Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: , a very efficient semi-blind approach for the detection of most significant taps (MSTs) in sparse orthogonal frequency-division multiplexing (OFDM) channel estimation is developed. The least square (LS) estimation problem of sparse OFDM channels is first formulated, showing that the key to sparse channel estimation lies in the detection of the MSTs. An in-depth study of the second-order statistics of the signal received through a noise-free sparse OFDM channel reveals the sparsity and other properties of the correlation functions of the received signal. These properties lead to a direct relationship beten the positions of the MSTs of the sparse channel and the most significant lags of the correlation functions, which is then used in conjunction with a pilot-assisted LS estimation to detect the MSTs in a semi-blind fashion. It os also shown that the new MST detection algorithm can be extended for the estimation of multiple-input–multiple-output (MIMO)–OFDM channels. A number of computer-simulation-based experiments for various sparse channels are carried out to confirm the effectiveness of the proposed semi-blind approach.
['Feng Wan', 'Wei-Ping Zhu', 'M.N.S. Swamy']
I am writing a research paper on a idea Semi-Blind Most Significant Tap Detection for Sparse Channel Estimation of OFDM Systems
[{"from": "human", "value": "I am writing a research paper on a idea Semi-Blind Most Significant Tap Detection for Sparse Channel Estimation of OFDM Systems"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: , a very efficient semi-blind approach for the detection of most significant taps (MSTs) in sparse orthogonal frequency-division multiplexing (OFDM) channel estimation is developed. The least square (LS) estimation problem of sparse OFDM channels is first formulated, showing that the key to sparse channel estimation lies in the detection of the MSTs. An in-depth study of the second-order statistics of the signal received through a noise-free sparse OFDM channel reveals the sparsity and other properties of the correlation functions of the received signal. These properties lead to a direct relationship beten the positions of the MSTs of the sparse channel and the most significant lags of the correlation functions, which is then used in conjunction with a pilot-assisted LS estimation to detect the MSTs in a semi-blind fashion. It os also shown that the new MST detection algorithm can be extended for the estimation of multiple-input\u2013multiple-output (MIMO)\u2013OFDM channels. A number of computer-simulation-based experiments for various sparse channels are carried out to confirm the effectiveness of the proposed semi-blind approach."}]