Tensor modelling of MIMO communication systems with performance analysis and Kronecker receivers

Costa, Michele Nazareth da; Favier, Gérard; Romano, João Marcos Travassos

doi:10.1016/j.sigpro.2017.12.015

Cited by 51 publications

(30 citation statements)

References 28 publications

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“…Most of these models can be viewed as constrained PARAFAC decompositions. See [11, 89, 90] for overviews of such models and corresponding point‐to‐point MIMO systems. In the context of cooperative or relay‐assisted communications, several papers have proposed tensor‐based models and algorithms for the supervised channel estimation problem [91, 92], considering two‐hop AF relays and two‐way MIMO relaying [93].…”

Section: Tensor‐based Mimo Wireless Communication Systemsmentioning

confidence: 99%

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Tensor methods for multisensor signal processing

Miron

Zniyed

Boyer

et al. 2020

IET signal process.

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Over the last two decades, tensor‐based methods have received growing attention in the signal processing community. In this work, the authors proposed a comprehensive overview of tensor‐based models and methods for multisensor signal processing. They presented for instance the Tucker decomposition, the canonical polyadic decomposition, the tensor‐train decomposition (TTD), the structured TTD, including nested Tucker train, as well as the associated optimisation strategies. More precisely, they gave synthetic descriptions of state‐of‐the‐art estimators as the alternating least square (ALS) algorithm, the high‐order singular value decomposition (HOSVD), and of more advanced algorithms as the rectified ALS, the TT‐SVD/TT‐HSVD and the Joint dImensionally Reduction and Factor retrieval Estimator scheme. They illustrated the efficiency of the introduced methodological and algorithmic concepts in the context of three important and timely signal processing‐based applications: the direction‐of‐arrival estimation based on sensor arrays, multidimensional harmonic retrieval and multiple‐input–multiple‐output wireless communication systems.

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Section: Tensor‐based Mimo Wireless Communication Systemsmentioning

confidence: 99%

“…If the coding tensor is assumed to be known at the receiver, the model ambiguity becomes a scalar factor related to a Kronecker product. This scalar ambiguity can be eliminated assuming the a priori knowledge of only one symbol, leading to a semi‐blind Kronecker receiver [90].…”

Section: Tensor‐based Mimo Wireless Communication Systemsmentioning

confidence: 99%

Tensor methods for multisensor signal processing

Miron

Zniyed

Boyer

et al. 2020

IET signal process.

Self Cite

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“…These performances can be used to blindly/semi-blindly recover symbols and estimate/equalize channel information without using long training sequences [28], [29], [30]. There are numerous other applications in wireless communication for tensor decompositions, such as direction-of-arrival (DOA) estimation for massive MIMO systems [31], noisy compressive sampling (CS) based on block-sparse tensors [32], the deep learning structure [33], packet routing strategy [34], multi-dimensional spectrum map construction [35] and more [36], [37], [38]. Such diverse application in wireless communication illustrates that the tensor is an excellent tool to solve numerous wireless communication problems.…”

Section: B Tensor Decomposition Model In Communication Systemsmentioning

confidence: 99%

Tensor Communication Waveform Design With Semi-Blind Receiver in the MIMO System

Dou

et al. 2020

IEEE Trans. Veh. Technol.

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A flexible mathematical framework for adaptive wireless communication waveform design is of importance for the implement of the cognitive radio-based software defined radio (CR-based SDR). As one of the popular models, the "spectrally modulated spectrally encoded" (SMSE) was proposed to tackle this problem but it cannot be trivially applied to the (massive) multiple input multiple output (MIMO) systems. In this paper, we extend the useful SMSE model into MIMO systems. Inspired by the tensor technique in signal processing and machine learning, we reformulate the desired waveforms as a higher-order tensors, of which the modes correspond to various modulation parameters such as coding chips, frequency and antennas. Beside the waveform design model, we further propose a new semi-blind receiver for the new model. Due the uniqueness of the applied tensor decomposition, we proved that the proposed receiver can jointly estimate the user symbols and channel state information without the aid of pilot sequences. Experimental results demonstrate the effectiveness of the proposed model in various communication scenes and outperforms the baseline systems. approximately 3 dB compared with the ideal receiver. Index Terms-Waveform design, semi-blind receiver, multiple input multiple output (MIMO) system, PARAFAC model, TUCKER-1 model. I. INTRODUCTION C OGNITIVE radio (CR) is an extension of software defined radio (SDR) [1]. It is widely used in military and commercial communications such as next generation networks [2], dynamic spectrum access [3], the IEEE 802.22 standard [4], and multiple input multiple output (MIMO) systems [5]. MIMO has been regarded as a promising technology for the nextgeneration wireless communications [6]. As shown in Fig. 1, a typical cognitive cycle is composed of four aspects: spectrum sensing, spectrum analysis, spectrum decision, and waveform framework. Spectrum decision determines the reconfigurable parameters such as data rate, operating frequency, modulation

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“…Motivated by the good performance of these approaches in the study of bilinear forms, we aim to further extend this approach to higher-order multilinear in parameters' systems. Applications, such as multichannel equalization [12], nonlinear acoustic devices for echo cancellation [13], multiple-input/multiple-output (MIMO) communication systems [14,15] and others, can be addressed within the framework of multilinear systems. Because many of these applications can be formulated in terms of system identification problems [16], it is of interest to estimate a model based on the available and observed data, which are usually the input and the output of the system.…”

Section: Introductionmentioning

confidence: 99%

System Identification Based on Tensor Decompositions: A Trilinear Approach

et al. 2019

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The theory of nonlinear systems can currently be encountered in many important fields, while the nonlinear behavior of electronic systems and devices has been studied for a long time. However, a global approach for dealing with nonlinear systems does not exist and the methods to address this problem differ depending on the application and on the types of nonlinearities. An interesting category of nonlinear systems is one that can be regarded as an ensemble of (approximately) linear systems. Some popular examples in this context are nonlinear electronic devices (such as acoustic echo cancellers, which are used in applications for two-party or multi-party voice communications, e.g., videoconferencing), which can be modeled as a cascade of linear and nonlinear systems, similar to the Hammerstein model. Multiple-input/single-output (MISO) systems can also be regarded as separable multilinear systems and be treated using the appropriate methods. The high dimension of the parameter space in such problems can be addressed with methods based on tensor decompositions and modelling. In recent work, we focused on a particular type of multilinear structure—namely the bilinear form (i.e., two-dimensional decompositions)—in the framework of identifying spatiotemporal models. In this paper, we extend the work to the decomposition of more complex systems and we propose an iterative Wiener filter tailored for the identification of trilinear forms (where third-order tensors are involved), which can then be further extended to higher order multilinear structures. In addition, we derive the least-mean-square (LMS) and normalized LMS (NLMS) algorithms tailored for such trilinear forms. Simulations performed in the context of system identification (based on the MISO system approach) indicate the good performance of the proposed solution, as compared to conventional approaches.

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Tensor modelling of MIMO communication systems with performance analysis and Kronecker receivers

Cited by 51 publications

References 28 publications

Tensor methods for multisensor signal processing

Tensor methods for multisensor signal processing

Tensor Communication Waveform Design With Semi-Blind Receiver in the MIMO System

System Identification Based on Tensor Decompositions: A Trilinear Approach

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