Christian Rom

Iterative Channel Estimation with Robust Wiener Filtering in LTE Downlink

In this paper, an iterative enhancement of the robust Wiener filter (RWF) estimator is presented for a turbo-coded orthogonal frequency division multiplexing (OFDM) system under the umbrella of the 3GPP long term evolution. The proposed scheme can operate with uncoded or coded feedback, and outperforms the conventional linear RWF in the whole signal-to-noise ratio (SNR) range with both approaches. Results show that most of the gain is obtained in the first iteration of the algorithm, and better performance is achieved with the coded feedback scheme. A good tradeoff between accuracy and complexity is achieved by selecting a low number of turbo coding iterations (TCI) in the iterative loop and concentrating most of them at the final decoding stage. Following this design, cell spectral efficiency gains of around 2.7% and 6.5% can be obtained with respect to linear RWF for micro- and macro-cell scenarios respectively.

Baseline E-UTRA Downlink Spectral Efficiency Evaluation

This paper evaluates spectral efficiency performance of the 3GPP Evolved UTRA (E-UTRA) downlink with baseline settings. A detailed link level tool has been developed including the majority of 3GPP E-UTRA link chain processing modules such as time-domain link adaptation and LI HARQ. Since MIMO is an integral part of the new system, we include four basic multiple antenna configurations in the analysis, namely the SISO, 1x2 SIMO, 2x2 SFC and 2x2 BLAST schemes. Since the BLAST is very sensitive to channel estimation error in cell edge, a case denoted as adapMIMO is considered as well, where the SFC is taken as a backup for the BLAST in cell edge. The system bandwidth is fixed at 20 MHz and a Typical Urban channel model is assumed for both macrocell and microcell evaluations. Simulation results show that in macrocell scenario, the spectral efficiency performance gain of SIMO over SISO is up to 54%, while SFC shows an additional gain of 14% over SIMO. Although the adapMIMO scheme has similar performance as SFC in the macrocellular case, it produces about 30% spectral efficiency gain in the microcell scenario.

Parametric Modeling and Pilot-Aided Estimation of the Wireless Multipath Channel in OFDM Systems

In this paper we present a refined model of the wireless multipath channel along with a thorough analysis on the impact of spatial smoothing techniques when used for improved channel estimation. The state-of-the-art channel estimation algorithm for pilot-aided OFDM systems is robustly designed and operates without knowledge of the time-varying multipath propagation delays in the wireless channel. However, algorithms exploiting knowledge of these time-varying delay parameters can outperform the state-of-the-art solution. We demonstrate from simulations how the Unitary ESPRIT algorithm together with spatial smoothing techniques exhibit a promising potential for multipath propagation delay estimation. Furthermore, we show that the optimum smoothing parameters depend notably on the channel model assumed, specifically in terms of the dynamical behavior of the multipath delays.

Interference Cancellation Based on Divergence Minimization for MIMO-OFDM Receivers

In this paper, we present a novel iterative receiver for MIMO-OFDM systems with synchronous interferers. The receiver is derived based on the Kullback-Leibler divergence minimization framework, and combines channel estimation, interference cancellation and residual noise estimation in an iterative manner. By using both the pilot and data symbols, the channel estimator improves the accuracy of the estimates in each iteration, which leads to a more effective interference cancellation and data detection process. A performance evaluation based on Monte-Carlo simulations shows that the proposed scheme can effectively mitigate the effect of interferers, and operates very close to the single-user performance even in severe interference scenarios.

Channel Estimation Based on Divergence Minimization for OFDM Systems with Co-Channel Interference

In this paper, we present a novel approach for pilot-aided channel estimation in OFDM systems with synchronous co-channel interference. The estimator is derived based on the Kullback-Leibler divergence minimization framework. The obtained solution iteratively updates both the desired users and the interferers channels, using a combination of linear minimum mean squared-error (LMMSE) filtering and interference cancellation, avoiding the complex matrix inversions involved in the full LMMSE channel estimation approach. Estimation of the noise variance is also included in the iterative algorithm, accounting for Gaussian noise and residual interference after each iteration. The estimates of both channels are used at the equalizer to reject the interfering signal, thus mitigating the degradation due to co-channel interference. Simulation results show that the receiver using the proposed estimator performs as good as the one employing the full LMMSE estimator and very closely to a receiver having perfect knowledge of the channel coefficients.

