Machine Learning in Antenna Design: An Overview on Machine Learning Concept and Algorithms

Misilmani, Hilal M. El; Naous, Tarek

doi:10.1109/hpcs48598.2019.9188224

Cited by 83 publications

(44 citation statements)

References 32 publications

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“…More recently, Tom Mitchell defined it as: -A computer program is said to learn from experience E with respect to some task T and some performance measure P, if its performance on T, as measured by P, improves with experience E [28]. Generally, ML can be divided into three key categories: Supervised Learning, Unsupervised Learning, and Reinforcement Learning (RL) [29]. Table 1 summarizes the three categories and provides a brief description of each.…”

Section: B Overview Of Machine Learningmentioning

confidence: 99%

“…In supervised learning what a correct output looks like is already known [29]. Fundamentally, a learning algorithm is trained on a given data set, after which it generalizes to give accurate predictions to all possible inputs [37].…”

Section: Learning Categorymentioning

confidence: 99%

“…Conversely, in unsupervised learning, the algorithm derives a structure from the data after identifying similarities in the inputs. What the results should look like is not known [29].…”

Section: Figure 3 Artificial Neural Networkmentioning

confidence: 99%

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Applications and Challenges of Artificial Intelligence in Space Missions

2024

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Artificial Intelligence (AI) is increasingly finding acceptance in the space community, especially Machine Learning (ML), a subdomain of AI. ML algorithms now find numerous applications in autonomous navigation, spacecraft health monitoring and operational management of satellite constellations. However, a large number of surveys on the applications of AI in space missions can be classified into two categories. The first category suffers from the limitation of being old and not covering some crucial and recent developments in the field; such as the contributions of Deep Learning (DL) and bioinspired AI algorithms. The challenge with the second category lies in its being too detailed with respect to the development and application of specific AI techniques or algorithms. These limitations have necessitated the need to have a concise survey with a wider scope for those interested in the applications and challenges of AI in the space industry, especially those with technical backgrounds in other fields. In this paper, we surveyed the use of traditional AI techniques in various domains of space missions without delving into formal methods. Some bioinspired AI algorithms were also surveyed and their potential application areas highlighted. Unlike similar surveys that focus only on technological challenges, we also addressed some crucial legal drawbacks that emanate from the reliance and use of AI in space. Summarily, while discussing future directions we reviewed some advancements in Internet of Things (IoT) and Blockchain technologies. Our review prioritized three application domains positioned to benefit immensely from the inevitable AI-Blockchain convergence in the space community. These include the Internet of Space Things (IoST), Satellite Communication and Spacecraft Data Security.

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Section: B Overview Of Machine Learningmentioning

confidence: 99%

Section: Learning Categorymentioning

confidence: 99%

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Applications and Challenges of Artificial Intelligence in Space Missions

2024

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“…El Misilmani, H. M, et al., [11] explained that the change in the size and variety of data available, as well as machine learning is attracting much attention in terms of securing the correct strategy in a number of fields, thanks to sophisticated computing and effective data storage. Algorithms are a huge part of the ongoing studies, and they're predicted to play an important role in current technology.The applications of machine learning in antenna creation are introduced and investigated in this paper.…”

Section: Figure 2 Machine Learning -Architecturementioning

confidence: 99%

Implementation of dual-band Planar Inverted F- Antenna (PIFA) using machine learning (ML) for 5G mobile applications

Padmapriya¹,

Manikanthan²

2021

Proceedings of the First International Conference on Computing, Communication and Control System, I3CAC 2021, 7-8 June 2021, Bh

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This paper proposed a new hybrid model for feeding printed PIFA antennas with the dual frequency ranges of 29GHz and 32GHz, which are optimal for 5G mobile communications. A two-element rectangular PIFA with an inset feed for the 29GHz and 32GHz bands is the first prototype in this initiative.To create The second program involves PIFA inset-fed lines and is symmetric dual-band two-element slotted rectangular patches. Inverted I-shaped slots in primary patches could be used to gain dual-band response. For 5G portable apps, hybrid algorithms are used.Bayesian convolution and the genetic algorithm are the two algorithms. These two algorithms are useful for wireless 5G applications. The third prototype is an asymmetric PIFA antenna with inverted I-shaped slotted perpendicular patches in dual service. The partial rectangular ground plane is inserted with the slot-formed DGS. The substrate has a width of 52074mm2, and the integrated antennas have very limited planar configurations occupy hardly any room, making them easier to fit into handset devices for the upcoming 5G mobile communications. Return demand drops, and bandwidths are diminished.Without any supplementary constructions, PIFA has a small mutual coupling. By using these two algorithms the antenna systems have directivity, gain, and providing effective values that are perfect for 5G mobile applications, as well as expected reflections and interactive application characteristics.

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“…Recently, machine learning has been applied to optimize antenna performance [13][14][15][16][17][18][19] and implement impedance matching [20][21][22]. A machine learning method can determine the element values without requiring a mathematical description of the matching circuit.…”

Section: Introductionmentioning

confidence: 99%

Antenna Impedance Matching Using Deep Learning

Kim

Bang

2021

Sensors

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We propose a deep neural network (DNN) to determine the matching circuit parameters for antenna impedance matching. The DNN determines the element values of the matching circuit without requiring a mathematical description of matching methods, and it approximates feasible solutions even for unimplementable inputs. For matching, the magnitude and phase of impedance should be known in general. In contrast, the element values of the matching circuit can be determined only using the impedance magnitude using the proposed DNN. A gamma-matching circuit consisting of a series capacitor and a parallel capacitor was applied to a conventional inverted-F antenna for impedance matching. For learning, the magnitude of input impedance S11 of the antenna was extracted according to the element values of the matching circuit. A total of 377 training samples and 66 validation samples were obtained. The DNN was then constructed considering the magnitude of impedance S11 as the input and the element values of the matching circuit as the output. During training, the loss converged as the number of epochs increased. In addition, the desired matching values for unlearned square and triangular waves were obtained during testing.

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Machine Learning in Antenna Design: An Overview on Machine Learning Concept and Algorithms

Cited by 83 publications

References 32 publications

Applications and Challenges of Artificial Intelligence in Space Missions

Applications and Challenges of Artificial Intelligence in Space Missions

Implementation of dual-band Planar Inverted F- Antenna (PIFA) using machine learning (ML) for 5G mobile applications

Antenna Impedance Matching Using Deep Learning

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