Sidelobe Canceling for Reconfigurable Holographic Metamaterial Antenna

Abstract

Abstract-Accurate and efficient methods for beam-steering of holographic metamaterial antennas is of critical importance for enabling consumer usage of satellite data capacities. We develop an optimization algorithm capable of performing adaptive, realtime control of antenna patterns while operating in dynamic environments. Our method provides a first analysis of the antenna pattern optimization problem in the context of metamaterials and for the purpose of directing the central beam and significantly suppressing sidelobe levels. The efficacy of the algorithm is demonstrated both on a computational model of the antenna and experimentally. Due to their exceptional portability, low-power consumption and lack of moving parts, metamaterial antennas are an attractive and viable technology when combined with proven software engineering strategies to optimize performance.

Analysis of a Waveguide-Fed Metasurface Antenna

“…11, produces the desired beam. However, the first and third sums can potentially produce additional, undesired beams, and therefore we must assess the impact of these additional terms [3]. The formation of a beam occurs for a given sum when the argument of the complex exponential vanishes; when this condition occurs, the fields from all elements interfere constructively [48,49].…”

“…The resulting binary weight distribution produces a single beam, but with an increase in the overall side lobe levels. Depending on the application, the side lobe levels associated with this straightforward design may be acceptable, but can certainly be decreased using other optimization methods [3]. Aside from the higher side lobe levels, the result shown in Fig.…”

Polarizability extraction of complementary metamaterial elements in waveguides for aperture modeling

“…In addition, the waveguide-fed metamaterial elements also leak energy out of the waveguide, such that the entire structure can act as an aperture antenna [48,58]. Indeed, metasurfaces can provide nearly total control over the wavefront in a manner similar to phased arrays [59] and other aperture antennas, but often with advantages not available in other formats [60][61][62][63][64].…”

Self-tuning fiber lasers

“…Methodological advances in machine learning, sparse sensing, and adaptive control are providing significant gains in the data-driven characterization and control of complex nonlinear systems, 1 including epidemiology, 2-4 neuroscience, 5 turbulent fluid dynamics, 6-10 and optics, [11][12][13][14][15] to name a few. These advances are driven by fundamental breakthroughs in data processing algorithms along with improved actuation hardware and sensors that provide unprecedented data streams, in terms of quality and bandwidth.…”

“…13 As we will describe below, the combination of methods from machine learning, 1, 33-36 sparsity, 32, 37-40 and control 30,41,42 are poised to revolutionize the data-driven control of complex systems. Other examples include beam forming and null-steering in a reconfigurable metamaterial antenna array, 14,15 and the machine learning characterization 6 and control 7-9 of turbulent fluids. 10 …”