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Mitigation Factors for Multi-domain Resilient Networked Distributed Tessellation Communications

Authors:
Steve Chan

Keywords: Transceiver polygons; Signal processing; Beamforming; Non-permissive cyber electromagnetic environment; 5G networks; Smart grids; Covariance matrix; Spatial filtering algorithms; Convex optimization problems; Semidefinite programming solvers; Space-Time A

Abstract:
Numerous technical calls have converged upon an overarching goal of Resilient Networked Distributed Tessellation Communications (RNDTC) so as to provide long-range communications through the notion of “tessellation” antennas, which are comprised of spatially distributed low Size, Weight, Power, and Cost (SWaP-C) transceiver “polygons.” At its core, this approach supplants higher powered amplifiers and large directional antennas with various tessellations of spatially dispersed transceiver polygons. In essence, the transmit power is spatially distributed amongst the polygons, and gain is achieved, via signal processing rather than the use of, by way of example, an antenna aperture so as to concentrate energy. Therefore, signal processing functions enable the various polygons to self-form into an array and enable beamforming, among other techniques, thereby enhancing the desired signals and somewhat obviating intentional/unintentional interference. However, the algorithmic approaches to date have varied pros and cons (e.g., the attainment of reduced sidelobes at the expense of the mainlobe, wherein interference suppression is achieved at the cost of the resolution of the signals). There are promising interference mitigation factor pathways, such as adaptive weight shifting, during the analyzing, transforming, and synthesizing of such signals. However, despite the advantages of adaptive weighting techniques, the computational complexity is extremely high, and the ensuing complexity reduction processes are subject to adversarial exploitation. Accordingly, this paper proposes mitigation factors by way of Artificial Intelligence (AI)-centric Genetic Algorithm (GA) approaches amidst the analysis, transformation, and synthesis amalgam. In particular, preliminary experimental results (to be furthered in future work) indicate promise for the auto-tuning of the Steady State Genetic Algorithm (SSGA) compression factor ζ for more optimal convergence.

Pages: 66 to 73

Copyright: Copyright (c) IARIA, 2020

Publication date: October 25, 2020

Published in: conference

ISSN: 2519-8599

ISBN: 978-1-61208-818-1

Location: Nice, France

Dates: from October 25, 2020 to October 29, 2020