The paper proposes a novel approach for fine frequency synchronization of OFDM syn- chronization systems in multi-path channels. Maximum Likelihood (ML) function of frequency offsets including integral and decimal par...The paper proposes a novel approach for fine frequency synchronization of OFDM syn- chronization systems in multi-path channels. Maximum Likelihood (ML) function of frequency offsets including integral and decimal parts in frequency domain is developed according to the law of great number to eliminate the noise impact of the signal. When the timing delay close to the actual time, the proposed function produces a deep valley indicating frequency offset when large Valley-Square- Error (VSE) appears. Coarse timing offset can also be detected when function’s Valley-Square-Error (VSE) is maximized. Simulation results shows that the proposed algorithm gives very robust estimation of frequency offset, and a coarse timing offset estimation.展开更多
Continuously publishing histograms in data streams is crucial to many real-time applications,as it provides not only critical statistical information,but also reduces privacy leaking risk.As the importance of elements...Continuously publishing histograms in data streams is crucial to many real-time applications,as it provides not only critical statistical information,but also reduces privacy leaking risk.As the importance of elements usually decreases over time in data streams,in this paper we model a data stream by a sequence of weighted sliding windows,and then study how to publish histograms over these windows continuously.The existing literature can hardly solve this problem in a real-time way,because they need to buffer all elements in each sliding window,resulting in high computational overhead and prohibitive storage burden.In this paper,we overcome this drawback by proposing an online algorithm denoted by Efficient Streaming Histogram Publishing(ESHP)to continuously publish histograms over weighted sliding windows.Specifically,our method first creates a novel sketching structure,called Approximate-Estimate Sketch(AESketch),to maintain the counting information of each histogram interval at every time instance;then,it creates histograms that satisfy the differential privacy requirement by smartly adding appropriate noise values into the sketching structure.Extensive experimental results and rigorous theoretical analysis demonstrate that the ESHP method can offer equivalent data utility with significantly lower computational overhead and storage costs when compared to other existing methods.展开更多
文摘The paper proposes a novel approach for fine frequency synchronization of OFDM syn- chronization systems in multi-path channels. Maximum Likelihood (ML) function of frequency offsets including integral and decimal parts in frequency domain is developed according to the law of great number to eliminate the noise impact of the signal. When the timing delay close to the actual time, the proposed function produces a deep valley indicating frequency offset when large Valley-Square- Error (VSE) appears. Coarse timing offset can also be detected when function’s Valley-Square-Error (VSE) is maximized. Simulation results shows that the proposed algorithm gives very robust estimation of frequency offset, and a coarse timing offset estimation.
基金supported by the Program for Synergy Innovation in the Anhui Higher Education Institutions of China(No.GXXT-2020-012)the National Natural Science Foundation of China(No.62172003)+2 种基金the Anhui Provincial Natural Science Foundation(No.2108085MF218)the Anhui Province University Natural Science Research Project(No.2022AH040052)the Science and Technology Innovation Program of Ma’anshan,China(No.2021a120009).
文摘Continuously publishing histograms in data streams is crucial to many real-time applications,as it provides not only critical statistical information,but also reduces privacy leaking risk.As the importance of elements usually decreases over time in data streams,in this paper we model a data stream by a sequence of weighted sliding windows,and then study how to publish histograms over these windows continuously.The existing literature can hardly solve this problem in a real-time way,because they need to buffer all elements in each sliding window,resulting in high computational overhead and prohibitive storage burden.In this paper,we overcome this drawback by proposing an online algorithm denoted by Efficient Streaming Histogram Publishing(ESHP)to continuously publish histograms over weighted sliding windows.Specifically,our method first creates a novel sketching structure,called Approximate-Estimate Sketch(AESketch),to maintain the counting information of each histogram interval at every time instance;then,it creates histograms that satisfy the differential privacy requirement by smartly adding appropriate noise values into the sketching structure.Extensive experimental results and rigorous theoretical analysis demonstrate that the ESHP method can offer equivalent data utility with significantly lower computational overhead and storage costs when compared to other existing methods.
