Hybrid Type-2 Fuzzy Based Channel Estimation for MIMO-OFDM System with Doppler Offset Influences

The channel estimation methods track and predict the variation in channel characteristics, so that the original signal can be obtained after nullifying the channel induced influences. The channel estimation methods impact the overall performance of the MIMO-OFDM system. When the communicating nodes are mobile, a complete estimation of the fast time varying channel is accomplished if the Doppler offset is evaluated along with the channel gain. However, most of the channel estimation approaches proposed in literature for MIMO-OFDM systems assume that the Doppler offset contributed by highly mobile communicating nodes is already known to the receiver. The estimation of the Doppler offset with the channel coefficients renders the channel estimation problem non linear. In this paper, the issue of this non linear channel estimation for high mobility communicating nodes with associated dynamic Doppler offset in a MIMO-OFDM system is addressed. In order to obtain complete information of the channel which includes the channel coefficients and the associated Doppler offsets, a hybrid interval type-2 fuzzy aided Kalman filter for channel estimation is proposed. The type-2 fuzzy based membership functions are used here opposed to the type-1 fuzzy membership functions because the type-2 fuzzy membership functions are capable of effective modeling even under high degree of uncertainties. Furthermore, a detailed computational complexity analysis of the proposed algorithm is presented which shows that the algorithm has moderate computational complexity and has good performance in fast time varying channel conditions with high node mobility in a MIMO-OFDM system.

[1]  Ronald A. Iltis,et al.  Joint Carrier Frequency Offset and Channel Estimation for Uplink MIMO-OFDMA Systems Using Parallel Schmidt Rao-Blackwellized Particle Filters , 2010, IEEE Transactions on Communications.

[2]  Dongrui Wu,et al.  Comparison and practical implementation of type-reduction algorithms for type-2 fuzzy sets and systems , 2011, 2011 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2011).

[3]  Jerry M. Mendel,et al.  Enhanced Karnik--Mendel Algorithms , 2009, IEEE Transactions on Fuzzy Systems.

[4]  Dennis S. Bernstein,et al.  Kalman filtering with constrained output injection , 2007, Int. J. Control.

[5]  Shie-Jue Lee,et al.  An Enhanced Type-Reduction Algorithm for Type-2 Fuzzy Sets , 2011, IEEE Transactions on Fuzzy Systems.

[6]  Arnaud Doucet,et al.  Joint Channel and Doppler Offset Estimation in Dynamic Cooperative Relay Networks , 2014, IEEE Transactions on Wireless Communications.

[7]  Georgios B. Giannakis,et al.  Data Sketching for Large-Scale Kalman Filtering , 2016, IEEE Transactions on Signal Processing.

[8]  Witold Pedrycz,et al.  Type-2 Fuzzy Logic: Theory and Applications , 2007, 2007 IEEE International Conference on Granular Computing (GRC 2007).

[9]  Qiang Shen,et al.  Closed form fuzzy interpolation , 2013, Fuzzy Sets Syst..

[10]  Jerry M. Mendel,et al.  Interval type-2 fuzzy logic systems , 2000, Ninth IEEE International Conference on Fuzzy Systems. FUZZ- IEEE 2000 (Cat. No.00CH37063).

[11]  Pramod R. Gunjal,et al.  Moving Object Tracking Using Kalman Filter , 2018, 2018 International Conference On Advances in Communication and Computing Technology (ICACCT).

[12]  Graham Kendall,et al.  On Nie-Tan Operator and Type-Reduction of Interval Type-2 Fuzzy Sets , 2018, IEEE Transactions on Fuzzy Systems.

[13]  Shyi-Ming Chen,et al.  Fuzzy interpolative reasoning for sparse fuzzy rule-based systems based on interval type-2 fuzzy sets , 2011, Expert Syst. Appl..

[14]  Namseok Chang,et al.  MIMO-OFDM Downlink Channel Prediction for IEEE802.16e Systems Using Kalman Filter , 2007, 2007 IEEE Wireless Communications and Networking Conference.

[15]  Xiangyun Zhou,et al.  Kalman filter-based channel estimation for amplify and forward relay communications , 2009, 2009 Conference Record of the Forty-Third Asilomar Conference on Signals, Systems and Computers.

[16]  M. Xiong,et al.  Extended Kalman Filter for Estimation of Parameters in Nonlinear State-Space Models of Biochemical Networks , 2008, PloS one.

