Deep robust regression

Diskin, Tzvi; Drašković, Gordana; Pascal, Frédéric; Wiesel, Ami

doi:10.1109/camsap.2017.8313200

Cited by 11 publications

(2 citation statements)

References 11 publications

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“…Parts of our work on MIMO detection using deep learning have already appeared in [28], see also [29]. Similar ideas were discussed in [30] in the context of robust regression.…”

Section: B Background On Machine Learningmentioning

confidence: 64%

Learning to Detect

Samuel

Diskin

Wiesel

2019

IEEE Trans. Signal Process.

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447

352

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In this paper we consider Multiple-Input-Multiple-Output (MIMO) detection using deep neural networks. We introduce two different deep architectures: a standard fully connected multi-layer network, and a Detection Network (DetNet) which is specifically designed for the task. The structure of DetNet is obtained by unfolding the iterations of a projected gradient descent algorithm into a network. We compare the accuracy and runtime complexity of the purposed approaches and achieve state-of-the-art performance while maintaining low computational requirements. Furthermore, we manage to train a single network to detect over an entire distribution of channels. Finally, we consider detection with soft outputs and show that the networks can easily be modified to produce soft decisions.

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“…Parts of our work on MIMO detection using deep learning have already appeared in [28], see also [29]. Similar ideas were discussed in [30] in the context of robust regression.…”

Section: B Background On Machine Learningmentioning

confidence: 64%

Learning to Detect

Samuel

Diskin

Wiesel

2019

IEEE Trans. Signal Process.

Self Cite

447

352

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“…The received signal Y undergoing the same pre-processing as in Section III-A3) forms the input of the NN and the aim here is to accurately estimate the value of the extrinsic LLR L i,e (u). Hence, we harness the classic regression model of [61] formulated as:…”

Section: B Soft-decision Detectionmentioning

confidence: 99%

Deep Learning-Based Soft Iterative-Detection of Channel-Coded Compressed Sensing-Aided Multi-Dimensional Index Modulation

Feng

El‐Hajjar

et al. 2023

IEEE Trans. Veh. Technol.

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The concept of Index Modulation (IM) has been actively researched as a benefit of its flexible trade-off between performance, achievable rate, energy efficiency, hardware cost and complexity. In order to fully exploit its the degrees of freedom, the concept of Multi-dimensional IM (MIM) has been developed in literature, where Compressed Sensing (CS) is often utilized to exploit the sparsity of the multi-dimensional transmitted signals. However, this flexibility and performance gains are attained at the cost of a substantially increased detection complexity. In this paper, we propose Deep Learning (DL) based detection for CS-aided MIM (CS-MIM), where both Hard-Decision (HD) as well as Soft-Decision (SD) detection combined with iterative decoding are conceived. More explicitly, firstly, we propose learning aided hard and soft detection for CS-MIM. Secondly, two novel neural network aided methods are proposed for Iterative Soft Detection (ISD), where iterations are carried out between the CS-MIM detector and a channel decoder. In contrast to the conventional detection of CS-MIM system, which critically relies on the knowledge of Channel State Information (CSI) at the receiver, the proposed learning-aided methods are capable of eliminating the overhead and complexity of Channel Estimation (CE), which results in an improved transmission rate. Explicitly, we develop an advanced DL architecture for blind-detectionaided MIM for the first time in the open literature, where the HD and SD CS algorithms are implemented by learning without the need for CE. Our simulation results demonstrate that the proposed learning techniques conceived for SD CS-MIM combined with iterative detection are capable of achieving nearcapacity performance at a reduced complexity compared to the conventional model-based SD relying on CSI acquisition.

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Robust Losses in Deep Regression

Rubio,

Dorronsoro

2023

Lecture Notes in Computer Science

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Deep robust regression

Cited by 11 publications

References 11 publications

Learning to Detect

Learning to Detect

Deep Learning-Based Soft Iterative-Detection of Channel-Coded Compressed Sensing-Aided Multi-Dimensional Index Modulation

Robust Losses in Deep Regression

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