Seong-Wook Song

Soft input channel estimation for turbo equalization

In this paper, we consider soft decision directed channel estimation for turbo equalization. To take advantage of soft information provided by the decoder, a minimum mean square error linear channel estimator is derived under an uncorrelated channel tap model, and a soft input recursive least squares algorithm is also developed by modifying the cost function of the conventional recursive least squares algorithm. The performance of the proposed channel estimators are analyzed in terms of mean square identification error (MSIE) for stationary channels. Simulation results for both time-invariant and time-varying frequency-selective Rayleigh fading channels are also presented.

Pilot-Aided OFDM Channel Estimation in the Presence of the Guard Band

In this letter, pilot design and channel estimation are discussed for orthogonal frequency-division multiplexing (OFDM) systems with guard subcarriers. First, we investigate the effects of guard band on channel estimation errors. From this, we propose pilot placement having a maximum distance between adjacent pilots except for the guard band, and show that it achieves minimum channel estimation errors among partially equispaced pilots using equivalence of the Toeplitz and circulant matrices. Also, an efficient channel estimator is developed by introducing an extended channel and its finite impulse response (FIR) approximation to overcome high numerical complexity caused by the presence of guard subcarriers and the use of a large number of subcarriers. Simulation results are presented for OFDM and orthogonal frequency division multiple access (OFDMA) systems consistent with IEEE 802.16a standards.

Turbo equalization with an unknown channel

We consider the problem of joint equalization and decoding, using the method of turbo equalization originally developed by Douillard, et al. [3]. In its original form, turbo-equalization requires accurate knowledge of the channel at the receiver. We propose a receiver structure, based on a soft-input Kalman channel estimator, that can operate effectively without accurate channel knowledge and without training data. The resulting joint channel and data estimator is shown to outperform standard turbo equalization based on moderate-length training data.

Reduced Complexity Self-Tuning Adaptive Algorithms in Application to Channel Estimation

In this letter, reduced complexity self-tuning algorithms are proposed using simplified parameter updating procedures. Convergence analysis based on the independence assumption and the ordinary differential equation (ODE) method shows that the tuning parameter of the proposed algorithm attains the same limit as the conventional self-tuning adaptive algorithm. Simulations are carried out for channel estimation to support the analysis and performance of the proposed algorithms.

Blind OFDM Channel Estimation Using FIR Constraints: Reduced Complexity and Identifiability

In this correspondence, blind channel estimators exploiting finite alphabet constraints are discussed for orthogonal frequency-division multiplexing (OFDM) systems. Considering the channel and data jointly, a joint maximum-likelihood (JML) algorithm is described, along with identifiability conditions in the noise-free case. This approach enables development of general identifiability conditions for the minimum-distance (MD) finite alphabet blind algorithm of Zhou and Giannakis. Both the JML and MD algorithms suffer from high numerical complexity, as they rely on exhaustive search methods to resolve a large number of ambiguities. We present a substantially more efficient blind algorithm, the reduced complexity minimum distance (RMD) algorithm, by exploiting properties of the assumed finite-length impulse response (FIR) channel. The RMD algorithm exploits constraints on the unwrapped phase of FIR systems and results in significant reductions in numerical complexity over existing methods. In many cases, the RMD approach is able to completely eliminate the exhaustive search of the JML and MD approaches, while providing channel estimates of the same quality

Blind Decoding of Control Channel for Other Users in 3GPP Standards

This paper explores the blind decoding of control channels for obtaining other user identities in 3GPP specification, such as high-speed packet access and long-term evolution. The reliable decoding of control channels with user identities is crucial to mitigate inter-cell interference as well as multi-user interference. This paper exploits a method of user identity filtering followed by a method of user identity detection based on the traffic persistency, which is common to all standards. Hence, the proposed methods are applicable to all the standards regulated by 3GPP specification. In particular, this paper analyzes the proposed other user identity detection algorithm under the random coding. Simulation results show that the proposed method is reliable even at low SNRs and is also aligned with the analysis.

Signal Regeneration Adaptive Equalizer for Fast Fading Environment

The signal regeneration method (SRE) and associated adaptive algorithms are proposed for fast fading channels. The adaptive algorithms such as the leaky least mean square (LLMS) algorithm assisted by the SRE method provide the signal-to-interference-noise-ratio (SINR) performance comparable to the linear minimum mean square error (LMMSE) equalizer, with low numerical complexity and low memory requirements. The proposed SRE-LLMS algorithm is analyzed based on the independence assumption, and shown to provide low excessive mean square error (EMSE) compared to the LMS algorithm without incurring a bias in its estimate. The simulation results for the wireless channel models such as VA30 and VA120 are presented to validate the analysis.