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刘思聪
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副教授
硕士生导师
- 电子邮箱：88e988cd03deaf2e47b71f376a29dca8151cd59f61ea409b0c814b61b6b2e66d786aea89b928a409d4baa7c92ea28b03bdebe67e00b5869aa93a7c63b107e4d4cf9fd445ce134624bcb2f0ba1edc99e184ccda6601f7fa2f508a5da3126ea181afa46025052131b8a2c4961228666a46ac35b4e1de3e525ea3bf92ec1de888fc
- 所在单位：信息学院
- 学历：博士研究生毕业
- 办公地点：厦门大学信息学院 信息与通信工程系
- 性别：男
- 联系方式：详细信息请见个人主页：https://liusc1028.github.io/academicWeb
- 学位：工学博士学位
- 在职信息：在职
- 毕业院校：清华大学
访问量：
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[1]刘思聪,Sparsity-Aware Channel Estimation for Underwater Acoustic Wireless Networks: A Generative Adversarial Network Enabled Approach.20th International Wireless Communications and Mobile Computing Conference, IWCMC 2024,1171-1176.
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[2]刘思聪,Block-Sparse Learning Enabled Approach Towards Efficient Channel Estimation for Underwater Visible Light Communications.20th International Wireless Communications and Mobile Computing Conference, IWCMC 2024,1166-1170.
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[3]洪学敏,刘思聪.Classification-Driven Discrete Neural Representation Learning for Semantic Communications.IEEE INTERNET OF THINGS JOURNAL,2024,11(9):16061-16073.
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[4]刘思聪.Integrated sensing, lighting and communication based on visible light communication: A review.DIGITAL SIGNAL PROCESSING,2023,145
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[5]刘思聪.Multi-Target Cooperative Visible Light Positioning: A Compressed Sensing Based Framework.IEEE International Conference on Communications,2023-May3290-3295.
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[6]刘思聪.Channel Estimation for Underwater Visible Light Communication: A Sparse Learning Perspective.2023 IEEE International Conference on Communications Workshops: Sustainable Communications for Renaissance, ICC Workshops 2023,943-948.
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[7]刘思聪.Sparsity-Aware Intelligent Massive Random Access Control for Massive MIMO Networks: A Reinforcement Learning Based Approach.IEEE Transactions on Wireless Communications,23(8):1-1.
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[8]刘思聪,Cooperative Robotics Visible Light Positioning: An Intelligent Compressed Sensing and GAN-Enabled Framework.IEEE Journal on Selected Topics in Signal Processing,1-12.
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[9]刘思聪,基于多节点协作的鲁棒可见光智能定位.电信科学,2023,39(05):28-41.
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[10]刘思聪,Denoising enabled channel estimation for underwater acoustic communications: A sparsity-aware model-driven learning approach.Intelligent and Converged Networks,2023,4(1):1-14.
