• View

  • Download

Embed Size (px)

Text of Blind Channel Estimation for MIMO-OFDM : BLIND CHANNEL ESTIMATION FOR MIMO-OFDM SYSTEMS 671 channel


    Blind Channel Estimation for MIMO-OFDM Systems

    Changyong Shin, Member, IEEE, Robert W. Heath, Jr., Senior Member, IEEE, and Edward J. Powers, Fellow, IEEE

    Abstract—By combining multiple-input multiple-output (MIMO) communication with the orthogonal frequency division multiplexing (OFDM) modulation scheme, MIMO-OFDM sys- tems can achieve high data rates over broadband wireless chan- nels. In this paper, to provide a bandwidth-efficient solution for MIMO-OFDM channel estimation, we establish conditions for channel identifiability and present a blind channel estimation technique based on a subspace approach. The proposed method unifies and generalizes the existing subspace-based methods for blind channel estimation in single-input single-output OFDM systems to blind channel estimation for two different MIMO-OFDM systems distinguished according to the number of transmit and receive antennas. In particular, the proposed method obtains accurate channel estimation and fast convergence with insensitivity to overestimates of the true channel order. If virtual carriers (VCs) are available, the proposed method can work with no or insufficient cyclic prefix (CP), thereby potentially increasing channel utilization. Furthermore, it is shown under specific system conditions that the proposed method can be applied to MIMO-OFDM systems without CPs, regardless of the presence of VCs, and obtains an accurate channel estimate with a small number of OFDM symbols. Thus, this method improves the transmission bandwidth efficiency. Simulation results illustrate the mean-square error performance of the proposed method via numerical experiments.

    Index Terms—Blind channel estimation, cyclic prefix, multiple- input multiple-output (MIMO) system, orthogonal frequency division multiplexing (OFDM), subspace method, virtual carrier.


    R ECENTLY, increasing interest has been concentratedon modulation techniques that provide high data rates over broadband wireless channels for applications, in- cluding wireless multimedia, wireless Internet access, and future-generation mobile communication systems. Orthogonal frequency division multiplexing (OFDM) is a promising digital modulation scheme to simplify the equalization in frequency- selective channels [1], [2]. The main benefit is that it simplifies implementation, and it is robust against the frequency-selective fading channels. Multiple-input multiple-output (MIMO) com- munication, enabled by multiple transmit and receive antennas,

    Manuscript received April 15, 2005; revised April 19, 2006 and April 25, 2006. This paper was presented in part at the IEEE Global Telecommunica- tions Conference, Dallas, TX, November 2004. The review of this paper was coordinated by Prof. Z. Wang.

    C. Shin is with the Communications and Network Laboratory, Samsung Advanced Institute of Technology (SAIT), 446-712 Korea (e-mail: cyshin3@

    R. W. Heath, Jr. and E. J. Powers are with the Wireless Networking and Communications Group (WNCG), Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX 78712-0240 USA (e-mail:;

    Digital Object Identifier 10.1109/TVT.2007.891429

    can increase significantly the channel capacity (see, e.g., [3]–[6] and references therein). Thus, MIMO-OFDM systems, which combine the OFDM with MIMO communication, can provide a high-performance transmission (see, e.g., [3], [4], and [7]–[9] and references therein).

    In a MIMO-OFDM system, a coherent signal detection re- quires a reliable estimate of the channel impulse responses between the transmit and receive antennas. These channels can be estimated by sending training sequences. The training requirements, however, are significant [10]–[18]. Furthermore, transmitting the training sequences is undesirable for certain communication systems [19], [20]. Thus, blind channel esti- mation for MIMO-OFDM systems has been an active area of research in recent years. Zhou et al. [21] proposed a subspace- based blind channel estimation method for space-time coded MIMO-OFDM systems using properly designed redundant linear precoding and the noise subspace method [22]–[24]. Bölcskei et al. [25] proposed an algorithm for blind channel estimation and equalization for MIMO-OFDM systems using second-order cyclostationary statistics induced by employing a periodic nonconstant-modulus antenna precoding. Yatawatta and Petropulu [26] presented a blind channel estimation method based on a nonredundant linear block precoding and cross- correlation operations. Zeng and Ng [27] proposed a subspace technique based on the noise subspace method for estimating the MIMO channels in the uplink of multiuser multiantenna zero-padding OFDM systems [28], [29].

