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Graph Neural Networks (GNNs) have gained considerable attention in recent years. Despite the surge in innovative GNN architecture designs, research heavily relies on the same 5-10 benchmark datasets for validation. To address this limitation, several generative graph models like ALBTER or GenCAT have emerged, aiming to fix this problem with synthetic graph datasets. However, these models often struggle to mirror the GNN performance of the original graphs. In this work, we present SkyMap, a generative model for labeled attributed graphs with a fine-grained control over graph topology and feature distribution parameters. We show that our model is able to consistently replicate the learnability of graphs on graph convolutional, attention, and isomorphism networks better (64% lower Wasserstein distance) than ALBTER and GenCAT. Further, we prove that by randomly sampling the input parameters of SkyMap, graph dataset constellations can be created that cover a large parametric space, hence making a significant stride in crafting synthetic datasets tailored for GNN evaluation and benchmarking, as we illustrate through a performance comparison between a GNN and a multilayer perceptron.

Abstract This paper introduces the concept of smart radio environments, currently intensely studied for wireless communication in metasurface‐programmable meter‐scaled environments (e.g., inside rooms), on the chip scale. Wireless networks‐on‐chips (WNoCs) are a candidate technology to improve inter‐core communication on chips but current proposals are plagued by a dilemma: either the received signal is weak, or it is significantly reverberated such that the on–off‐keying modulation speed must be throttled. Here, this vexing problem is overcome by endowing the wireless on‐chip environment with in situ programmability which enables the shaping of the channel impulse response (CIR); thereby, a pulse‐like CIR shape can be imposed despite strong multipath propagation and without entailing a reduced received signal strength. First, a programmable metasurface suitable for integration in the on‐chip environment (“on‐chip reconfigurable intelligent surface”) is designed and characterized. Second, its configuration is optimized to equalize selected wireless on‐chip channels “over the air.” Third, by conducting a rigorous communication analysis, the feasibility of significantly higher modulation speeds with shaped CIRs is evidenced. The results introduce a programmability paradigm to WNoCs which boosts their competitiveness as complementary on‐chip interconnect solution.

The miniaturization of transceivers and antennas is enabling the development of Wireless Networks-on-Chip (WNoC), in which chip-scale communication is utilized to increase the computing performance of multi-core/multi-chip architectures. Although the potential benefits of the WNoC paradigm have been studied in depth, its practicality remains unclear due to the lack of a proper characterization of the wireless channel at the chip scale and across the spectrum, among others. In this paper, the state of the art in wave propagation and channel modeling for chip-scale communication is surveyed. First, the peculiarities of WNoC, including the design drivers, architecture, environment, and on-chip electromagnetics are reviewed. After a brief description of the different methods to characterize wave propagation at chip-scales, a comprehensive discussion covering the different works at millimeter-wave (mmWave), Terahertz (THz) and optical frequencies is provided. Finally, the major challenges in the characterization of the WNoC channel and potential solutions to address them are discussed, providing a roadmap for the foundations of practical WNoCs.

Programmable metasurfaces have garnered significant attention as they confer unprecedented control over the electromagnetic (EM) response of any surface. Such feature has given rise to novel design paradigms such as Software-Defined Metamaterials (SDM) and Reconfigurable Intelligent Surfaces (RIS) with multiple groundbreaking applications. However, the development of programmable metasurfaces tailored to the particularities of a potentially large application pool becomes a daunting task because the design space becomes remarkably large. This paper aims to ease the design process by proposing a methodology that employs a semi-analytical formulation to model the response of a metasurface and, then, derives performance scaling trends as functions of a representative set of design and application-specific variables. Although the methodology is amenable to any EM functionality, this paper explores its use for the case of beam steering at 26 GHz for 5G applications. Conventional beam steering metrics are evaluated as functions of the unit cell size, number of unit cell states, and metasurface size for different incidence and reflection angles. It is shown that metasurfaces $5\lambda \times 5 \lambda $ or larger with unit cells of $\lambda /3$ and four unit cell states ensure good performance overall. Further, it is demonstrated that performance degrades significantly for angles larger than $\theta > 60^{o}$ and that, to combat this, extra effort is needed in the development of the unit cell. These performance trends, when combined with power and cost models, will pave the way to optimal metasurface dimensioning.

