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

CIF：小型：低复杂性大规模 MIMO 系统：阵列几何、建模和学习的协同使用

• 批准号: 2124929
• 负责人: Bhaskar Rao
• 金额: $ 50万
• 依托单位: University of California-San Diego
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-07-01 至 2025-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2124929&HistoricalAwards=false
• 关键词: CIF; Small; Low; Complexity; Massive

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.

该项目应对下一代无线通信系统的挑战。用于可靠且高数据速率通信的关键启用技术是部署多个输入多重输出（MIMO）系统，该系统由多个用于传输和接收的天线组成。随着在下一代系统中使用毫米波（mmwave）频率，较短的波长使许多天线在一个小的物理区域中部署，从而导致大量的MIMO系统。但是，大量的MIMO系统往往具有很高的复杂性，高功耗和高成本。该项目试图以较少的方式做更多的事情：“少”是指有限的硬件（较少的无线电链，对数字转换器的一位模拟等等）和“更多”，以便能够通过围绕这些硬件限制来提取大量MIMO系统的好处（最少的降级）。为了更少的努力，该项目改编了一种协同方法，在该方法中，系统体系结构和算法（基于模型和数据驱动）的创新通过强大的推理框架（稀疏的贝叶斯学习和机器学习技术）提供了辅助的结构（天线阵列几何和建模），从而相互完成。该项目将导致最先进的无线通信系统，该系统应有助于维持美国在这项重要技术中的领导地位，以培训在这一战略重要性领域的下一代研究人员。要开发低复杂性，低成本，低成本，下一代MMWave Massive Mimo Systems，该项目具有两个主要组成部分。一种是用于对一维和二维阵列的利用天线阵列几何形状，用于由稳健推理编制的丰富通道传感器。这项工作的一个关键方面是将嵌套的阵列嵌入使用更少的射频链中的巨大的MIMO架构中。嵌套阵列的丰富灵敏度能力正在使用稀疏的贝叶斯学习方法最大程度地探索。通过增强的通道模型转换变量和未知的角扩展，通道传感也可以完成。另一个组成部分是通过深层神经网络学习的使用来补偿引入的非线性以降低功率和成本。正在开发通过学习以及完全数据驱动的技术完成的模型，以解决无线系统的特定和独特需求，例如可变数量的用户和渠道连贯性。该奖项反映了NSF的法定任务，并被认为是通过基金会的知识分子优点和更广泛影响的审查标准来评估通过评估来获得的支持。

项目成果

Super-Resolution With Binary Priors: Theory and Algorithms

二元先验的超分辨率：理论和算法

• DOI: 10.1109/tsp.2023.3260564
• 发表时间: 2023
• 期刊: IEEE transactions on signal processing
• 影响因子: 5.4
• 作者: Sarangi, Pulak;Hattori, Ryoma;Komiyama, Takaki;Pal, Piya
• 通讯作者: Pal, Piya

ResNEsts and DenseNEsts: Block-based DNN Models with Improved Representation Guarantees.

ResNEsts 和 DenseNEsts：具有改进表示保证的基于块的 DNN 模型。

• 发表时间: 2021
• 期刊: Advances in neural information processing systems
• 作者: Chen,Kuan-Lin;Lee,Ching-Hua;Garudadri,Harinath;Rao,BhaskarD
• 通讯作者: Rao,BhaskarD

Is Vector Quantization Good Enough for Access Point Placement?

矢量量化对于接入点放置是否足够好？

• DOI: 10.1109/ieeeconf53345.2021.9723121
• 发表时间: 2021
• 期刊: and Computers
• 作者: Gopal, Govind R.;Villardi, Gabriel Porto;Rao, Bhaskar D.
• 通讯作者: Rao, Bhaskar D.

Measurement Matrix Design for Sample-Efficient Binary Compressed Sensing

• DOI: 10.1109/lsp.2022.3179230
• 发表时间: 2022-01-01
• 期刊: IEEE SIGNAL PROCESSING LETTERS
• 影响因子: 3.9
• 作者: Sarangi, Pulak;Pal, Piya
• 通讯作者: Pal, Piya

Maximum Likelihood-Based Gridless DoA Estimation Using Structured Covariance Matrix Recovery and SBL With Grid Refinement

• DOI: 10.1109/tsp.2023.3254919
• 发表时间: 2023-01-01
• 期刊: IEEE TRANSACTIONS ON SIGNAL PROCESSING
• 影响因子: 5.4
• 作者: Pote, Rohan R.;Rao, Bhaskar D.
• 通讯作者: Rao, Bhaskar D.