Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The conventional channel estimation methods based on a preamble for filter bank multicarrier with offset quadrature amplitude modulation (FBMC/OQAM) systems in mobile-to-mobile sensor networks are inefficient. By utilizing the intrinsicsparsity of wireless channels, channel estimation is researched as a compressive sensing (CS) problem to improve the estimation performance. , an AdaptiveRegularized Compressive Sampling Matching Pursuit (ARCoSaMP) algorithm is proposed. Unlike anterior greedy algorithms, the new algorithm can achieve the accuracy of reconstruction by choosing the support set adaptively, and exploiting the regularization process, which realizes the second selecting of atoms in the support set although the sparsity of the channel is unknown. Simulation results show that CS-based methods obtain significant channel estimation performance improvement compared to that of conventional preamble-based methods. The proposed ARCoSaMP algorithm outperforms the conventional sparse adaptive matching pursuit (SAMP) algorithm. ARCoSaMP provides even more interesting results than the mostadvanced greedy compressive sampling matching pursuit (CoSaMP) algorithm without a prior sparse knowledge of the channel.
['Han Wang', 'Wencai Du', 'Lingwei Xu']
I am writing a research paper on a idea A New Sparse Adaptive Channel Estimation Method Based on Compressive Sensing for FBMC/OQAM Transmission Network.
[{"from": "human", "value": "I am writing a research paper on a idea A New Sparse Adaptive Channel Estimation Method Based on Compressive Sensing for FBMC/OQAM Transmission Network."}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The conventional channel estimation methods based on a preamble for filter bank multicarrier with offset quadrature amplitude modulation (FBMC/OQAM) systems in mobile-to-mobile sensor networks are inefficient. By utilizing the intrinsicsparsity of wireless channels, channel estimation is researched as a compressive sensing (CS) problem to improve the estimation performance. , an AdaptiveRegularized Compressive Sampling Matching Pursuit (ARCoSaMP) algorithm is proposed. Unlike anterior greedy algorithms, the new algorithm can achieve the accuracy of reconstruction by choosing the support set adaptively, and exploiting the regularization process, which realizes the second selecting of atoms in the support set although the sparsity of the channel is unknown. Simulation results show that CS-based methods obtain significant channel estimation performance improvement compared to that of conventional preamble-based methods. The proposed ARCoSaMP algorithm outperforms the conventional sparse adaptive matching pursuit (SAMP) algorithm. ARCoSaMP provides even more interesting results than the mostadvanced greedy compressive sampling matching pursuit (CoSaMP) algorithm without a prior sparse knowledge of the channel."}]