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Award ID contains: 2316865

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  1. ; ; ; ; (, IEEE)
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  2. ; ; ; ; (, IEEE)
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  3. ; ; (, IEEE)
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  4. ; ; ; ; (, IEEE)
    WS1- ICASSP 2024 Satellite Workshop on Deep Neural Network Model Compression 
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    Accepted Manuscript Full Text Available
  5. ; (, IEEE)
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  6. ; ; ; ; (, IEEE Internet of Things Journal)
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  7. ; ; (, Proc. IEEE 9th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP))
    This paper explores the use of reconfigurable intelligent surfaces (RISs) for moving target detection in multi-input multi-output (MIMO) radar. Unlike previous related works that ignore the propa-gation delay difference between the direct path and the RIS-reflected path, we examine the detection problem in RIS-assisted MIMO radar by taking into account the effect of asynchronous propagation. Specifically, we first develop a general signal model for RIS-aided MIMO radar with multiple asynchronous RISs and arbitrary wave-forms. Next, we formulate the RIS design problem by maximizing the overall received signal energy. The resulting optimization problem is non-convex, which is solved with semidefinite relaxation (SDR) techniques. A coherent detector is introduced for target detection. Finally, numerical results are presented to demonstrate the performance of the RIS-aided MIMO radar in comparison with the conventional MIMO radar. 
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  8. ; ; ; (, IEEE)
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  9. ; ; ; (, IEEE Transactions on Wireless Communications)
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  10. ; ; ; (, IEEE Transactions on Wireless Communications)
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