Towards Holographic Beam-Forming Metasurface Technology for Next Generation CubeSats

Yurduseven, Okan; Podilchak, Symon K.; Khalily, Mohsen

doi:10.1109/ucet51115.2020.9205418

Cited by 6 publications

(7 citation statements)

References 38 publications

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“…In [30], the authors designed a microwave imaging system to reconstruct the target leveraging computational imaging methods and holographic beamforming. A metasurface antenna on the satellite platform was proposed with a view to performing holographic beamforming for the next-generation satellite communication [31]. However, both [30], [31] works did not represent any technical contribution towards the hybrid beamforming scheme or holographic beamforming.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Integrated Sensing, Localization, and Communication in Holographic MIMO-enabled Wireless Network: A Deep Learning Approach

ADHIKARY¹,

Munir²,

Raha³

et al. 2023

Preprint

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<p>The impending sixth-generation wireless communication networks are anticipated to guarantee mass connectivity, high integration, and lower power consumption for generating the required beamforming. To achieve these goals, an artificial intelligence (AI) framework is proposed by utilizing holographic MIMO-assisted integrated sensing, localization, and communication. The proposed AI framework ensures lower power consumption to activate the minimum number of grids from the holographic grid array for the generation of holographic beamforming. An optimization problem is formulated to maximize the signal-to-interference-plus-noise ratio received by the users, which in turn maximizes the utility function for sensing considering the user distances, beampattern gains, sensingcommunication loss, and dense locations controlling parameter. A novel AI-based framework is proposed to solve the formulated NP-hard optimization problem by decomposing it into two subproblems: the sensing problem and the communication resource allocation problem. First, a variational autoencoder (VAE) based mechanism is devised to solve the sensing problem mitigating the disputes to obtain the users’ exact location. Second, a sequential neural network-based scheme is utilized to allocate the communication resources to the heterogeneous users for generating the desired beamforming based on the findings of the VAE-based mechanism. Moreover, an extreme case power allocation strategy is presented once a large number of users enter the system. The extreme case power allocation strategy applies when the total power prediction exceeds the total system power for allocating the communication resources to the users. Finally, simulation results validate that the proposed AI-based framework outperforms the long short-term memory method with a cumulative power savings of 34.02% taking the ground truth power into account. Therefore, the proposed AI framework generates effective beamforming to serve the communication users.</p>

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Section: Related Workmentioning

confidence: 99%

“…A metasurface antenna on the satellite platform was proposed with a view to performing holographic beamforming for the next-generation satellite communication [31]. However, both [30], [31] works did not represent any technical contribution towards the hybrid beamforming scheme or holographic beamforming.…”

Section: Related Workmentioning

confidence: 99%

Integrated Sensing, Localization, and Communication in Holographic MIMO-enabled Wireless Network: A Deep Learning Approach

ADHIKARY¹,

Munir²,

Raha³

et al. 2023

Preprint

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“…Existing works have proposed various metamaterial enabled holographic beamforming schemes for application to wireless communications [26], [27], imaging [28], and satellite applications [29]. In [26], the authors consider the downlink transmission between a base station (BS) equipped with an RHS and multiple users, and the sum rate of the communication system is maximized via holographic beamforming under a total power constraint at the BS.…”

Section: Introductionmentioning

confidence: 99%

“…The authors of [28] develop a microwave imaging system which leverages holographic beamforming and computational imaging techniques for target reconstruction. A metasurface antenna which performs holographic beamforming on a satellite platform is designed in [29]. However, these schemes only consider the beamsteering capabilities for communication, imaging, or satellite applications, and thus they cannot be directly applied to ISAC systems.…”

Section: Introductionmentioning

confidence: 99%

Holographic Integrated Sensing and Communication

Zhang

et al. 2022

IEEE J. Select. Areas Commun.

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To overcome spectrum congestion, a promising approach is to integrate sensing and communication (ISAC) functions in one hardware platform. Recently, metamaterial antennas, whose tunable radiation elements are arranged more densely than those of traditional multiple-input-multiple-output (MIMO) arrays, have been developed to enhance the sensing and communication performance by offering a finer controllability of the antenna beampattern. In this paper, we propose a holographic beamforming scheme, which is enabled by metamaterial antennas with tunable radiated amplitudes, that jointly performs sensing and communication. However, it is challenging to design the beamformer for ISAC functions by taking into account the unique amplitude-controlled structure of holographic beamforming. To address this challenge, we formulate an integrated sensing and communication problem to optimize the beamformer, and design a holographic beamforming optimization algorithm to efficiently solve the formulated problem. A lower bound for the maximum beampattern gain is provided through theoretical analysis, which reveals the potential performance enhancement gain that is obtained by densely deploying several elements in a metamaterial antenna. Simulation results substantiate the theoretical analysis and show that the maximum beamforming gain of a metamaterial antenna that utilizes the proposed holographic beamforming scheme can be increased by at least 50% compared with that of a traditional MIMO array of the same size. In addition, the cost of the proposed scheme is lower than that of a traditional MIMO scheme while providing the same ISAC performance.

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“…The efficiency of applying DRL to RIS-assisted wireless communication systems has been demonstrated in simulation experiments in the relevant literature. Related meta-surface techniques, including RIS, have also recently been widely used for IS-HAP-TNs [ 33 , 34 , 35 , 36 ].…”

Section: Introductionmentioning

confidence: 99%

Joint Beamforming Design for RIS-Assisted Integrated Satellite-HAP-Terrestrial Networks Using Deep Reinforcement Learning

Zhu

et al. 2023

Sensors

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In this paper, we consider reconfigurable intelligent surface (RIS)-assisted integrated satellite high-altitude platform terrestrial networks (IS-HAP-TNs) that can improve network performance by exploiting the HAP stability and RIS reflection. Specifically, the reflector RIS is installed on the side of HAP to reflect signals from the multiple ground user equipment (UE) to the satellite. To aim at maximizing the system sum rate, we jointly optimize the transmit beamforming matrix at the ground UEs and RIS phase shift matrix. Due to the limitation of the unit modulus of the RIS reflective elements constraint, the combinatorial optimization problem is difficult to tackle effectively by traditional solving methods. Based on this, this paper studies the deep reinforcement learning (DRL) algorithm to achieve online decision making for this joint optimization problem. In addition, it is verified through simulation experiments that the proposed DRL algorithm outperforms the standard scheme in terms of system performance, execution time, and computing speed, making real-time decision making truly feasible.

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Towards Holographic Beam-Forming Metasurface Technology for Next Generation CubeSats

Cited by 6 publications

References 38 publications

Integrated Sensing, Localization, and Communication in Holographic MIMO-enabled Wireless Network: A Deep Learning Approach

Integrated Sensing, Localization, and Communication in Holographic MIMO-enabled Wireless Network: A Deep Learning Approach

Holographic Integrated Sensing and Communication

Joint Beamforming Design for RIS-Assisted Integrated Satellite-HAP-Terrestrial Networks Using Deep Reinforcement Learning

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