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Chao Jia

Publications
4
Citations
82
Publications by Year
20212024
Co-Authors:
Show All Co-Authors (12)
Image of author Chao Jia

Chao Jia

Affiliation

State Key Laboratory of Networking and Switching Technology
and the School of Information and Communication Engineering
Beijing University of Posts and Telecommunications, Beijing, China

Publication Topics

Biography

Chao Jia (Student Member, IEEE) received the B.S. degree from the School of Computer Science and Engineering, Northeastern University, Shenyang, China. He is currently pursuing the M.S. degree in communication and information systems with Beijing University of Posts and Telecommunications. His current research interests include massive MIMO and machine learning.(Based on document published on 3 September 2021).
Publications
4
Citations
82
Publications by Year
20212024
Co-Authors:
Show All Co-Authors (12)
Author's Published Works

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An Interference Cancelation Scheme of Integrated Sensing and Communications in Wireless Networks

Chao Jia;Zhongyuan Zhao;Like Sun;Tony Q. S. Quek

IEEE Wireless Communications Letters
Year: 2024 | Volume: 13, Issue: 12 | Journal Article |
In this letter, an application of interference cancelation scheme into ISAC is investigated. First, an interference cancelation scheme of ISAC (IC-ISAC) is proposed, where the communication and sensing tasks are simultaneously accomplished by mitigating the mutual interference, thereby significantly improving the spectrum efficiency. Second, the closed-form expressions of the outage probability fo...Show More

An Interference Cancelation Scheme of Integrated Sensing and Communications in Wireless Networks

Chao Jia;Zhongyuan Zhao;Like Sun;Tony Q. S. Quek

Year: 2024 | Volume: 13, Issue: 12 | Journal Article |

Opportunistic Cooperation of Integrated Sensing and Communications in Wireless Networks

Chao Jia;Zhongyuan Zhao;Like Sun;Zhiguo Ding;Tony Q. S. Quek

IEEE Wireless Communications Letters
Year: 2024 | Volume: 13, Issue: 6 | Journal Article |
Cited by: Papers (1)
The use of integrated sensing and communications (ISAC) can improve the performance of both sensing and communication functionalities of wireless networks by flexibly sharing radio resources. However, its performance cannot be always guaranteed due to the dynamic interference circumstances. Therefore, it requires a sophisticated design of cooperation strategies to balance the tradeoff. To provide ...Show More

Opportunistic Cooperation of Integrated Sensing and Communications in Wireless Networks

Chao Jia;Zhongyuan Zhao;Like Sun;Zhiguo Ding;Tony Q. S. Quek

Year: 2024 | Volume: 13, Issue: 6 | Journal Article |

Federated Learning With Non-IID Data in Wireless Networks

Zhongyuan Zhao;Chenyuan Feng;Wei Hong;Jiamo Jiang;Chao Jia;Tony Q. S. Quek;Mugen Peng

IEEE Transactions on Wireless Communications
Year: 2022 | Volume: 21, Issue: 3 | Journal Article |
Cited by: Papers (79)
Federated learning provides a promising paradigm to enable network edge intelligence in the future sixth generation (6G) systems. However, due to the high dynamics of wireless circumstances and user behavior, the collected training data is non-independent and identically distributed (non-IID), which causes severe performance degradation of federated learning. To solve this problem, federated learn...Show More

Federated Learning With Non-IID Data in Wireless Networks

Zhongyuan Zhao;Chenyuan Feng;Wei Hong;Jiamo Jiang;Chao Jia;Tony Q. S. Quek;Mugen Peng

Year: 2022 | Volume: 21, Issue: 3 | Journal Article |

Deep Reinforcement Learning-Based Multi-Panel Beam Management in Massive MIMO Systems: Algorithm Design and System-Level Simulation

Yang Li;Jiamo Jiang;Chao Jia;Yifei Yuan;Zhongyuan Zhao;Ying Du;Zhiqin Wang

2021 IEEE 32nd Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)
Year: 2021 | Conference Paper |
Cited by: Papers (2)
To adapt to the complicated interference and the high dynamics of wireless circumstances, deep reinforcement learning (DRL) has been considered as a potential solution for beam management in the massive multiple-input and multiple-output (MIMO) systems. However, due to the extremely high dimensions of both action and state spaces, the existing DRL-based schemes are with high computation costs, and...Show More

Deep Reinforcement Learning-Based Multi-Panel Beam Management in Massive MIMO Systems: Algorithm Design and System-Level Simulation

Yang Li;Jiamo Jiang;Chao Jia;Yifei Yuan;Zhongyuan Zhao;Ying Du;Zhiqin Wang

Year: 2021 | Conference Paper |

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