This shows you the differences between two versions of the page.
Both sides previous revision Previous revision Next revision | Previous revision Next revision Both sides next revision | ||
start [2022/03/05 10:52] lingjialiu |
start [2022/03/16 17:44] lingjialiu |
||
---|---|---|---|
Line 53: | Line 53: | ||
**Available positions**: research assistant positions are available for self-motivated Ph.D. students who are interested in conducting research on the topic of signal processing, machine learning and networking for **<fc #ff0000>Fall 2022</fc>** (**[[Openings|click for details]]**).\\ | **Available positions**: research assistant positions are available for self-motivated Ph.D. students who are interested in conducting research on the topic of signal processing, machine learning and networking for **<fc #ff0000>Fall 2022</fc>** (**[[Openings|click for details]]**).\\ | ||
\\ | \\ | ||
- | **<fc #ff0000>[Grant]</fc>** __March 3, 2022__: "Capturing Unknown Transmissions & Perceiving Anomalous Signal Transmissions and Emanations" has been awarded by the Intelligence Advanced Research Projects Activity (IARPA). This is a $14 million collaborative project led by BAE Systems. Dr. Lingjia Liu is serving as the **PI** of the sub-award for Virginia Tech and Virginia Tech's share is $1,478,540.\\ | ||
\\ | \\ | ||
- | **<fc #6495ed>[Paper Acceptance]</fc>** __February 14, 2022__: Our paper, //RC-Struct: A Structure-based Neural Network Approach for MIMO-OFDM Detection//, has been accepted to //IEEE Trans. Wireless Commun.// (**TWireless**) Congratulations Jiarui and Lianjun!\\ | + | **<fc #6495ed>[Paper Acceptance]</fc>** __March 16, 2022__: Our paper, //Harnessing Tensor Structures – Multi-Mode Reservoir Computing and Its Application in Massive MIMO//, has been accepted to //IEEE Trans. Wireless Commun.// (**TWireless**) Congratulations Zhou and Jiarui! \\ |
+ | \\ | ||
+ | **<fc #6495ed>[Paper Acceptance]</fc>** __March 16, 2022__: Our paper, //Angle-based Downlink Beam Selection and User Scheduling for Massive MIMO Systems//, has been accepted to //IEEE Trans. Wireless Commun.// (**TWireless**) Congratulations Nan and Rubayet! The majority of the work is done when Nan is working as a visiting PhD student in our group.\\ | ||
+ | \\ | ||
+ | **<fc #ff0000>[Grant]</fc>** __March 3, 2022__: "Capturing Unknown Transmissions & Perceiving Anomalous Signal Transmissions and Emanations" has been awarded by the Intelligence Advanced Research Projects Activity (IARPA). This is a **$14 million** collaborative project led by BAE Systems. Dr. Lingjia Liu is serving as the **PI** of the sub-award for Virginia Tech and Virginia Tech's share is **$1,478,540**.\\ | ||
+ | \\ | ||
+ | **<fc #6495ed>[Paper Acceptance]</fc>** __March 1, 2022__: Our paper, //Learning to Equalize OTFS//, has been accepted to //IEEE Trans. Wireless Commun.// (**TWireless**) Congratulations Zhou and Jiarui! This is a collaborative work with Prof. [[https://ece.duke.edu/faculty/robert-calderbank|Robert Calderbank]] from ECE/CS@Duke.\\ | ||
+ | \\ | ||
+ | **<fc #6495ed>[Paper Acceptance]</fc>** __February 14, 2022__: Our paper, //RC-Struct: A Structure-based Neural Network Approach for MIMO-OFDM Detection//, has been accepted to //IEEE Trans. Wireless Commun.// (**TWireless**) Congratulations Jiarui and Lianjun! This is a collaborative work with Prof. [[https://lizhongzheng.mit.edu/|Lizhong Zheng]] from EECS@MIT.\\ | ||
\\ | \\ | ||
**<fc #6495ed>[Paper Acceptance]</fc>** __January 20, 2022__: 3 of our papers have been accepted to //IEEE Intl Conf on Commun.// (**ICC**) 2022. The topics are ranging from //error bounds characterization for reservoir computing// to //policy-based spiking neural networks for multi-agent reinforcement learning//. Congratulations Shashank, Lianjun, and Nima!\\ | **<fc #6495ed>[Paper Acceptance]</fc>** __January 20, 2022__: 3 of our papers have been accepted to //IEEE Intl Conf on Commun.// (**ICC**) 2022. The topics are ranging from //error bounds characterization for reservoir computing// to //policy-based spiking neural networks for multi-agent reinforcement learning//. Congratulations Shashank, Lianjun, and Nima!\\ |