A time domain multiple-CFOs and CIRs estimation algorithm over wireless multimedia sensor networks

Channel parameters estimation in an orthogonal frequency division multiple access(OFDMA)systemfor the receiver station is a multi-dimensional(MD)optimization problem,because every user node has aseparate local oscillator and every transmitter to receiver link has individual carrier frequency offset(CFO)and channel impulse response(CIR)parameters .In order to reduce the computational complexityfor MD optimization,a time domain CFOs and CIRs estimation algorithm over the OFDMA based wirelessmultimedia sensor networks(WMSN)is proposed in this paper.In this algorithm,the receiver stationcan decouple the signal from every node by correlation based on specially designed training sequences,sothat the MD optimization problem is simplified to an 1-D optimal problem.It is proved that the multipleCFOs can be identified from the correlation result using the phase shift of the consecutive training se-quences .Based on the CFOs estimation result,the CIRs can then be estimated according to the minimummean square error(MMSE)criterion.The theoretic analysis and simulation results show that the proposedalgorithm can effectively decouple the signal from different user nodes and the bit error rate(BER)per-formance curves are close to the ideal estimation when the user number is not large.

Channel parameters estimation in an orthogonal for the receiver station is a multi-dimensional （MD） frequency division multiple access （OFDMA） system optimization problem, because every user node has a separate local oscillator and every transmitter to receiver link has individual carrier frequency offset （CFO） and channel impulse response （CIR） parameters. In order to reduce the computational complexity for MD optimization, a time domain CFOs and CIRs estimation algorithm over the OFDMA based wireless multimedia sensor networks （WMSN） is proposed in this paper. In this algorithm, the receiver station can decouple the signal from every node by correlation based on specially designed training sequences, so that the MD optimization problem is simplified to an 1-D optimal problem. It is proved that the multiple CFOs can be identified from the correlation result using the phase shift of the consecutive training se- quences. Based on the CFOs estimation result, the CIRs can then he estimated according to the minimum mean square error （MMSE） criterion. The theoretic analysis and simulation results show that the proposed algorithm can effectively decouple the signal from different user nodes and the bit error rate （BER） per- formance curves are close to the ideal estimation when the user number is not large.