Software-defined metasurfaces are electromagnetically ultra-thin, artificial components that can provide engineered and externally controllable functionalities. The control over these functionalities is enabled by the metasurface tunability, which is implemented by embedded electronic circuits that modify locally the surface resistance and reactance. Integrating controllers within the metasurface able them to intercommunicate and adaptively reconfigure, thus imparting a desired electromagnetic operation, opens the path towards the creation of an artificially intelligent (AI) fabric where each unit cell can have its own sensing, programmable computing, and actuation facilities. In this work we take a crucial step towards bringing the AI metasurface technology to emerging applications, in particular exploring the wireless mm-wave intercell communication capabilities in a software-defined HyperSurface designed for operation in the microwave regime. We examine three different wireless communication channels within the landscape of the reflective metasurface: Firstly, in the layer where the control electronics of the HyperSurface lie, secondly inside a dedicated layer enclosed between two metallic plates, and, thirdly, inside the metasurface itself. For each case we examine the physical implementation of the mm-wave transceiver nodes, we quantify communication channel metrics, and we identify complexity vs. performance trade-offs.

Metamaterials are artificial structures that have recently enabled the realization of novel electromagnetic components with engineered and even unnatural functionalities. Existing metamaterials are specifically designed for a single application working under preset conditions (e.g., electromagnetic cloaking for a fixed angle of incidence) and cannot be reused. Software-defined metamaterials (SDMs) are a much sought-after paradigm shift, exhibiting electromagnetic properties that can be reconfigured at runtime using a set of software primitives. To enable this new technology, SDMs require the integration of a network of controllers within the structure of the metamaterial, where each controller interacts locally and communicates globally to obtain the programmed behavior. The design approach for such controllers and the interconnection network, however, remains unclear due to the unique combination of constraints and requirements of the scenario. To bridge this gap, this paper aims to provide a context analysis from the computation and communication perspectives. Then, analogies are drawn between the SDM scenario and other applications both at the micro and nano scales, identifying possible candidates for the implementation of the controllers and the intra-SDM network. Finally, the main challenges of SDMs related to computing and communications are outlined.

As the current standardization for the 5G networks nears completion, work towards understanding the potential technologies for the 6G wireless networks is already underway. One of these potential technologies for the 6G networks is reconfigurable intelligent surfaces. They offer unprecedented degrees of freedom towards engineering the wireless channel, i.e., the ability to modify the characteristics of the channel whenever and however required. Nevertheless, such properties demand that the response of the associated metasurface is well understood under all possible operational conditions. While an understanding of the radiation pattern characteristics can be obtained through either analytical models or full-wave simulations, they suffer from inaccuracy and extremely high computational complexity, respectively. Hence, in this paper, we propose a neural network-based approach that enables a fast and accurate characterization of the metasurface response. We analyze multiple scenarios and demonstrate the capabilities and utility of the proposed methodology. Concretely, we show that this method can learn and predict the parameters governing the reflected wave radiation pattern with an accuracy of a full-wave simulation (98.8–99.8%) and the time and computational complexity of an analytical model. The aforementioned result and methodology will be of specific importance for the design, fault tolerance, and maintenance of the thousands of reconfigurable intelligent surfaces that will be deployed in the 6G network environment.

Graphene plasmonic antennas possess two significant features that render them appealing for short-range wireless communications, notably, inherent tunability and miniaturization due to the unique frequency dispersion of graphene and its support for surface plasmon waves in the terahertz band. In this letter, dipole-like antennas using few-layer graphene are proposed to achieve a better trade-off between miniaturization and radiation efficiency than current monolayer graphene antennas. The characteristics of few-layer graphene antennas are evaluated and then compared with those of antennas based on monolayer graphene and graphene stacks, which could also provide such improvements. To this end, first, the propagation properties of one-dimensional and two-dimensional plasmonic waveguides based on the aforementioned graphene structures are obtained by transfer matrix theory and finite-element simulation, respectively. Second, the antennas are investigated as three-dimensional structures using a full-wave solver. Results show that the highest radiation efficiency among the compared designs is achieved with the few-layer graphene, while the highest miniaturization is obtained with the even mode of the graphene stack antenna.