Advanced Search Results For "Tao, Meixia"

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Publication Type: Periodical

Source(s): IEEE Transactions on Wireless Communications; 2023, Vol. 22 Issue: 3 p2171-2184, 14p

Abstract: Reconfigurable intelligent surface (RIS) has attracted extensive attention in recent years. However, most research focuses on the scenario of the narrowband and/or instantaneous channel state information (CSI), while wide bandwidth with the use of mill...

Publication Type: Periodical

Source(s): IEEE Journal of Selected Topics in Signal Processing; January 2023, Vol. 17 Issue: 1 p82-97, 16p

Abstract: Federated learning (FL) is a promising distributed learning paradigm in which multiple edge devices (EDs) collaborate to train a shared model without exchanging privacy-sensitive raw data. With the heterogeneous architectures of local models in FL, kno...

Publication Type: Periodical

Source(s): IEEE Transactions on Wireless Communications; January 2023, Vol. 22 Issue: 1 p688-703, 16p

Abstract: With the booming development of Internet of things (IoT) devices and machine learning (ML) technique, edge machine learning is emerging to process the enormous sampled data for realizing intelligent applications at the network edge. With limited edge r...

Publication Type: Periodical

Source(s): IEEE Communications Letters; January 2023, Vol. 27 Issue: 1 p160-164, 5p

Abstract: This letter introduces a new coded transmission design for model aggregation in federated learning (FL) over Gaussian multiple access channels (MAC), named coded over-the-air computation (codedAirComp). It enjoys the optimality of analog AirComp-based ...

Publication Type: Periodical

Source(s): IEEE Transactions on Communications; January 2023, Vol. 71 Issue: 1 p173-186, 14p

Abstract: One of the main focuses in distributed learning is communication efficiency, since model aggregation at each round of training can consist of millions to billions of parameters. Several model compression methods, such as gradient quantization and spars...

Publication Type: Periodical

Source(s): IEEE Journal on Selected Areas in Communications; January 2023, Vol. 41 Issue: 1 p170-185, 16p

Abstract: Existing deep learning-enabled semantic communication systems often rely on shared background knowledge between the transmitter and receiver that includes empirical data and their associated semantic information. In practice, the semantic information i...

Publication Type: Academic Journal

Source(s): Sensors (14248220); Jul2022, Vol. 22 Issue 14, pN.PAG-N.PAG, 22p

Abstract: Copyright of Sensors (14248220) is the property of MDPI and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email artic...

Publication Type: Academic Journal

Source(s): IEEE Transactions on Communications; Jul2022, Vol. 70 Issue 7, p4498-4512, 15p

Abstract: Copyright of IEEE Transactions on Communications is the property of IEEE and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, downloa...

Publication Type: Periodical

Source(s): IEEE Wireless Communications Letters; September 2022, Vol. 11 Issue: 9 p1860-1864, 5p

Abstract: This letter considers temporal-correlated massive access, where each device, once activated, is likely to transmit continuously over several consecutive frames. Motivated by that the device activity at each frame is correlated to not only its previous ...

Publication Type: Periodical

Source(s): China Communications Magazine; August 2022, Vol. 19 Issue: 8 p41-56, 16p

Abstract: As a promising edge learning framework in future 6G networks, federated learning (FL) faces a number of technical challenges due to the heterogeneous network environment and diversified user behaviors. Data imbalance is one of these challenges that can...