An Intelligent Optimization Approach to Non-stationary Interference Suppression for Wireless Networks
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摘要
In this paper, we propose a new technique to effectively suppress non-stationary interference signal for wireless networks. This technique combines a new scheme of interference signal estimation with the intelligent optimization projection. In order to capture interference signal's subspace, a time-varying method is used to estimate the non-stationary interference.Orthogonal polynomials are used for the basis function instead of the power of the time to reduce the computational complexity.The interference is then removed from the corrupted signal by subspace projection, resulting in less distortion to the desired signal. The performance of this approach is validated by computer simulation.
In this paper, we propose a new technique to effectively suppress non-stationary interference signal for wireless networks. This technique combines a new scheme of interference signal estimation with the intelligent optimization projection. In order to capture interference signal's subspace, a time-varying method is used to estimate the non-stationary interference.Orthogonal polynomials are used for the basis function instead of the power of the time to reduce the computational complexity.The interference is then removed from the corrupted signal by subspace projection, resulting in less distortion to the desired signal. The performance of this approach is validated by computer simulation.
In this paper, we propose a new technique to effectively suppress non-stationary interference signal for wireless networks. This technique combines a new scheme of interference signal estimation with the intelligent optimization projection. In order to capture interference signal's subspace, a time-varying method is used to estimate the non-stationary interference.Orthogonal polynomials are used for the basis function instead of the power of the time to reduce the computational complexity.The interference is then removed from the corrupted signal by subspace projection, resulting in less distortion to the desired signal. The performance of this approach is validated by computer simulation.
引文
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[15]L.Liu,“Orthogonal polynomial based time-varying autoregressive filtering for jammer excision in spread spectrum communication systems,”in Proc.IEEE International Conference on Networking Control and Senseing,Chicago,IL,USA:IEEE,2010,pp.117-121.
[16]M.G.Amin,“Interference mitigation in spread spectrum communication systems using time-frequency distributions,”IEEE Trans.Single Processing,vol.45,no.1,pp.90-101,1997.
[17]Y.Zhang,M.G.Amin,and H.Ge,“Nonstationary interference suppression in ds/ss communications using space-time oblique projection techniques,”in Proc.the 2nd IEEE International Symposium on Signal Processing and Information Technology,Marrakech,Morocco:IEEE,2002.
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[2]J.Yan,M.Zhou,and Z.Ding,“Recent advances in energy-efficient routing protocols for wireless sensor networks:a review,”IEEE Access,vol.4,pp.5673-5686,Oct.2016.
[3]X.Wang,C.Wang,J.Zhang,M.C.Zhou,and C.Jiang,“Improved rule installation for real-time query service in software-defined internet of vehicles,”IEEE Trans.on Intelligent Transportation Systems,vol.18,no.2,pp.225-235,Feb.2017.
[4]W.J.Zhu,M.L.Deng,and Q.L.Zhou,“An intrusion detection algorithm for wireless networks based on ASDL,”IEEE/CAA J.Autom.Sinica,vol.5,no.1,pp.92-107,Jan.2018.
[5]W.Stallings and C.Beard,Wireless Communication Networks and Systems.Reading,Mass Pearson,2015.
[6]L.Chen,J.Zheng,M.Su,and J.Zhang,“A strong interference suppressor for satellite signals in GNSS receivers,”Circuits,Systems,and Signal Processing,vol.36,no.7,pp.3004-3019,Jul.2017.
[7]C.Li,L.Huang,and X.Wang,“The suppression of narrow band jammers algorithm and the implementation of Beidou B3 signal,”in Proc.International Conference on Power Electronics and Energy Engineering,Hong Kong,China,2015.
[8]A.Akan and Y.Cekic,“Interference suppression in dsss communication systems using instantaneous frequency estimation.”in Proc.IEEE Int.Conf.Electronics,Circuits and Systems,Pafos,Cyprus:IEEE,1999,pp.461-464.
[9]L.Stankovic,“A method for time-frequency analysis,”IEEE Trans.on Signal Processing,vol.42,pp.225-229,1994.
[10]P.Shan and A.A.Bexx,“High-resolution instantaneous frequency estimation based on time-varying ar modeling time-frequency and time scale analysis,”in Proc.IEEE SP International Symposium on,Pittsburgh,PA,USA:IEEE,1998,pp.109-112.
[11]K.B.Eom,“Time-varying autoregressive modeling of hrr radar signatures,”IEEE Trans.on Aerospace and Electronic System,vol.35,pp.974-988,July 1999.
[12]P.Shan and A.A.Beex,“FM interference suppression in spread spectrum communication using time-varying autoregressive model based instantaneous frequency estimation,”in Proc.IEEE Int.Conf.Acoustics,Speech and Signal Processing,Phoenix,AZ,USA:IEEE,1999,pp.2559-2562.
[13]P.Shan and A.A.Beex,“Time-varying filtering using full spectral information for soft cancellation of FM interference in spread spectrum communications,”in Proc.2nd IEEE Workshop on Signal Processing Advances in Wireless Communications,Cannes,France:IEEE,1999,pp.321-324.
[14]M.G.Hall,A.V.Oppenheim,and A.S.Willsky,“Time-varying autoregressive modeling of speech,”Signal Processing,vol.5,no.3,pp.267-285,1984.
[15]L.Liu,“Orthogonal polynomial based time-varying autoregressive filtering for jammer excision in spread spectrum communication systems,”in Proc.IEEE International Conference on Networking Control and Senseing,Chicago,IL,USA:IEEE,2010,pp.117-121.
[16]M.G.Amin,“Interference mitigation in spread spectrum communication systems using time-frequency distributions,”IEEE Trans.Single Processing,vol.45,no.1,pp.90-101,1997.
[17]Y.Zhang,M.G.Amin,and H.Ge,“Nonstationary interference suppression in ds/ss communications using space-time oblique projection techniques,”in Proc.the 2nd IEEE International Symposium on Signal Processing and Information Technology,Marrakech,Morocco:IEEE,2002.
[18]L.Liu and H.Ge,“Time-varying ar modeling and subspace projection for FM jammer suppression,”in Proc.IEEE.Asilomar Conf.on Signal,System and Computers,vol.2,Grove,CA,USA:IEEE,2003,pp.1057-1062.
[19]E.W.Cheney,Introduction to Approximation Theory.New York:McGraw-Hill Book Company,1966.
[20]G.Qi,“High accuracy range estimation of FMCW level radar based on the phase of the zero-padded FFT,”in Proc.IEEE International Conference on Signal Processing,Hawaii,USA:IEEE,2004,pp.2078-2081.
[21]C.D.Meyer,Matrix Analysis and Applied Linear Algebra.Society for Industrial and Applied Mathematics,2000.
[22]N.Dunford and J.T.Schwartz,Linear Operators,Part I:General Theory.New York:Interscience,1958.
[23]R.T.Behrens and L.L.Scharf,“Signal processing applications of oblique projection operators,”IEEE Trans.Signal Processing,vol.42,pp.1413-1424,1994.