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Collaborative Research:Expedite CSI Processing with Lightweight AI in Massive MIMO Communication Systems

项目来源

美国国家科学基金(NSF)

项目主持人

Rose Qingyang Hu

项目受资助机构

Virginia Polytechnic Institute and State University

项目编号

2507713

财政年度

2025,2021

立项时间

未公开

项目级别

国家级

研究期限

未知 / 未知

受资助金额

263420.00美元

学科

未公开

学科代码

未公开

基金类别

Standard Grant

关键词

CCSS-Comms Circuits&Sens Sys ; REU SUPP-Res Exp for Ugrd Supp

参与者

未公开

参与机构

VIRGINIA POLYTECHNIC INSTITUTE&STATE UNIVERSITY

项目标书摘要：Next generation wireless communications will need to support heterogeneous devices with different capabilities on communications,computations,and power to deliver applications with various performance demands such as high data rate,low power consumption,and low latency.Massive multiple-input multiple output(MIMO)has been widely considered a compelling technology for achieving high capacity and high spectrum efficiency in the future wireless communication networks.To fully unleash the potential performance gains claimed by massive MIMO communication systems,it is of vital importance to have timely and accurate channel state information(CSI)at the transmitters,especially at the base station side.The main goal of this project is to explore a systematic approach that accelerates the CSI processing by orders of magnitude in massive MIMO communication systems.The project will lay a foundation to enhancing data rate and energy efficiency,spectral efficiency in the next-generation wireless communications.The research efforts associated with the project can have a significant impact on the lightweight artificial intelligence(AI)design for wireless communication systems,which will further improve many application domains,including beyond 5G wireless networks,autonomous machine-to-machine communications,vehicular networks,and Internet-of-Things.The outcomes of the project can foster the transition of our society into the intelligent wireless networking age,where wireless communication systems can provide seamless support to match many different wireless applications for massive network devices and support many services with high computation demands and quality of service needs.Moreover,the Principal Investigators are committed to integrating research and education by introducing emerging computing and lightweight AI in wireless communication systems into the current electrical and computer engineering curricula in the three participating universities.The project will also provide opportunities for students to learn,develop and apply advanced wireless communications,which they would not receive from a traditional B.S.or M.S.curriculum.Meeting the coherence time requirement in massive MIMO systems can be extremely difficult for CSI processing due to the complex traditional model as well as AI model development and inconsistent performance across environments.In this research project,theoretical analysis and performance evaluations will be obtained for novel algorithms designed for 1)optimization on the decompressed feature in the CSI reconstruction process,2)simplifying the AI structures for multi-rate compression and reconstruction,and 3)autonomous CSI reconstruction performance evaluation and AI model update.The optimized features and simplified AI structures can significantly reduce the complexity in terms of floating point operations per second(FLOPs).Thus,the AI implementation can be accelerated by 1 to 2 orders of magnitude without losing reconstruction accuracy for timely CSI processing in massive MIMO communication systems.The systematic methodologies can be readily extended to facilitate many other applications that encounter the similar challenges and present similar needs on reducing latency and computation needs.Furthermore,this research project can greatly promote the understanding in AI-supported massive MIMO systems for better spectrum and power efficiency and will contribute fundamentally to the design of highly efficient machine-to-machine communications that require high level of autonomy.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.

人员信息

Rose Qingyang Hu(Principal Investigator)：rosehu@vt.edu；

机构信息

【Virginia Polytechnic Institute and State University(Performance Institution)】StreetAddress：300 TURNER ST NW,BLACKSBURG,Virginia,United States/ZipCode：240603359；【VIRGINIA POLYTECHNIC INSTITUTE&STATE UNIVERSITY】StreetAddress：300 TURNER ST NW,BLACKSBURG,Virginia,United States/PhoneNumber：5402315281/ZipCode：240603359；

项目主管部门

Directorate for Engineering(ENG)-Division of Electrical,Communications and Cyber Systems(ECCS)

项目官员

Huaiyu Dai(Email：hdai@nsf.gov；Phone：7032924568)

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  • 1.Joint Power and Spectrum Orchestration for D2D Semantic Communication Underlying Energy-Efficient Cellular Networks

    • 关键词：
    • Semantics; Energy efficiency; Device-to-device communication; Resourcemanagement; Wireless communication; Cellular networks; Power demand;Optimization; Training; Electronic mail; Device-to-device semanticcommunication; energy efficiency; power allocation; spectrum reuse;RESOURCE-ALLOCATION; ARCHITECTURE; INTERNET; SYSTEMS
    • Xia, Le;Sun, Yao;Sun, Haijian;Hu, Rose Qingyang;Niyato, Dusit;Imran, Muhammad Ali
    • 《IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS》
    • 2026年
    • 25卷
    • 期刊

