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关键技术是部署多输入多输出（MIMO）系统，该系统由多个用于传输和接收的天线组成。在下一代系统中使用毫米波（mmWave）频率，较短的波长可以在较小的物理区域内部署许多天线，从而导致大规模 MIMO 系统，但往往具有高复杂性、高可靠性。功耗和高该项目力求用更少的成本做更多的事情：“更少”是指有限的硬件（更少的射频链、一位模数转换器等），而“更多”是指能够获得收益（以最小的成本）。为了用更少的资源做更多的事情，该项目采用了一种协同方法，其中系统架构和算法（基于模型和数据驱动）的创新通过明智地利用结构（基于模型和数据驱动）相互补充。天线阵列几何形状和该项目将产生最先进的无线通信系统，这将有助于保持美国在这一重要技术方面的领先地位，并培训下一代。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：一是利用天线阵列几何形状，两者均适用于一维。和二维数组，对于这项工作的一个关键方面是使用更少的射频链将嵌套阵列嵌入到大规模 MIMO 架构中，并使用稀疏贝叶斯最大程度地利用嵌套阵列的丰富传感能力。通道感测还辅以包含可变和未知角度扩展的增强通道模型，另一个组成部分是通过深度神经网络进行学习来补偿引入的非线性，以降低补充模型的功耗和成本。通过学习以及完全数据驱动的技术正在开发，以满足无线系统的特定和独特需求，例如可变数量的用户和信道一致性。该奖项反映了 NSF 的法定使命，并通过使用评估被认为值得支持基金会的智力价值和更广泛的影响审查标准。

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

Modified Vector Quantization for Small-Cell Access Point Placement with Inter-Cell Interference

• DOI: 10.1109/twc.2022.3148996
• 发表时间: 2020-11
• 期刊: IEEE Transactions on Wireless Communications
• 影响因子: 10.4
• 作者: G. R. Gopal;Elina Nayebi;G. Villardi;B. Rao