A machine learning technique is applied to the design and optimization of reflectarray antennas to considerably accelerate computing time without compromising accuracy. In particular, Support Vector Machines (SVMs), automatic learning structures that are able to deal with regression problems, are employed to obtain surrogate models of the reflectarray element to substitute the full-wave analysis tool for the characterization of the unit cell in the design and optimization algorithms. The analysis, design and optimization of a very large reflectarray antenna for Direct Broadcast Satellite applications are accelerated up to three orders of magnitude. This is here demonstrated with three examples: one showing the design of a reflectarray; and two for the crosspolar optimization, one with one coverage for each linear polarization (Europe and the Middle East) and another with a Middle East coverage working in dual-linear polarization. The accuracy of the proposed approach is validated by means of a comparison of the final designs with full-wave simulations based on local periodicity obtaining good agreement. The crosspolar dicrimination and crosspolar isolation are greatly improved using the SVMs while considerably reducing computing time.

Real-time industrial applications in the scope of the industry 4.0. present significant challenges from the communication perspective: low latency, ultra-reliability, and determinism. Given that wireless networks provide a significant cost reduction, lower deployment time, and free movement of the wireless nodes, wireless solutions have attracted the industry attention. However, industrial networks are mostly built by wired means because state-of-the-art wireless networks cannot cope with the industrial applications requirements. In this paper, we present the hardware implementation of wireless SHARP (w-SHARP), a promising wireless technology for real-time industrial applications. w-SHARP follows the principles of Time-Sensitive Networking and provides time synchronization, time-aware scheduling with bounded latency, and high reliability. The implementation has been carried out on a Field Programmable Gate Array-based Software Defined Radio platform. We demonstrate, through a hardware testbed, that w-SHARP is able to provide ultra-low control cycles, low latency, and high reliability. This implementation may open new perspectives in the implementation of high-performance industrial wireless networks, as both PHY and MAC layers are now subject to be optimized for specific industrial applications.

In recent years, machine learning techniques (MLTs) have been applied to accelerate analysis and design of electromagnetic devices. Algorithms such as artificial neural networks or support vector machines for regression (SVRs) have been proposed for the design of large reflectarrays for space applications at a single frequency. However, multi-frequency optimization of such large antennas has not been tackled with MLTs. In this letter and for the first time, we propose a technique based on the use of SVR analysis to obtain the reflection coefficients to accelerate the design of a very large shaped-beam reflectarray for direct broadcast satellite in a 15% bandwidth. An in-house method of moments based on local periodicity is employed to generate samples to train the SVRs for each considered frequency. Then, the surrogate model is used for a design at central frequency, which is used as starting point for a wideband design procedure that is accelerated more than an order of magnitude without a significant loss of accuracy. It is shown that, by virtue of the proposed methodology, the minimum copolar gain in the coverage zone is improved more than 10dB at the upper frequency while maintaining a computationally efficient design procedure.

The time-sensitive network paradigm envisions the integration of operation technology and information technology in the same network. One of the requirements for building time-sensitive networks is sharing a global time along the network. This requirement is especially critical in wireless systems, where there are few robust methods to perform accurate time transfer. In this article, the problem of time transfer over realistic wireless channels is studied, and a time distribution scheme is proposed. The time distribution scheme has three components: Precision time protocol, a novel timestamping method (enhanced timestamps) and an algorithm to implement the enhanced timestamps. The performance of the proposed scheme has been evaluated in MATLAB using the IEEE 802.11n Standard under several standard wireless local area network channel models. The results show that the system can reach subnanosecond time transfer accuracy under non-line-of-sight and time-variant conditions, but its performance greatly depends on the signal-to-noise ratio and on the channel variation rate.

An optimisation framework for near field multifocusing on antenna arrays is proposed. Its aim is to obtain the feeding weights that must be applied to the elements of the array so that the antenna focuses on two or more targets simultaneously. The method is based on the definition of a proper cost function, which is minimised using an optimisation algorithm over an equivalent least squares problem. The success of the technique is noted in the fact that different array parameters can be obtained: magnitude and phase of the array elements or only phase synthesis. In order to show the efficiency of the method, several tests are carried out using different arrays and optimisation parameters. The simulation, manufacture and measurement of an 8 × 8 planar array able to focus on two different targets is also performed. Results show how the framework can be applied to general arrays and demanding requirements, so it may be easily extended to different synthesis problems besides multifocusing.

A common approach in the literature when obtaining surrogate models of reflectarray unit cells is to include, among other variables, the angles of incidence as input variables to the model. In this work, we use support vector regression (SVR) to compare this approach with a new strategy which consists in grouping the refletarray elements under a small set of angles of incidence and train surrogate models per angle of incidence pair. In this case, the dimensionality of the SVR decreases in two with regard to the former approach. In both cases, two geometrical variables are considered for reflectarray design, obtaining 4D and 2D SVRs, respectively. In contrast to the common approach in the literature, the comparison between the 4D and 2D SVRs shows that a proper discretization of the angles of incidence is more competitive than introducing the angles as input variables in the SVR. The 2D SVR offers a shorter training time, faster reflectarray analysis, and a similar accuracy than the 4D SVR, making it more suitable for design and optimization procedures.