    A variety of second-order statistics (SOS)-based blind es- timators (see, e.g., [22]–[24], and [30]–[51] and references therein) have been presented since Tong et al. [52] introduced a SOS-based technique for the blind identification of single-input multiple-output systems. Among those methods, the noise sub- space method is believed to be one of the most promising due to its simple structure and good performance. Thus, by exploiting the fundamental structure of the noise subspace method, sub- space methods [53]–[55] for single-input single-output (SISO) OFDM systems have been proposed and achieved good esti- mation performance. Muquet et al. [53] developed a subspace method for SISO-OFDM systems by utilizing the redundancy introduced by the cyclic prefix (CP) insertion, and derived a condition for channel identifiability. For shaping of the transmit spectrum, practical OFDM systems are not fully loaded [56]. The subcarriers that are set to zero without any information are referred to as virtual carriers (VCs) [57]. Other than the CP, the presence of VCs provides another useful resource that can be used for channel estimation. Li and Roy [54] proposed a subspace blind channel estimator for SISO-OFDM systems by considering the existence of VCs and provided a condition for

    0018-9545/$25.00 © 2007 IEEE


    channel identifiability. They showed that their estimator based on the noise subspace method can achieve better estimation performance than other blind estimation techniques [55], [58]. In particular, they demonstrated that the CPs are more useful for their estimator than VCs. In [54], however, the reason why the utilization of CPs rather than VCs increases the accuracy of channel estimation was not discussed. In addition, it was not considered how the channel estimation performance can be improved in cases with no or insufficient CPs.

    In this paper, we unify and generalize the SISO-OFDM subspace methods in [53] and [54] to the case of blind channel estimation for spatial multiplexing MIMO-OFDM systems with any number of transmit and receive antennas. We present a new blind channel estimator based on the noise subspace method and establish conditions for blind channel identification in spatially multiplexed MIMO-OFDM systems. The proposed method works regardless of the presence of VCs, can use as little as one received OFDM symbol for a filtering matrix, and operates with any number of transmit or receive antennas. Considering the presence of VCs, the proposed method can be applied to MIMO-OFDM systems without CPs, where blind es- timation techniques based on CPs cannot be employed, thereby providing the systems with the potential to achieve a higher channel utilization. For MIMO-OFDM systems with CPs, the proposed method can provide an additional performance gain with respect to the existing blind channel estimation methods. We provide numerical results that illustrate tradeoffs in mean- square error as a function of the CP length, number of VCs, and number of OFDM symbols used in the estimate.

    Compared with [25] and [26], we do not use a transmit precoding to aid our blind channel estimator. Furthermore, by virtue of our subspace approach, we need fewer OFDM symbols to obtain a reliable estimate. Our estimator, however, has an additional full rank requirement that is not present in the precoding-based methods. The combination of our estimator and the precoding-based methods is an interesting topic for future work. Our approach generalizes the studies in [53] and [54] to operate with multiple transmit and multiple receive antennas under the assumption that spatial multiplexing is used at the transmitter. With one transmit and receive antenna, our approach and identifiability conditions are simplified to those presented in [53] and [54]. A subspace-based method for MIMO-OFDM systems with spatial multiplexing was proposed in [59]. Compared with [59], we also consider the case of excess transmit antennas. Furthermore, the identifiability conditions provided in [59] are not complete. Specifically, their full rank requirement does not appear to be sufficient, and the channel ambiguity condition does not appear to be comprehensive.

    The rest of this paper is organized as follows. In Section II, we briefly describe a MIMO-OFDM system model. In Section III, we establish conditions for the MIMO-OFDM channel identifiability by generalizing the conditions presented in [54] and develop a blind channel estimation scheme based on the noise subspace method. Section IV contains simulation results demonstrating the performance of the proposed method. Finally, a conclusion is provided in Section V.

    The notations used in this paper are as follows. Matrices and vectors are denoted by symbols in boldface, and (·)∗,

    Fig. 1. MIMO-OFDM system model with Mt transmit and Mr receive antennas.