Frederik Simoens

Multidimensional mapping for bit-interleaved coded modulation with BPSK/QPSK signaling

In recent years, it has been recognized that bit-interleaved coded modulation with iterative decoding (BICM-ID) achieves excellent performance on virtually any channel, provided the signal mapping is carefully designed. In this paper, we introduce multidimensional mapping for BICM-ID, where a group of bits is mapped to a vector of symbols, rather than a single symbol. This allows for more flexibility and potential performance improvements. Our analysis shows that multidimensional mapping leads to an increase in Euclidean distance, thus boosting the performance compared to conventional mapping schemes. We derive a design criterion for optimal mappings and we provide such optimal mappings for BPSK and QPSK constellations.

reduced complexity data-aided and code-aided frequency offset estimation for flat-fading MIMO channels

This contribution deals with carrier frequency offset estimation for flat-fading multiple-input multiple-output (MIMO) channels. Both data-aided (using training symbols) and iterative code-aided (using the unknown coded symbols) estimation is considered. In both scenarios, maximum-likelihood (ML) frequency offset estimation involves solving a maximization problem with no closed-form solution. Since numerical calculation of the ML estimates is computationally hard, we derive a simple closed-form approximation. Simulation results indicate that the ML algorithm and the proposed reduced-complexity algorithm operate closely to the Cramer-Rao bound (CRB)

Code-aided estimation and detection on time-varying correlated MIMO channels: a factor graph approach

This paper concerns channel tracking in a multiantenna context for correlated flat-fading channels obeying a Gauss-Markov model. It is known that data-aided tracking of fast-fading channels requires a lot of pilot symbols in order to achieve sufficient accuracy, and hence decreases the spectral efficiency. To overcome this problem, we design a code-aided estimation scheme which exploits information from both the pilot symbols and the unknown coded data symbols. The algorithm is derived based on a factor graph representation of the system and application of the sum-product algorithm. The sum-product algorithm reveals how soft information from the decoder should be exploited for the purpose of estimation and how the information bits can be detected. Simulation results illustrate the effectiveness of our approach.

Spatial mapping for MIMO systems

This contribution proposes a new spatial mapping technique for bit-interleaved coded modulation (BICM) over multiple-input multiple-output (MIMO) channels. It is an extension of conventional BICM whereby the serial-to-parallel converter is replaced with a more general mapping strategy. Genie performance analysis of spatial mapping schemes results in very simple design criteria for the mapping function. Using an iterative detector, a significant performance gain is observed compared to a conventional BICM scheme, at no significant increase in computational complexity. The predicted performance gains are verified through computer simulations.

Code-aided joint channel estimation and frame synchronization for MIMO systems

This contribution deals with the problem of joint frame synchronization and channel estimation for a system using bit-interleaved coded modulation in a MIMO context. A receiver, based on the EM algorithm, is derived. This receiver iterates between detection and estimation. We illustrate how initial estimates may be obtained and how convergence problems may be avoided. Through computer simulations the performance of the proposed receiver is investigated, both in terms of frame error rate (FER) and estimation error variance. We show that exploiting code properties for estimation purposes allows to reduce the length of training sequences and thus results in an increase in spectral efficiency.

Monte Carlo solutions for blind phase noise estimation

This paper investigates the use of Monte Carlo sampling methods for phase noise estimation on additive white Gaussian noise (AWGN) channels. The main contributions of the paper are (i) the development of a Monte Carlo framework for phase noise estimation, with special attention to sequential importance sampling and Rao-Blackwellization, (ii) the interpretation of existing Monte Carlo solutions within this generic framework, and (iii) the derivation of a novel phase noise estimator. Contrary to the ad hoc phase noise estimators that have been proposed in the past, the estimators considered in this paper are derived from solid probabilistic and performance-determining arguments. Computer simulations demonstrate that, on one hand, the Monte Carlo phase noise estimators outperform the existing estimators and, on the other hand, our newly proposed solution exhibits a lower complexity than the existing Monte Carlo solutions.

Linear Precoders for Bit-Interleaved Coded Modulation on AWGN Channels: Analysis and Design Criteria

This paper investigates linear precoding for bit-interleaved coded modulation (BICM) on additive white Gaussian noise (AWGN) channels. Concatenating a linear precoder as an inner encoder with an outer (convolutional) encoder produces a powerful code with a limited decoding complexity. Linear precoders are examined and optimized for two scenarios: using (i) a noniterative decoding strategy and (ii) an iterative decoding strategy under a perfect feedback assumption. The precoder design is based on an information-theoretical approach, on the one hand, and a pair-wise error probability (PEP) analysis, on the other hand. Both approaches render convenient precoder design rules. For a binary phase-shift keying (BPSK) signal set, the optimal precoders that result from these rules are also derived. Numerical results confirm the analytical findings and simulations illustrate the effectiveness of the approach.

Binary erasure codes for packet transmission subject to correlated erasures

We design some simple binary codes that are very well suited to reconstruct erased packets over a transmission medium that is characterized by correlation between subsequent erasures. We demonstrate the effectiveness of these codes for the transmission of video packets for HDTV over a DSL connection.

Computationally efficient frequency offset estimation for flat-fading MIMO channels: performance analysis and training sequence design

The paper deals with carrier frequency offset estimation for a flat-fading multiple-input multiple-output (MIMO) channel using a training sequence. The resulting maximum likelihood (ML) estimation entails solving a maximization problem with no closed-form solution. Since numerical calculation of the estimate is computationally hard, we propose a sub-optimal closed-form solution. In contrast with single-input-single-output (SISO) systems, however, self-noise arises in MIMO closed-form frequency offset estimation. Through proper training sequence design, we show how to avoid this self-noise and achieve a performance close to ML-performance and the Cramer-Rao bound (CRB).

A reduced complexity frequency offset estimation technique for flat fading MIMO channels

In this contribution we consider the carrier frequency offset estimation in the case of linear digital modulation, transmitted over a flat-fading multiple-input multiple-output (MIMO) Channel. For this problem, maximum-likelihood (ML) estimation gives rise to a carrier frequency offset algorithm that involves the maximization of the magnitude of a Fourier transform. Since no closed-form solution can be derived and numerical calculation of the estimate is computationally hard, we propose a suboptimal solution. Our simulation results show that the proposed estimator yields a performance that is comparable with the Cramer-Rao bound.