[17]  Eric Pierre Simon,et al.  Joint Carrier Frequency Offset and Channel Estimation for OFDM Systems via the EM Algorithm in the Presence of Very High Mobility , 2012, IEEE Transactions on Signal Processing.

[18]  Lars Lindbom.  Simplified Kalman estimation of fading mobile radio channels: high performance at LMS computational load , 1993, 1993 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[19]  Hiok Chai Quek,et al.  Scale and move transformation-based fuzzy rule interpolation with interval type-2 fuzzy sets , 2013, 2013 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE).

[20]  Woei Wan Tan,et al.  Towards an efficient type-reduction method for interval type-2 fuzzy logic systems , 2008, 2008 IEEE International Conference on Fuzzy Systems (IEEE World Congress on Computational Intelligence).

[21]  D. Salmond,et al.  Target tracking: introduction and Kalman tracking filters , 2001 .

[22]  Dongrui Wu,et al.  Approaches for Reducing the Computational Cost of Interval Type-2 Fuzzy Logic Systems: Overview and Comparisons , 2013, IEEE Transactions on Fuzzy Systems.

[23]  Georgios B. Giannakis,et al.  Space-time coding and Kalman filtering for time-selective fading channels , 2002, IEEE Trans. Commun..

[24]  M. Melgarejo,et al.  Improved iterative algorithm for computing the generalized centroid of an interval type-2 fuzzy set , 2008, NAFIPS 2008 - 2008 Annual Meeting of the North American Fuzzy Information Processing Society.

[25]  Richard D. Wesel,et al.  Multi-input multi-output fading channel tracking and equalization using Kalman estimation , 2002, IEEE Trans. Signal Process..

[26]  M. Melgarejo,et al.  A Fast Recursive Method to Compute the Generalized Centroid of an Interval Type-2 Fuzzy Set , 2007, NAFIPS 2007 - 2007 Annual Meeting of the North American Fuzzy Information Processing Society.

[27]  J. Mendel.  Uncertain Rule-Based Fuzzy Logic Systems: Introduction and New Directions , 2001 .

[28]  Dongrui Wu,et al.  Genetic learning and performance evaluation of interval type-2 fuzzy logic controllers , 2006, Eng. Appl. Artif. Intell..

[29]  S. Haykin,et al.  Adaptive Filter Theory , 1986 .

[30]  Qiang Shen,et al.  Generalisation of Scale and Move Transformation-Based Fuzzy Interpolation , 2011, J. Adv. Comput. Intell. Intell. Informatics.

[31]  Bor-Sen Chen,et al.  Robust Fast Time-Varying Multipath Fading Channel Estimation and Equalization for MIMO-OFDM Systems via a Fuzzy Method , 2012, IEEE Transactions on Vehicular Technology.

[32]  Joumana Farah,et al.  Target Tracking Using Machine Learning and Kalman Filter in Wireless Sensor Networks , 2014, IEEE Sensors Journal.

[33]  Xin Fu,et al.  Closed form fuzzy interpolation with interval type-2 fuzzy sets , 2014, 2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE).

[34]  Gerald Matz,et al.  Kalman tracking of time-varying channels in wireless MIMO-OFDM systems , 2003, The Thrity-Seventh Asilomar Conference on Signals, Systems & Computers, 2003.

[35]  Hani Hagras,et al.  A hierarchical type-2 fuzzy logic control architecture for autonomous mobile robots , 2004, IEEE Transactions on Fuzzy Systems.

[36]  Shyi-Ming Chen,et al.  Fuzzy rule interpolation based on the ratio of fuzziness of interval type-2 fuzzy sets , 2011, Expert Syst. Appl..

[37]  V Vaidehi,et al.  Computational Complexity of the Kalman Tracking Algorithm , 1998 .

[38]  Dennis S. Bernstein,et al.  Reduced-order Kalman filtering for time-varying systems , 2007, 2007 46th IEEE Conference on Decision and Control.

[39]  Dongrui Wu,et al.  GENETIC LEARNING AND PERFORMANCE EVALUATION OF TYPE-2 FUZZY LOGIC CONTROLLERS , 2006 .

[40]  J. Mendel.  Computational requirements for a discrete Kalman filter , 1971 .

[41]  H. Hagras,et al.  Type-2 FLCs: A New Generation of Fuzzy Controllers , 2007, IEEE Computational Intelligence Magazine.