Time-interpolated channel estimation for increasing channel capacity of superimposed training-based precoding schemes

This study proposes a time-interpolated channel estimation method for the superimposed training-based precoding scheme in slowly time-varying channels. The regular inclusion of a superimposed training sequence, which is necessary for explicit time-varying channel estimation, reduces the power allocated to information transmission. Therefore time-interpolated channel estimation, where the transmission without a superimposed training symbol is performed during a few coherence times, makes very efficient use of transmitting power and possible gains in terms of capacity. A closed-form solution of the periodicity of time-interpolated channel estimation was derived using this method together with a Markov channel model. The simulation results confirmed that the use of the time-interpolated channel estimation method improves the performance of channel capacity and possibly provides energy savings for mobile terminals.

[1]  Sung-Yoon Jung,et al.  Channel Estimation and LDPC Code Puncturing Schemes Based on Incremental Pilots for OFDM , 2010 .

[2]  Hong Shen Wang,et al.  Finite-state Markov channel-a useful model for radio communication channels , 1995 .

[3]  Sung-Yoon Jung,et al.  Power allocation for hidden-pilot-aided precoding schemes in orthogonal frequency division multiplexing systems , 2014, IET Commun..

[4]  Xin Li,et al.  Turbo equalization with nonlinear Kalman filtering for time-varying frequency-selective fading channels , 2007, IEEE Transactions on Wireless Communications.

[5]  Robert W. Heath,et al.  Blind channel identification and equalization in OFDM-based multiantenna systems , 2002, IEEE Trans. Signal Process..

[6]  P. Takis Mathiopoulos,et al.  Fast simulation of diversity Nakagami fading channels using finite-state Markov models , 2003, IEEE Trans. Broadcast..

[7]  Pao-Chi Chang,et al.  On verifying the first-order Markovian assumption for a Rayleigh fading channel model , 1996 .

[8]  Cecilio Pimentel,et al.  Finite-state Markov modeling of correlated Rician-fading channels , 2004, IEEE Transactions on Vehicular Technology.

[9]  P. Sadeghi,et al.  Finite-state Markov modeling of fading channels - a survey of principles and applications , 2008, IEEE Signal Processing Magazine.

[10]  G. A. Halls HIPERLAN: the High Performance Radio Local Area Network standard , 1994 .

[11]  H. D. Luke Families of polyphase sequences with near-optimal two-valued auto- and crosscorrelation functions , 1992 .

[12]  Wei Chen,et al.  Estimation of time and frequency selective channels in OFDM systems: a Kalman filter structure , 2004, IEEE Global Telecommunications Conference, 2004. GLOBECOM '04..

[13]  Hongwei Yang A road to future broadband wireless access: MIMO-OFDM-Based air interface , 2005, IEEE Communications Magazine.

[14]  Umberto Spagnolini,et al.  Kalman filter of channel modes in time-varying wireless systems , 2005, Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005..

[15]  Geoffrey Ye Li,et al.  MIMO-OFDM for wireless communications: signal detection with enhanced channel estimation , 2002, IEEE Trans. Commun..

[16]  Ning Chen,et al.  What is the price paid for superimposed training in OFDM? , 2004, 2004 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[17]  Dong-Jo Park,et al.  Hidden Pilot Based Precoder Design for MIMO-OFDM Systems , 2008, IEEE Communications Letters.