Tensor Decompositions in Wireless Communications and MIMO Radar

Chen, Hongyang; Ahmad, Fauzia; Vorobyov, Sergiy A.; Porikli, Fatih

doi:10.1109/jstsp.2021.3061937

Cited by 62 publications

(30 citation statements)

References 109 publications

(111 reference statements)

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“…For a pedagogical purpose, we first introduce the basics of tensors and tensor canonical polyadic decomposition (CPD), the most fundamental tensor decomposition model in unsupervised learning. in social network analysis [43], "blind source separation" in electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI) data analysis [44], [45], and "blind signal estimation" in radar/sonar signal processing [46]. In these applications, tensor CPD has been proven to be a powerful tool with good interpretability.…”

Section: Sparsity-aware Modeling For Tensor Decompositionsmentioning

confidence: 99%

Rethinking Bayesian Learning for Data Analysis: The Art of Prior and Inference in Sparsity-Aware Modeling

Liu¹,

Yin²,

Chatzis³

et al. 2022

Preprint

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Sparse modeling for signal processing and machine learning, in general, has been at the focus of scientific research for over two decades. Among others, supervised sparsity-aware learning comprises two major paths paved by: a) discriminative methods that establish direct input-output mapping based on a regularized cost function optimization, and b) generative methods that learn the underlying distributions.The latter, more widely known as Bayesian methods, enable uncertainty evaluation with respect to the performed predictions. Furthermore, they can better exploit related prior information and also, in principle, can naturally introduce robustness into the model, due to their unique capacity to marginalize out uncertainties related to the parameter estimates. Moreover, hyper-parameters (tuning parameters) associated with the adopted priors, which correspond to cost function regularizers, can be learnt via the training data and not via costly cross-validation techniques, which is, in general, the case with the discriminative methods. To implement sparsity-aware learning, the crucial point lies in the choice of the function regularizer for discriminative methods and the choice of the prior distribution for Bayesian learning. Over the last decade or so, due to the intense research on deep learning, emphasis has been put on discriminative techniques. However, a come back of Bayesian methods is taking place that sheds new light on the design of deep neural networks, which also establish firm links with Bayesian models, such Lei Cheng and Feng Yin contribute equally.

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Section: Sparsity-aware Modeling For Tensor Decompositionsmentioning

confidence: 99%

Rethinking Bayesian Learning for Data Analysis: The Art of Prior and Inference in Sparsity-Aware Modeling

Liu¹,

Yin²,

Chatzis³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…A receiver based on (18) assumes that the channel is known. However, even in the absence of complete channel state information, the structure of the received signal can be exploited through several tensor decomposition schemes for designing blind/semi-blind receivers [11,12,14]. Such receivers can perform joint symbol and channel estimation.…”

Section: Applications Of Other Tensor Decompositionsmentioning

confidence: 99%

“…A short summary of a few of the important tensor tools along with some applications can be found in Table 4. More details on various tensor decompositions and their numerous applications can be found in [14,21,38,39]. A tensor singular value decomposition (SVD) is described in [15] which decomposes a tensor into two unitary tensors and a pseudo-diagonal tensor connected via the Einstein product.…”

Section: Applications Of Other Tensor Decompositionsmentioning

confidence: 99%

“…In the past decade, the use of tensors for modeling communication systems has gained much attention for analysis and the improvement of system performance, as it allows a consolidated representation of multiple signaling domains [9][10][11][12][13][14]. In this paper, we show that tensors can be used for signals and systems representation, and tools from multilinear algebra, such as the Einstein product, can be employed for developing a generic system model.…”

Section: Introductionmentioning

confidence: 98%

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Multi-Domain Communication Systems and Networks: A Tensor-Based Approach

Pandey

Venugopal

Leib

2021

Network

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Most modern communication systems, such as those intended for deployment in IoT applications or 5G and beyond networks, utilize multiple domains for transmission and reception at the physical layer. Depending on the application, these domains can include space, time, frequency, users, code sequences, and transmission media, to name a few. As such, the design criteria of future communication systems must be cognizant of the opportunities and the challenges that exist in exploiting the multi-domain nature of the signals and systems involved for information transmission. Focussing on the Physical Layer, this paper presents a novel mathematical framework using tensors, to represent, design, and analyze multi-domain systems. Various domains can be integrated into the transceiver design scheme using tensors. Tools from multi-linear algebra can be used to develop simultaneous signal processing techniques across all the domains. In particular, we present tensor partial response signaling (TPRS) which allows the introduction of controlled interference within elements of a domain and also across domains. We develop the TPRS system using the tensor contracted convolution to generate a multi-domain signal with desired spectral and cross-spectral properties across domains. In addition, by studying the information theoretic properties of the multi-domain tensor channel, we present the trade-off between different domains that can be harnessed using this framework. Numerical examples for capacity and mean square error are presented to highlight the domain trade-off revealed by the tensor formulation. Furthermore, an application of the tensor framework to MIMO Generalized Frequency Division Multiplexing (GFDM) is also presented.

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“…See Appendix A for more details. These beamforming vectors (i.e., columns of W) together (i.e., whole W) define the DFT-based TB in slow-time [27]. It has the same application features as the previous described TB technique.…”

Section: B Ddma Approachmentioning

confidence: 99%

Transmit Beamspace DDMA Based Automotive MIMO Radar

Xu¹,

Vorobyov²,

Yang³

2021

Preprint

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The time division multiple access (TDMA) technique has been applied in automotive multiple-input multiple-output (MIMO) radar. However, it suffers from the transmit energy loss, and as a result the parameter estimation performance degradation when the number of transmit elements increases. To tackle these problem, a transmit beamspace (TB) Doppler division multiple access (DDMA) approach is proposed. First, a phase modulation matrix with empty Doppler spectrum is introduced. By exploiting the empty Doppler spectrum, a test function based on sequential detection is developed to mitigate the Doppler ambiguity in DDMA waveform. Then, a discrete Fourier transform (DFT)-based TB in slow-time is formed. The proposed method can achieve waveform diversity in Doppler domain and generate a TB in slow-time that concentrates the transmitted power in a fixed spatial region to improve the transmit energy distribution for automotive MIMO radar, which is favored by medium/long range radar (MRR/LRR) applications. As compared to the conventional TDMA technique, the proposed TB DDMA approach can fully exploit the transmission capabilities of all transmit elements to ensure that the emitted power is efficiently used and inherits easy implementation. Moreover, the proposed TB DDMA method avoids the trade-off between the active time for each transmit antenna and the frame time. Simulation results verify the effectiveness of the proposed TB DDMA approach for automotive MIMO radar.

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Tensor Decompositions in Wireless Communications and MIMO Radar

Cited by 62 publications

References 109 publications

Rethinking Bayesian Learning for Data Analysis: The Art of Prior and Inference in Sparsity-Aware Modeling

Rethinking Bayesian Learning for Data Analysis: The Art of Prior and Inference in Sparsity-Aware Modeling

Multi-Domain Communication Systems and Networks: A Tensor-Based Approach

Transmit Beamspace DDMA Based Automotive MIMO Radar

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