Linear MMSE-Optimal Turbo Equalization Using Context Trees

Kim, Kyeongyeon; Kalantarova, Nargiz; Kozat, Süleyman S.; Singer, Andrew C.

doi:10.1109/tsp.2013.2256899

Cited by 12 publications

(14 citation statements)

References 24 publications

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“…The proposed scheme is called as normalized adaptive minimal symbol-error-rate (NAMSER) turbo equalizer. Compared with some existing works which are LMS turbo equalizer [25], MMSE turbo equalizer [26] and AMSER equalizer [12] with turbo receiver, the proposed scheme has better performance and lower complexity. The simulation results show that the proposed scheme has better error rate performance than that of the existing schemes.…”

Section: Introductionmentioning

confidence: 94%

A Novel Adaptive Turbo Receiver for Large-Scale MIMO Communications

2018

KSII TIIS

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Massive (large-scale) MIMO (multiple-input multiple-output) is one of the key technologies in next-generation wireless communication systems. This paper proposes a high-performance low-complexity turbo receiver for SC-FDMA (single-carrier frequency-division multiple access) based MMIMO (massive MIMO) systems. Because SC-FDMA technology has the desirable characteristics of OFDMA (orthogonal frequency division multiple access) and the low PAPR (peak-to-average power ratio) of SC transmission schemes, the 3GPP LTE (long-term evolution) has adopted it as the uplink transmission to meet the demand high data rate and low error rate performance. The complexity of computing will be increased greatly in base station with massive MIMO (MMIMO) system. In this paper, a low-complexity adaptive turbo equalization receiver based on normalized minimal symbol-error-rate for MMIMO SC-FDMA system is proposed. The proposed receiver is with low complexity than that of the conventional turbo MMSE (minimum mean square error) equalizer and is also with better bit error rate (BER) performance than that of the conventional adaptive turbo MMSE equalizer. Simulation results confirm the effectiveness of the proposed scheme.

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Section: Introductionmentioning

confidence: 94%

A Novel Adaptive Turbo Receiver for Large-Scale MIMO Communications

2018

KSII TIIS

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“…Since its introduction and application to data compression, the CTW algorithm and its many variants have also been applied to numerous different statistical tasks, including prediction [32], segmentation [11]. reinforcement learning [25], network traffic analysis [12], turbo decoding [13], spam detection [24], and finance [6]. Biological applications can be found in several of the references cited above, as well as in [8,9,15].…”

Section: Introductionmentioning

confidence: 99%

Deep Tree Models for ‘Big’ Biological Data

Mertzanis

Panotonoulou

Skoularidou

et al. 2018

2018 IEEE 19th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)

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The identification of useful temporal dependence structure in discrete time series data is an important component of algorithms applied to many tasks in statistical inference and machine learning, and used in a wide variety of problems across the spectrum of biological studies. Most of the early statistical approaches were ineffective in practice, because the amount of data required for reliable modelling grew exponentially with memory length. On the other hand, many of the more modern methodological approaches that make use of more flexible and parsimonious models result in algorithms that do not scale well and are computationally ineffective for larger data sets. In this paper we describe a class of novel methodological tools for effective Bayesian inference for general discrete time series, motivated primarily by questions regarding data originating from studies in genetics and neuroscience. Our starting point is the development of a rich class of Bayesian hierarchical models for variable-memory Markov chains. The particular prior structure we adopt makes it possible to design effective, linear-time algorithms that can compute most of the important features of the relevant posterior and predictive distributions without resorting to Markov chain Monte Carlo simulation. The origin of some of these algorithms can be traced to the family of Context Tree Weighting (CTW) algorithms developed for data compression since the mid-1990s. We have used the resulting methodological tools in numerous application-specific tasks (including prediction, segmentation, classification, anomaly detection, entropy estimation, and causality testing) on data from different areas of application. The results obtained compare quite favourably with those obtained using earlier approaches, such as Probabilistic Suffix Trees (PST), Variable-Length Markov Chains (VLMC), and the class of Markov Transition Distributions (MTD).

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“…Although linear equalization is the simplest equalization method, it delivers an extremely inferior performance compared to that of the optimal methods, such as Maximum A Posteriori (MAP) or Maximum Likelihood (ML) methods [10,17,18]. Nonetheless, the high complexities of the optimal methods, and also their need of the channel information [8,10,12,19,20], make them practically infeasible for UWA channel equalization, because of the extremely large delay spread of UWA channels [8,18,[21][22][23]. Hence, we seek to provide powerful nonlinear equalizers with low complexities as well as linear ones.…”

Section: Introductionmentioning

confidence: 99%

“…In piecewise linear equalization methods, the space of the received signal is partitioned into disjoint regions, each of which is then fitted a linear equalizer [18,24]. We use the term "linear" to refer generally to the class of "affine" rather than strictly linear filters.…”

Section: Introductionmentioning

confidence: 99%

Adaptive and efficient nonlinear channel equalization for underwater acoustic communication

Kari

Vanli

Kozat

2017

Physical Communication

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We investigate underwater acoustic (UWA) channel equalization and introduce hierarchical and adaptive nonlinear channel equalization algorithms that are highly efficient and provide significantly improved bit error rate (BER) performance. Due to the high complexity of nonlinear equalizers and poor performance of linear ones, to equalize highly difficult underwater acoustic channels, we employ piecewise linear equalizers. However, in order to achieve the performance of the best piecewise linear model, we use a tree structure to hierarchically partition the space of the received signal. Furthermore, the equalization algorithm should be completely adaptive, since due to the highly non-stationary nature of the underwater medium, the optimal MSE equalizer as well as the best piecewise linear equalizer changes in time. To this end, we introduce an adaptive piecewise linear equalization algorithm that not only adapts the linear equalizer at each region but also learns the complete hierarchical structure with a computational complexity only polynomial in the number of nodes of the tree. Furthermore, our algorithm is constructed to directly minimize the final squared error without introducing any ad-hoc parameters. We demonstrate the performance of our algorithms through highly realistic experiments performed on accurately simulated underwater acoustic channels.

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Linear MMSE-Optimal Turbo Equalization Using Context Trees

Cited by 12 publications

References 24 publications

A Novel Adaptive Turbo Receiver for Large-Scale MIMO Communications

A Novel Adaptive Turbo Receiver for Large-Scale MIMO Communications

Deep Tree Models for ‘Big’ Biological Data

Adaptive and efficient nonlinear channel equalization for underwater acoustic communication

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