Multi-Discriminator Distributed Generative Model for Multi-Layer RF Metasurface Discovery

Hodge, John A.; Mishra, Kumar Vijay; Zaghloul, Amir I.

doi:10.1109/globalsip45357.2019.8969135

Cited by 28 publications

(15 citation statements)

References 27 publications

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“…The generator takes as input a concatenation of 101 equally spaced spectral points between 400 and 800 nm of the optical response (R) and 411 Gaussian noise samples, for a total of 512 dimensional input. The choice of noise vectors for generative models 11,25,30 does not correlate with the data samples needed for the training, since the cGAN model learns the same probabilistic distribution of EM simulation datasets (see Supplementary Information Section VII). During training, G learns about the conditional probability distribution of Al-nanoantennae structural design space and produces a 2D cross sectional image given an optical response as input.…”

Section: Methodsmentioning

confidence: 99%

A cyclical deep learning based framework for simultaneous inverse and forward design of nanophotonic metasurfaces

Mall

Patil

Sethi

et al. 2020

Sci Rep

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The conventional approach to nanophotonic metasurface design and optimization for a targeted electromagnetic response involves exploring large geometry and material spaces. This is a highly iterative process based on trial and error, which is computationally costly and time consuming. Moreover, the non-uniqueness of structural designs and high non-linearity between electromagnetic response and design makes this problem challenging. To model this unintuitive relationship between electromagnetic response and metasurface structural design as a probability distribution in the design space, we introduce a framework for inverse design of nanophotonic metasurfaces based on cyclical deep learning (DL). The proposed framework performs inverse design and optimization mechanism for the generation of meta-atoms and meta-molecules as metasurface units based on DL models and genetic algorithm. The framework includes consecutive DL models that emulate both numerical electromagnetic simulation and iterative processes of optimization, and generate optimized structural designs while simultaneously performing forward and inverse design tasks. A selection and evaluation of generated structural designs is performed by the genetic algorithm to construct a desired optical response and design space that mimics real world responses. Importantly, our cyclical generation framework also explores the space of new metasurface topologies. As an example application of the utility of our proposed architecture, we demonstrate the inverse design of gap-plasmon based half-wave plate metasurface for user-defined optical response. Our proposed technique can be easily generalized for designing nanophtonic metasurfaces for a wide range of targeted optical response.

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Section: Methodsmentioning

confidence: 99%

A cyclical deep learning based framework for simultaneous inverse and forward design of nanophotonic metasurfaces

Mall

Patil

Sethi

et al. 2020

Sci Rep

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“…The DL is capable of uncovering complex relationships in data/signals and, thus, can achieve better performance. This has been demonstrated in several successful applications of DL in wireless communications problems such as channel estimation [31], [32], analog beam selection [33], [34], and also hybrid beamforming [33], [35]- [39]. In particular, DL-based techniques have been shown [32], [37], [38], [40], [41] to be computationally efficient in searching for optimum beamformers and tolerant to imperfect channel inputs when compared with the conventional methods,.…”

Section: Arxiv:191210036v1 [Eesssp] 20 Dec 2019mentioning

confidence: 96%

Deep Learning Design for Joint Antenna Selection and Hybrid Beamforming in Massive MIMO

Elbir

Mishra

2019

2019 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting

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Hybrid analog and digital beamforming transceivers are instrumental in addressing the challenge of expensive hardware and high training overheads in the next generation millimeter-wave (mm-Wave) massive MIMO (multiple-input multiple-output) systems. However, lack of fully digital beamforming in hybrid architectures and short coherence times at mm-Wave impose additional constraints on the channel estimation. Prior works on addressing these challenges have focused largely on narrowband channels wherein optimization-based or greedy algorithms were employed to derive hybrid beamformers. In this paper, we introduce a deep learning (DL) approach for joint channel estimation and hybrid beamforming for frequencyselective, wideband mm-Wave systems. In particular, we consider a massive MIMO Orthogonal Frequency Division Multiplexing (MIMO-OFDM) system and propose three different DL frameworks comprising convolutional neural networks (CNNs), which accept the received pilot signal as input and yield the hybrid beamformers at the output. Numerical experiments demonstrate that, compared to the current state-of-the-art optimization and DL methods, our approach provides higher spectral efficiency, lesser computational cost, and higher tolerance against the deviations in the received pilot data, corrupted channel matrix, and propagation environment.Index Terms-Channel estimation, deep learning, hybrid beamforming, mm-Wave, wideband massive MIMO.

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“…The use of DNNs is expected to substantially reduce computational complexity and processing overhead since it only requires several layers of simple operations such as matrix-vector multiplications. Moreover, several successful DL applications have been demonstrated in wireless communications problems such as channel estimation [13]- [22], analog beam selection [23], [24], and hybrid beamforming [23], [25]- [29]. Besides, DL-based techniques, when compared with other conventional optimization methods, have been shown [14], [27], [28], [30] to be more computationally efficient in searching for beamformers and more tolerant to imperfect channel inputs.…”

Section: A Related Workmentioning

confidence: 99%

Deep Learning Based Frequency-Selective Channel Estimation for Hybrid mmWave MIMO Systems

Abdallah¹,

Çelik²,

Mansour³

2021

Preprint

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Millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems typically employ hybrid mixed signal processing to avoid expensive hardware and high training overheads. However, the lack of fully digital beamforming at mmWave bands imposes additional challenges in channel estimation. Prior art on hybrid architectures has mainly focused on greedy optimization algorithms to estimate frequency-flat narrowband mmWave channels, despite the fact that in practice, the large bandwidth associated with mmWave channels results in frequency-selective channels. In this paper, we consider a frequency-selective wideband mmWave system and propose two deep learning (DL) compressive sensing (CS) based algorithms for channel estimation. The proposed algorithms learn critical apriori information from training data to provide highly accurate channel estimates with low training overhead. In the first approach, a DL-CS based algorithm simultaneously estimates the channel supports in the frequency domain, which are then used for channel reconstruction. The second approach exploits the estimated supports to apply a low-complexity multi-resolution fine-tuning method to further enhance the estimation performance. Simulation results demonstrate that the proposed DLbased schemes significantly outperform conventional orthogonal matching pursuit (OMP) techniques in terms of the normalized mean-squared error (NMSE), computational complexity, and spectral efficiency, particularly in the low signal-to-noise ratio regime. When compared to OMP approaches that achieve an NMSE gap of {4−10} dB with respect to the Cramer Rao Lower Bound (CRLB), the proposed algorithms reduce the CRLB gap to only {1 − 1.5} dB, while significantly reducing complexity by two orders of magnitude.

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Multi-Discriminator Distributed Generative Model for Multi-Layer RF Metasurface Discovery

Cited by 28 publications

References 27 publications

A cyclical deep learning based framework for simultaneous inverse and forward design of nanophotonic metasurfaces

A cyclical deep learning based framework for simultaneous inverse and forward design of nanophotonic metasurfaces

Deep Learning Design for Joint Antenna Selection and Hybrid Beamforming in Massive MIMO

Deep Learning Based Frequency-Selective Channel Estimation for Hybrid mmWave MIMO Systems

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