CIF: Small: Massive MIMO for Massive Machine-Type Communication
CIF: Small: Massive MIMO for Massive Machine-Type Communication
批准号:
1814803
负责人:
Xiaodong Wang
金额:
$49.99万
依托单位:
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2018
资助国家:
美国
项目状态:
已结题
起止时间:
2018-10-01 至 2022-09-30
中文摘要
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英文摘要
The objective of this project is to investigate massive multiple-input multiple-output (MIMO) coding and signal processing techniques for massive machine-type communications, which is an important scenario in the emerging 5G wireless systems. Key features of the massive machine-type communication system include the massive device connectivity and the sporadic device activity pattern. The massive MIMO technique is well suited for accommodating a large number of devices and maintaining a constant level of energy efficiency that is independent of the number of devices. In addition to the potential technical impacts, the investigators plan to incorporate the research into graduate and undergraduate curricula and to also develop summer projects to facilitate K-12 outreach.This project considers the uplink of a massive machine-type communication system consisting of a base station equipped with a large number of antennas, as well as a large number of potential device nodes, out of which a fraction are active at any given time. On the transmitter side, to address the shortage of orthogonal pilot sequences when the number of devices is large, multi-base codes will be designed that contain a large number of orthogonal subsets of vectors. To address the high-complexity of device activity detection when the number of nodes is large, pilot sequences based on various codes and Grassmannian modulation constellations will be designed that can be decoded efficiently with complexity independent of the number of nodes. On the receiver side, joint device activity detection and channel estimation will be developed by exploiting the sparsity in both device activity and the channel structures. Moreover, when the base station employs a smaller number of radio frequency chains compared with the antenna size, matrix or tensor completion techniques will be developed for channel estimation or data decoding in the presence of missing data, by exploiting the low-rank property of the received signals.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.
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DOI:
10.1109/tit.2021.3049172
发表时间:
2019-03
期刊:
IEEE Transactions on Information Theory
影响因子:
2.5
作者:
[Junjie Ma;Rishabh Dudeja;Ji Xu;A. Maleki;Xiaodong Wang]
通讯作者:
Junjie Ma;Rishabh Dudeja;Ji Xu;A. Maleki;Xiaodong Wang
Unsourced Multiple Access Based on Sparse Tanner Graph—Efficient Decoding, Analysis, and Optimization
基于稀疏Tanner图的无源多址高效解码、分析和优化
DOI:
10.1109/jsac.2022.3143225
发表时间:
2022
期刊:
IEEE Journal on Selected Areas in Communications
影响因子:
16.4
作者:
[Liu, Jiaai, Wang, Xiaodong]
通讯作者:
Wang, Xiaodong
Sparsity-Exploiting Blind Receiver Algorithms for Unsourced Multiple Access in MIMO and Massive MIMO Channels
MIMO 和大规模 MIMO 信道中无源多址的稀疏性利用盲接收器算法
DOI:
10.1109/tcomm.2021.3116239
发表时间:
2021
期刊:
IEEE Transactions on Communications
影响因子:
8.3
作者:
[Liu, Jiaai, Wang, Xiaodong]
通讯作者:
Wang, Xiaodong
DOI:
10.1109/jstsp.2022.3232944
发表时间:
2022-06
期刊:
IEEE Journal of Selected Topics in Signal Processing
影响因子:
7.5
作者:
[Ye Hu;Xiaodong Wang;W. Saad]
通讯作者:
Ye Hu;Xiaodong Wang;W. Saad
DOI:
10.1109/jiot.2020.3003646
发表时间:
2020-04
期刊:
IEEE Internet of Things Journal
影响因子:
10.6
作者:
[Xinhua Wang;Xiaodong Wang;A. Ashikhmin]
通讯作者:
Xinhua Wang;Xiaodong Wang;A. Ashikhmin
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