CAREER: Harnessing Interference with Deep Learning: Algorithms and Large-Scale Experiments

职业：利用深度学习的干扰：算法和大规模实验

• 批准号: 2239524
• 负责人: Mojtaba Vaezi
• 金额: $ 59.95万
• 依托单位: Villanova University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-10-01 至 2028-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2239524&HistoricalAwards=false
• 关键词: CAREER; Harnessing; Interference; Deep; Learning

The increasing density of cellular networks and the adoption of drones have made interference a significant obstacle to achieving efficient and high-quality communication. Existing solutions for inter-cell interference are impractical due to excessive signaling overhead and synchronization burden. This project leverages deep learning to manage interference from neighboring cells without requiring channel information for interference signals. The proposed algorithms will rely on measured signal and interference power or quality, which are available in modern cellular networks, particularly in 5G. The goal is to improve the spectral efficiency of cellular networks, enable widespread drone adoption, and simplify the design of communication systems. The project also includes outreach efforts to educate underrepresented high school students in Philadelphia. Undergraduate students will have opportunities for interdisciplinary research in wireless communications and artificial intelligence, and a new senior elective course on deep learning for communications will be designed. The findings of this research will be disseminated through high-impact journals, conferences, and workshops, benefiting both academic and industrial communities. This project will pursue a new approach for interference mitigation in two-dimensional and three-dimensional cellular networks. The proposed deep learning-aided algorithms could transform the way real-life cellular networks are designed and operated in two ways: (i) by creating new foundation for interference management that work even when channel information is unknown and the network topology dynamically changes; and (ii) designing end-to-end communication models that can adapt to interference, simplify the current blockby-block design, and yield significantly higher data rates. The proposed algorithms will undergo extensive testing on both small and large scales, and the results will be evaluated on the NSF PAWR platforms that could lead to real-world implementation. This project has the potential to significantly increase the efficiency of current and next-generation cellular networks and enable the widespread use of drones in cellular networks.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

蜂窝网络密度的不断增加和无人机的采用使得干扰成为实现高效、高质量通信的重大障碍。由于过多的信令开销和同步负担，现有的小区间干扰解决方案是不切实际的。该项目利用深度学习来管理来自相邻小区的干扰，而不需要干扰信号的信道信息。所提出的算法将依赖于现代蜂窝网络（尤其是 5G）中可用的测量信号和干扰功率或质量。目标是提高蜂窝网络的频谱效率，实现无人机的广泛采用，并简化通信系统的设计。该项目还包括教育费城代表性不足的高中生的外展工作。本科生将有机会进行无线通信和人工智能的跨学科研究，并将设计一门新的通信深度学习高级选修课程。这项研究的结果将通过高影响力的期刊、会议和研讨会进行传播，使学术界和工业界受益。该项目将寻求一种在二维和三维蜂窝网络中减轻干扰的新方法。所提出的深度学习辅助算法可以通过两种方式改变现实生活中蜂窝网络的设计和操作方式：（i）为干扰管理创建新的基础，即使在信道信息未知且网络拓扑动态变化时也能发挥作用； (ii) 设计能够适应干扰、简化当前逐块设计并产生显着更高数据速率的端到端通信模型。所提出的算法将在小规模和大规模上进行广泛的测试，结果将在 NSF PAWR 平台上进行评估，这可能会导致实际实施。该项目有潜力显着提高当前和下一代蜂窝网络的效率，并使无人机在蜂窝网络中得到广泛使用。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优势和技术进行评估，被认为值得支持。更广泛的影响审查标准。