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An Interference Cancelation Scheme of Integrated Sensing and Communications in Wireless Networks
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
Opportunistic Cooperation of Integrated Sensing and Communications in Wireless Networks
Chao Jia;Zhongyuan Zhao;Like Sun;Zhiguo Ding;Tony Q. S. Quek
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
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
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
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
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