Multi-input Multi-output Channel Estimation Research Articles

Training-Aided Frequency-Domain Channel Estimation and Equalization for Single-Carrier Coherent Optical Transmission Systems

Frequency-domain equalization supported by training-aided 2 × 2 multi-input multi-output channel estimation (CE) is proposed for coherent optical transmission systems. In particular, novel training sequence (TS) schemes based on a constant amplitude zero auto-correlation code, efficient CE algorithms and equalizer-tap updating solutions are derived from the theoretical linear fiber channel model and investigated through simulations. The robustness of the proposed methods is demonstrated with respect to the main degrading optical propagation effects, such as amplified spontaneous emission noise, chromatic dispersion, polarization-mode dispersion, polarization-dependent loss, time misalignment between received and reference TS, frequency offset between transmitter and receiver lasers, as well as dynamic state of polarization rotation.

Channel Estimation Scheme for WLAN Systems with Backward Compatibility

IEEE 802.11n standards introduced a mixed-mode format frame structure to achieve higher throughput with multiple antennas while providing backward compatibility with legacy systems. Although multi-input multi-output channel estimation was possible only with high-throughput long training fields (HT-LTFs), the proposed scheme utilizes a legacy LTF as well as HT-LTFs in a decision feedback manner to improve the accuracy of the estimates. It was verified through theoretical analysis and simulations that the proposed scheme effectively enhances the mean square error performance.

Channel Estimation Scheme for WLAN Systems with Backward Compatibility

IEEE 802.11n standards introduced a mixed-mode format frame structure to achieve higher throughput with multiple antennas while providing backward compatibility with legacy systems. Although multi-input multi-output channel estimation was possible only with high-throughput long training fields (HT-LTFs), the proposed scheme utilizes a legacy LTF as well as HT-LTFs in a decision feedback manner to improve the accuracy of the estimates. It was verified through theoretical analysis and simulations that the proposed scheme effectively enhances the mean square error performance.

Enhanced channel estimation and symbol detection for high speed multi-input multi-output underwater acoustic communications

The need for achieving higher data rates in underwater acoustic communications leverages the use of multi-input multi-output (MIMO) schemes. In this paper two key issues regarding the design of a MIMO communications system, namely, channel estimation and symbol detection, are addressed. To enhance channel estimation performance, a cyclic approach for designing training sequences and a channel estimation algorithm called the iterative adaptive approach (IAA) are presented. Sparse channel estimates can be obtained by combining IAA with the Bayesian information criterion (BIC). Moreover, the RELAX algorithm can be used to improve the IAA with BIC estimates further. Regarding symbol detection, a minimum mean-squared error based detection scheme, called RELAX-BLAST, which is a combination of vertical Bell Labs layered space-time (V-BLAST) algorithm and the cyclic principle of the RELAX algorithm, is presented and it is shown that RELAX-BLAST outperforms V-BLAST. Both simulated and experimental results are provided to validate the proposed MIMO scheme. RACE'08 experimental results employing a 4 x 24 MIMO system show that the proposed scheme enjoys an average uncoded bit error rate of 0.38% at a payload data rate of 31.25 kbps and an average coded bit error rate of 0% at a payload data rate of 15.63 kbps.

Subspace system identification for training-based MIMO channel estimation

The application of state-space-based subspace system identification methods to training-based estimation for quasi-static multi-input–multi-output (MIMO) frequency-selective channels is explored with the motivation for better model approximation performance. A modification of the traditional subspace methods is derived to suit the non-contiguous nature of training data in mobile communication systems. To track the time variation of the channel, a new recursive subspace-based channel estimation is proposed and demonstrated in simulation with practical MIMO channel models. The comparison between the state-space-based channel estimation algorithm and the FIR-based Recursive Least Squares algorithm shows the former is a more robust modeling approach than the latter.

Bounds on SIMO and MIMO Channel Estimation and Equalization with Side Information

Constrained Cramer-Rao bounds are developed for convolutive multi-input multi-output (MIMO) channel and source estimation in additive Gaussian noise. Properties of the MIMO Fisher information matrix (FIM) are studied, and we develop the maximum rank of the unconstrained FIM and provide necessary conditions for the FIM to achieve full rank. Equality constraints on channel and signal parameters provide a means to study the potential value of side information, such as training symbols (semi-blind case), constant modulus (CM) sources, or known channels. Nonredundant constraints may be combined in an arbitrary fashion, so that side information may be different for different sources. The bounds are useful for evaluating the performance of SIMO and MIMO channel estimation and equalization algorithms. We present examples using the constant modulus blind equalization algorithm. The constrained bounds are also useful for evaluating the relative value of different types of side information, and we present examples comparing semi-blind, constant modulus, and known channel constraints. While the examples presented are primarily in the communications context, the CRB framework applies generally to convolutive source separation problems.