Abstract
The efficient design of compact antennas operating over multiple bands suitable for the Internet of Things (IoT) is addressed by means of an instance of the system-by-design (SbD) paradigm. More specifically, an iterative strategy that combines different software modules for the search spaceexploration, the fast physicalmodeling of the radiators, and the quality evaluation of the guess solutions is proposed. To enable such a SbD instance, an innovative strategy that exploits an orthogonal array (OA) scheme to determine the training set of a Learning-by-Example (LBE) algorithm based on a support vector regressor (SVR) is introduced for the efficient physicalmodeling of the layout to be optimized. The features and the potentialities of the proposed methodological approach are assessed in different applicative scenarios by considering representative numerical and experimental validation examples.
Highlights
1 Introduction and rationale Nowadays, the ever-growing development of the Internet of Things/Internet of Everything (IoT/IoE) is rapidly pushing the concept of “pervasive intelligence” [1,2,3] to an unprecedented level, in which several small and relatively cheap objects of our daily lives will be densely interconnected through wireless machine-to-machine (M2M) interactions [4]
3rd Generation Partnership Project (3GPP) recently released in June 2016 the first version of the NarrowBandIoT (NB-IoT) [5, 6]
5 Conclusions The problem of efficiently designing multiband antennas has been addressed by means of an instance of the SbD paradigm
Summary
A POA block that evaluates the matching between the desired and the resonant frequencies of the trial multiband antenna, g, by computing the value of (g), which takes into account the physical/application constraints. After an off-line training phase performed to learn the input-output relations of the system to be emulated (Section 3.2.1) starting from a set of T training set couples g(t), fn g(t) , t = 1, ..., T, n = 1, ..., N, a fast online testing phase is carried out to emulate the physical system itself (Section 3.2.2) Towards this end, the relation between the estimated resonant frequencies, fn (g), n = 1, ..., N, and the antenna parameters g is modelled as [46]: T fn (g) =.
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