Compensation Estimation Method for Fast Fading MIMO-OFDM Channels Based on Compressed Sensing

Manuscript received February 25, 2015; revised July 18, 2015. Corresponding author email: [email protected]. doi:10.12720/jcm.10.7.466-473 to time-variant multipath channels in orthogonal OFDM leads to inter-carrier interference (ICI) which increases the error of system [4]-[5]. Therefore, the channel time variation during a symbol block must be considered to support high-speed mobile channels. Many pilot-aided channel estimation methods [3]-[5] usually estimate the channel response for a few selected subcarriers first. Those observations are used to interpolate the rest subcarriers. In such schemes, the required number of pilots depends on the coherence bandwidth of the channel, since the spacing of the pilot sequence has to satisfy the Nyquist sampling theorem to properly sample the fast fading channels. However, these schemes just consider the rich scattering environment, with the sparsity of the MIMO-OFDM channels being ignored. A number of sparse channel estimation schemes [6]-[17] have been proposed for time-frequency fading channels. The time-frequency joint sparse channel estimation scheme [17] first relies on a pseudorandom time-domain preamble, which is identical for all transmit antennas. The sparse common support property of the MIMO channels is utilized to acquire the partial common support. Then, frequency-domain orthogonal pilots are used for the channel recovery. However, the common support and the required number of pilots depend on the coherence bandwidth of channels and time of transmit antennas. The overhead of the required pilots or preamble will significantly increase as the number of transmit antennas. The blocks of transmitted OFDM symbols become large, which decreases the spectral efficiency. It may lead to the unacceptable high computational consumption, inter symbol interference and low frequency mask [18]. According to the Heisenberg uncertainty principle [19], the pulse signal is concentrated distribution in frequency domain and must disperse in the time domain. The sensing matrix of is updated by highspeed mobile channels [20]. It is not easy to obtain sparse channels that meet the requirements of band and time limited [21]. The solution of problem to construct the sparse channels requires the use of a dynamic mathematical model of pulse wave functions. However, such models often involve errors due to a variety of causes, including fast fading environments, atmospheric effects, and hardware limitations. In this paper, we focus on the compensation estimation of mathematical model in compressed sensing (CS) based fast fading MIMO-OFDM channels. In order to achieve