Analysis of Time and Frequency Domain Pace Algorithms for OFDM with Virtual Subcarriers

This paper studies common linear frequency direction pilot- symbol aided channel estimation algorithms for orthogonal frequency division multiplexing in a UTRA long term evolution context. Three deterministic algorithms are analyzed: the maximum likelihood (ML) approach, the noise reduction algorithm (NRA) and the robust Wiener (RW) filter. A closed form mean squared error is provided for these three algorithms. Analytical and simulation results show that, in the presence of virtual subcarriers, the ML can suffer large performance degradation due to ill-conditioned matrix issues. A solution to this problem is to use the Tikhonov regularization method giving the NRA. The equivalence between the NRA and the RW is proved analytically. A practical implementation of the NRA and RW is proposed based on partial-input partial-output FFT, leading to 6 to 8 times lower complexity than the reference implementation.

Effect of Phase Noise on Spectral Efficiency for Utra Long Term Evolution

In this paper, the effects of phase noise on the spectral efficiency of the next generation of OFDM based mobile systems with channel estimation is investigated. The simulation context and parameter settings are taken from the 3GPP Evolved UTRA (E-UTRA) study item, focusing on an OFDM downlink single antenna system in 20 MHz bandwidth. Phase noise is modeled as a Wiener-Levy process and several phase noise powers are evaluated. The OFDM coherent detection method is based on pilot assisted channel estimation (PACE) with Wiener based frequency domain interpolation and second order Gaussian interpolation for the time domain interpolation. The cell level spectral efficiency is also evaluated for micro and macro-cell scenarios. The simulation results indicate that the phase noise effect in E-UTRA downlink can be reduced by using high performance local oscillator or by placing pilots in every OFDM symbols

OFDM receiver for fast time-varying channels using block-sparse Bayesian learning

We propose an iterative algorithm for orthogonal frequency-division multiplexing (OFDM) receivers operating over fast time-varying channels. The design relies on the assumptions that the channel response can be characterized by a few nonnegligible separable multipath components and that the temporal variation of each component gain can be well characterized with a basis expansion model (BEM) using a small number of terms. As a result, the channel estimation problem is posed as that of estimating a vector of complex coefficients that exhibits a block-sparse structure, which we solve with tools from block-sparse Bayesian learning (BSBL). Using variational Bayesian inference, we embed the channel estimator in a receiver structure that performs iterative channel and noise precision estimation, intercarrier interference (ICI) cancelation, detection, and decoding. Simulation results illustrate the superior performance of the proposed receiver over state-of-the-art receivers.

Message-Passing Receiver for OFDM Systems Over Highly Delay-Dispersive Channels

Propagation channels with maximum excess delay exceeding the duration of the cyclic prefix (CP) in OFDM systems cause intercarrier and intersymbol interference which, unless accounted for, degrade the receiver performance. Using tools from Bayesian inference and sparse signal reconstruction, we derive an iterative algorithm that estimates an approximate representation of the channel impulse response and the noise variance, estimates and cancels the intrinsic interference and decodes the data over a block of symbols. Simulation results show that the receiver employing our algorithm outperforms receivers applying traditional interference cancellation and pilot-based schemes, and it approaches the performance of an ideal receiver with perfect channel state information and perfect interference cancellation capabilities. We highlight the relevance of our algorithm in the context of both current and future wireless communications systems. By enabling the OFDM receiver experiencing these harsh conditions to locally cancel the interference, our design circumvents the spectral efficiency loss incurred by extending the CP duration, otherwise a straightforward solution. Furthermore, it sets the premises for the development of receivers for multicarrier systems like 5G, in which the subcarrier orthogonality may be relaxed or the frame duration shortened, at the expense of cutting down the CP or even removing it altogether.

Analysis of Smoothing Techniques for Subspace Estimation with Application to Channel Estimation

In this paper, we present a thorough investigation on the impact of spatial smoothing and forward-backward averaging techniques for subspace-based channel estimation. The spatial smoothing technique requires the selection of a window size, which, if not selected properly, leads to dramatically performance breakdown of the subspace-based methods. We aim to provide an explanation of the performance drop for certain window sizes and subsequently an understanding of a proper window size selection. In particular, we describe the behavior of the magnitude of the least signal eigenvalue as a function of the window size employed. Through simulations we show that the magnitude of this eigenvalue is of particular importance for estimating the subspace and the entailing performance of the channel estimator.