    Semantic communication (SemCom) has been recently deemed a promising next-generation wireless technique to enable efficient spectrum savings and information exchanges, thus naturally introducing a novel and practical network paradigm where cellular and device-to-device (D2D) SemCom approaches coexist. Nevertheless, the involved wireless resource management becomes complicated and challenging due to the unique semantic performance measurements and energy-consuming semantic coding mechanism. To this end, this paper jointly investigates power control and spectrum reuse problems for energy-efficient D2D SemCom cellular networks. Concretely, we first model the user preference-aware semantic triplet transmission and leverage a novel metric of semantic value to identify the semantic information importance conveyed in SemCom. Then, we define the additional power consumption from semantic encoding in conjunction with basic power amplifier dissipation to derive the overall system energy efficiency (semantic-value/Joule). Next, we formulate an energy efficiency maximization problem for joint power and spectrum allocation subject to several SemCom-related and practical constraints. Afterward, we propose an optimal resource management solution by employing the fractional-to-subtractive problem transformation and decomposition while developing a three-stage method with theoretical analysis of its optimality guarantee and computational complexity. Numerical results demonstrate the adequate performance superiority of our proposed solution compared with different benchmarks.

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  • 2.Approximate Wireless Communication for Lossy Gradient Updates in IoT Federated Learning

    • 关键词：
    • Adversarial machine learning;Random errors;Approximate communication;Forward error correction;Forward error-correction;Gradient model update;Gradient modelling;Learning parameters;Lossy wireless communication;Model updates;Random bit errors;Wireless communications
    • Ma, Xiang;Sun, Haijian;Hu, Rose Qingyang;Qian, Yi
    • 《IEEE Internet of Things Journal》
    • 2024年
    • 期刊

    Federated learning (FL) has emerged as a distributed machine learning (ML) technique that can protect local data privacy for participating clients and improve system efficiency. Instead of sharing raw data, FL exchanges intermediate learning parameters, such as gradients, among clients. This article presents an efficient wireless communication approach tailored for FL parameter transmission, especially for Internet of Things (IoT) devices, to facilitate model aggregation. Our study considers practical wireless channels that can lead to random bit errors, substantially affecting FL performance. Motivated by empirical gradient value distribution, we introduce a novel received bit masking method that confines received gradient values within prescribed limits. Moreover, given the intrinsic error resilience of ML gradients, our approach enables the delivery of approximate gradient values with errors without resorting to extensive error correction coding or retransmission. This strategy reduces computational overhead at both the transmitter and the receiver and minimizes communication latency. Consequently, our scheme is particularly well-suited for resource-constrained IoT devices. Our simulations demonstrate that our proposed scheme can effectively mitigate random bit errors in FL performance, achieving similar learning objectives but with the 50% air time required by existing methods involving error correction and retransmission. © 2014 IEEE.

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  • 3.Spatial Channel State Information Prediction with Generative AI: Towards Holographic Communication and Digital Radio Twin

    • 关键词：
    • 5G mobile communication systems;Beam forming networks;Channel state information;Communication channels (information theory);Digital radio;Forecasting;Holography;Interactive computer systems;Radio;6g mobile communication;Array signal processing;Channel-state information;Digital radio twin;Generative artificial intelligence;Holographic communication;Mobile communications;Neural radio tracing;Precoding;Real;Time system ;Spatial channel state information;Spatial channels;Stream;Wireless communications
    • Zhang, Lihao;Sun, Haijian;Zeng, Yong;Hu, Rose Qingyang
    • 《IEEE Network》
    • 2024年
    • 期刊

    As the deployment of 5G technology matures, the anticipation for 6G is growing, which promises to deliver faster and more reliable wireless connections via cutting-edge radio technologies. A pivot to these radio technologies is the effective management of large-scale antenna arrays, which aims to construct valid spatial streams to maximize system throughput. Traditional management methods predominantly rely on user feedback to adapt to dynamic wireless channels. However, a more promising approach lies in the prediction of spatial channel state information (spatial-CSI), which is a channel characterization that consists of all robust line-of-sight (LoS) and non-line-of-sight (NLoS) paths between the transmitter (Tx) and receiver (Rx), with three-dimensional (3D) trajectory, attenuation, phase shift, delay, and polarization of each path. Recent advances in hardware and neural networks make it possible to predict such spatial-CSI using precise environmental information, and further explores the possibility of holographic communication, which implies complete control over every aspect of the radio waves. This paper presents a preliminary exploration of using generative artificial intelligence (AI) to accurately model the environment particularly for radio simulations and identify valid paths within it for real-time spatial-CSI prediction. Our validation project demonstrates promising results, highlighting the potential of this approach in driving forward the evolution of 6G wireless communication technologies. IEEE

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