Adaptive Filtering-Based Iterative Channel Estimation for MIMO Wireless Communications Jongsoo Choi, Martin Bouchard and Tet Hin Yeap School of Information Technology and Engineering University of Ottawa Ottawa, Ontario, Canada, K1N 6N5 Email:{jchoi,bouchard,tet}@site.uottawa.ca Abstract— Accurate channel state information (CSI) is a pre- requisite for diversity and multiplexing gains in multiple-input multiple-output (MIMO) wireless systems. More refined CSI can be attained by bootstrapping channel estimation techniques. In this paper, we propose adaptive filtering-based iterative channel estimators with the incorporation of an iterative receiver over a flat-fading MIMO wireless link. The performance of three different algorithms with the aid of hard decision feedback is evaluated and compared through simulations. We show that the least-mean-square algorithm is a reasonable choice among the other considered algorithms in terms of the performance and feasibility. Keywords – Adaptive filtering, channel estimation, iterative receiver, MIMO wireless link. I. I NTRODUCTION The use of a multiple-input multiple-output (MIMO) wire- less link has made the promise of higher data rates and increased spectral efficiency through increased computational complexity [1]. Future wireless communications will oper- ate under a MIMO antenna link to support a wide range of services requiring high transmission data rates. Accurate channel state information (CSI) is a prerequisite for the full exploitation of the advantages of MIMO systems. However, in real wireless scenarios the perfect CSI is never known to the receiver. Obviously channel estimation becomes a key topic for future applications of the MIMO system. Recently, iterative (turbo) receivers for MIMO wireless links, which perform joint detection and decoding, have received great attention due to their performance gain achieved. A popular estimation technique incorporated in an iterative receiver is iterative channel estimation [2],[3]. In an iterative channel estimation method, both pilot symbols and soft (or hard) estimates of the data symbols are used to improve the channel quality in a semiblind manner which distinguishes itself from a blind procedure and a supervised procedure. Adaptive filtering techniques may be a natural choice for iterative channel estimation, because of their computational efficiency compared to one-shot approaches [3],[4], such as least-squares (LS) and linear minimum mean-square error (MMSE) methods which need matrix inversions. At a cost of complexity, both recursive least-squares (RLS) [5],[6] and Kalman filter (KF) [7],[8] algorithms are extensively used for channel estimation in wireless communications, while a least-mean-square (LMS) algorithm is rarely used despite its computational efficiency and feasibility. In [5] and [6], RLS methods are used for iterative channel estimation of MIMO systems, but only the soft estimate of data symbols was evaluated. Although in [7] and [8], KF techniques are applied for tracking MIMO channels, they are not in an iterative estimation mode. The performance of adaptive filtering-based iterative channel estimators is compared in [9], but it is per- formed in a single transmit and single receive link. One should note that soft iterative channel estimation does not always guarantee the refinement of channel estimates, especially at a lower signal-to-noise ratio (SNR). The authors [10] have revealed that hard iterative channel estimation can attain a better performance gain than soft iterative channel estimation under a BPSK modulation. In this paper, three adaptive filtering-based iterative channel estimation techniques using hard decisions are evaluated with the incorporation of an iterative receiver, turbo-BLAST [11]. The performance of three algorithms is compared through Monte Carlo simulations. II. SIGNAL MODEL AND TRANSCEIVER STRUCTURE A. Signal Model We consider a MIMO wireless link with M transmit and N receive antennas in a flat-fading environment. We assume that the channel matrix is modeled as Rayleigh random processes and the fading is to be uncorrelated across antennas where each individual realization of the channel path is independent for all time steps k. The received signal vector r ∈C N×1 on the receiver at symbol time k can be written as r[k]= H[k]s[k]+ v[k] (1) where H ∈C (N×M) denotes the channel matrix, s ∈C M×1 is the symbol vector simultaneously transmitted by the M transmit antennas, and v ∈ C N×1 denotes the complex additive white Gaussian noise vector with zero mean and covariance matrix σ 2 v I N . Throughout the paper we assume that transmission and receiving are perfectly synchronized with symbol timing. B. Transmitter Structure A high-level description of a turbo-BLAST transmitter [11] employing the random layered space-time codes is depicted in Fig. 1(a). User information stream b is demultiplexed into