CIF: Small: Low Complexity Massive MIMO Systems: Synergistic use of Array Geometry, Modeling and Learning

该项目解决了下一代无线通信系统的挑战。实现可靠和高数据速率通信的一项关键技术是部署多输入多输出(MIMO)系统，该系统由用于发送和接收的多个天线组成。随着毫米波频率在下一代系统中的使用，较短的波长使得能够在较小的物理区域内部署许多天线，从而导致大规模的MIMO系统。然而，大规模MIMO系统往往具有高复杂度、高功耗和高成本的特点。这个项目寻求用更少的资源做更多的事情：“更少”指的是有限的硬件(更少的射频链、一位模数转换器等)。更多的是能够通过绕过这些硬件限制来获取大规模MIMO系统的好处(以最小的降级)。为了用更少的资源做更多的事情，该项目采用了一种协同办法，通过明智地利用结构(天线阵列几何形状和建模)，并辅之以强大的推理框架(稀疏贝叶斯学习和机器学习技术)，使系统架构和算法(基于模型和数据驱动)的创新相辅相成。该项目将带来最先进的无线通信系统，有助于保持美国在这一重要技术领域的领导地位，并培训这一战略重要领域的下一代研究人员。为了开发低复杂性、低成本的下一代毫米波大规模MIMO系统，该项目有两个主要组成部分。一种是利用天线阵列几何结构，用于一维和二维阵列，以更少的传感器进行丰富的信道感知，并辅之以稳健的推理。这项工作的一个关键方面是将嵌套阵列嵌入到使用较少射频链的大规模MIMO体系结构中。使用稀疏贝叶斯学习方法最大限度地利用了嵌套阵列的丰富传感能力。此外，还利用结合了可变和未知角度分布的增强型通道模型来补充通道感知。另一个组成部分是通过深度神经网络进行学习，以补偿为降低功率和成本而引入的非线性。正在开发的模型辅以学习以及完全数据驱动的技术，以满足无线系统的特定和独特需求，例如可变用户数量和通道一致性。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更广泛的影响审查标准进行评估，被认为值得支持。

The project addresses the challenges of next-generation wireless communication systems. A key enabling technology for reliable and high-data-rate communication is the deployment of Multiple Input Multiple Output (MIMO) systems, which consist of multiple antennas for transmission and reception. With the use of the millimeter-wave (mmWave) frequencies in next-generation systems, the shorter wavelength enables deployment of many antennas in a small physical area, leading to massive MIMO systems. Massive MIMO systems, however, tend to have high complexity, high power consumption and high cost. This project seeks to do more with less: “Less” refers to limited hardware (fewer radio-frequency chains, one-bit analog to digital converters, etc.) and “more” to being able to extract the benefits (with minimal degradation) of massive MIMO systems by working around these hardware limitations. To do more with less, the project adopts a synergistic approach where innovations in system architecture and algorithms (model-based and data-driven) complement each other via judicious exploitation of structure (antenna array geometry and modeling) aided by powerful inference frameworks (sparse Bayesian learning and machine-learning techniques). The project will lead to state-of-the-art wireless communication systems that should help with maintaining US leadership in this important technology as well to train the next generation of researchers in this area of strategic importance.To develop low-complexity, low-cost, next-generation mmWave massive MIMO systems, this project has two major components. One is to harness antenna array geometry, both for one-dimensional and two-dimensional arrays, for rich channel sensing with fewer sensors complemented by robust inference. A key aspect of this work is embedding a nested array into a massive MIMO architecture employing fewer radio-frequency chains. The rich sensing capability of the nested array is being maximally exploited using the sparse Bayesian learning method. The channel sensing is also complemented with enhanced channel models incorporating variable and unknown angular spreads. A further component is the use of learning through deep neural networks to compensate for nonlinearities introduced to reduce power and cost. Models complemented by learning as well as fully data-driven techniques are being developed that address the specific and unique needs of wireless systems, such as variable numbers of users and channel coherence.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.