Massive MIMO for Underwater Acoustic WiFi Networks

用于水下声学 WiFi 网络的大规模 MIMO

• 批准号: 2228539
• 负责人: Yahong Zheng
• 金额: $ 36万
• 依托单位: Lehigh University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-01-01 至 2025-12-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2228539&HistoricalAwards=false
• 关键词: Massive; MIMO; Underwater; Acoustic; WiFi

Underwater wireless communication remains a critical bottleneck to advancing ocean technologies and sustainable ocean development. The existing underwater acoustic communication networks suffer from low bandwidth and low efficiency due to harsh acoustic channel conditions and large propagation delays. This project takes advantage of space resource and utilizes massive MIMO (Multiple-Input-Multiple-Output) schemes for the Access Point (AP) of underwater acoustic WiFi networks. The objective of the proposed research is to drastically increase the network throughput and bandwidth efficiency by joint MAC-PHY layer optimization. The proposed research will benefit ocean technology and blue economy in several aspects: the utilization of underwater WiFi will drastically reduce sound pollution in the ocean as acoustic WiFi network facilitates low-power low-range communication and reduces high-power long-range transmission; a successful test bed for underwater WiFi networks will likely promote underwater robotics and Internet of Underwater Things (IoUT), which in turn can enhance the capability of ocean sensing and big data collection; the proposed research has the potential to be transferred to the field and will likely play a critical role in enabling IoUTs. The educational plan includes developing assessment tools for measuring curiosity and creativity in student learning, attracting undergraduate students into research, and incorporating competitions in classroom teaching.The project explicitly factorizes the large propagation delays between mobile nodes and the massive MIMO AP into the sum throughput utility function and employs a Deep Q-Learning Neural (DQN) Network to learn the optimal spatial-temporal precoding policy in the dynamic acoustic environment. The proposed research is divided into two intertwined tasks. The first task is the throughput optimization and protocol design, which formulates the massive MIMO precoder and MAC time allocation of the uplink and downlink links into a Stochastic Dynamic Programming (SDP) problem considering both statistical channel state information and propagation delay profile. A 2-layer decomposition and DQN network are used to combat the curse of dimensionality and solve the SDP. The optimal solutions are translated into practical signaling designs of the WiFi AP transmitter and receiver. The second task is the hardware implementation and testbed design, which includes an AP with a 36-transducer massive MIMO array and a set of mobile nodes each with 4-8 transducers. The integrated testbed will be used to conduct field experiments and to collect data for DQN training. The innovative approach to protocol design and resource allocation is likely to result in a breakthrough in underwater acoustic communication and networking.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.

水下无线通信仍然是推进海洋技术和可持续海洋发展的关键瓶颈。由于恶劣的声学通道条件和较大的传播延迟，现有的水下声学通信网络遭受了低带宽和低效率。该项目利用了空间资源，并利用了水下声学WiFi网络的接入点（AP）的大规模MIMO（多输入 - 数字输出）方案。拟议的研究的目的是通过关节Mac-Phy层优化大大提高网络吞吐量和带宽效率。拟议的研究将使海洋技术和蓝色经济在几个方面受益：随着声学WiFi网络促进低功率的低端沟通并减少高功率的长距离传播，水下WiFi的利用将大大降低海洋的声音污染；水下WiFi网络的成功测试床可能会促进水下机器人技术和水下物品（IOUT），从而增强了海洋传感和大数据收集的能力；拟议的研究有可能转移到该领域，并可能在实现IOUTS方面发挥关键作用。教育计划包括开发评估工具，以衡量学生学习中的好奇心和创造力，吸引学生参加研究，并将竞争纳入课堂教学中。该项目明确将大量传播延迟移动节点和大规模的mimo ap之间的大量传播延迟，并在总和吞吐量的功能中纳入深度的Q-enermity nenaltial nenality（DQN），以实现Q-neal（DQN）的态度（DQN），以实现“ dqn”（dqn）的态度。 环境。拟议的研究分为两个交织的任务。第一个任务是吞吐量优化和协议设计，该设计将上行链路和下行链路链接到随机动态编程（SDP）问题的大规模MIMO预编码器和MAC时间分配，考虑到统计通道状态信息和传播延迟延迟配置文件。 2层分解和DQN网络用于对抗维度的诅咒并解决SDP。最佳解决方案转化为WiFi AP发射器和接收器的实用信号设计。第二个任务是硬件实现和测试台设计，其中包括一个带有36透明的大型MIMO数组的AP和一组带有4-8个换能器的移动节点。集成测试床将用于进行现场实验并收集DQN培训的数据。协议设计和资源分配的创新方法可能会导致水下声学通信和网络的突破。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子和更广泛影响的评估评估的评估来支持的。