Massive MIMO for Underwater Acoustic WiFi Networks

水下无线通信仍然是推动海洋技术进步和海洋可持续发展的关键瓶颈。现有的水声通信网络存在着信道条件恶劣、传播时延大、带宽低、效率低等问题。本课题充分利用空间资源，采用大规模的MIMO(多输入多输出)方案作为水声WiFi网络的接入点。提出的研究目标是通过联合MAC-PHY层优化来大幅提高网络吞吐量和带宽效率。拟议的研究将在几个方面有利于海洋技术和蓝色经济：利用水下WiFi将大幅减少海洋中的声音污染，因为水声WiFi网络促进了低功率近距离通信并减少了高功率远程传输；成功的水下WiFi网络试验台将可能促进水下机器人和水下物联网(IOUT)，这反过来可以增强海洋传感和大数据收集的能力；拟议的研究有可能转移到现场，并可能在实现物联网方面发挥关键作用。该教育计划包括开发评估工具来衡量学生学习的好奇心和创造力，吸引本科生参与研究，并在课堂教学中纳入竞争。该项目明确地将移动节点和大规模MIMO AP之间的大传播延迟分解到总吞吐量效用函数中，并使用深度Q-学习神经网络(DQN)来学习动态声学环境中的最优空时预编码策略。拟议的研究分为两个相互交织的任务。第一个任务是吞吐量优化和协议设计，它将大规模的MIMO预编码器和上下行链路的MAC时间分配问题转化为一个同时考虑统计信道状态信息和传播时延分布的随机动态规划(SDP)问题。采用两层分解和DQN网络相结合的方法来克服维度灾变和求解SDP问题。将最优解转化为WiFi AP发射机和接收机的实际信令设计。第二个任务是硬件实现和试验台设计，其中包括一个具有36个换能器的大规模MIMO阵列的AP和一组每个具有4-8个换能器的移动节点。综合试验台将用于进行现场试验，并为DQN培训收集数据。协议设计和资源分配的创新方法很可能导致水声通信和网络的突破。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.