning, we aim to resolve four major challenges of NOMA: error mitigation, user grouping, precoding, and adaptive control. ******According to a new report from Accenture, the new 5G wireless system will contribute $40 billion annually to Canada's economy by 2026. Furthermore, in Canada's wireless industry, more than 150,000 short-term jobs will be created from 2020 to 2025, and an additional 250,000 permanent jobs will be annually created by 2026. The proposed research will enable Canadian wireless companies to grow fast and to lead the global wireless industry. Most of all, the proposed research is “deliberately” and “strategically” designed to train HQP in two key areas at the same time: i) wireless communications and ii) deep learning. In the proposed research program, 4 MSc students and 4 PhD students will be trained to learn the state-of-the-art for future wireless systems and the fundamentals of deep learning.**

在拟议的研究中，我们将利用深度学习技术开发新的和有效的移动的通信技术。无线行业目前在开发第五代（5G）无线系统方面面临许多挑战：极高的数据速率、极短的传输延迟以及呈指数级增长的移动的数据业务。例如，在5G中，网络级数据速率应该是10-20 Gbps（与当前4G相比高10-20倍），延迟（端到端传输延迟）应该是1毫秒（与当前4G相比的五分之一）。此外，预计加拿大移动的数据流量将在2016年至2022年期间增长500%，复合年增长率为38%。为了应对这些挑战，许多研究人员已经研究了一种根本不同的通信方式，其中允许多个用户通过非正交地使用系统资源（例如，时间、频率、代码）。这种技术被称为非正交多址接入（NOMA），理论上可以提供更高的数据速率和更短的延迟。然而，NOMA系统的设计和优化是非常具有挑战性的，因为系统是高度非线性和非常复杂的。为了克服NOMA系统的关键问题，本研究提出通过利用最先进的深度学习技术采取一种新的智能方法。深度学习是机器学习的一个分支，它能够通过自动提取复杂的分布式特征来学习数据的有用表示，从而产生非常强大的非线性系统。深度学习是人工智能（AI）的重要组成部分，已被证明有助于推进各种研究领域。通过利用最先进的深度学习技术，我们将开发能够自主运行并支持极高数据速率的NOMA系统。具体来说，使用深度学习，我们的目标是解决NOMA的四大挑战：错误缓解，用户分组，预编码和自适应控制。** 根据埃森哲的一份新报告，到2026年，新的5G无线系统每年将为加拿大经济贡献400亿美元。此外，在加拿大的无线行业，从2020年到2025年将创造超过15万个短期工作岗位，到2026年每年将创造额外的25万个永久性工作岗位。拟议的研究将使加拿大无线公司快速增长，并领导全球无线产业。最重要的是，拟议的研究是“故意”和“战略性”设计的，旨在同时在两个关键领域培训HQP：i）无线通信和ii）深度学习。 在拟议的研究计划中，4名硕士生和4名博士生将接受培训，学习未来无线系统的最新技术和深度学习的基础知识。