Soft Robotics-Inspired Flexible Antenna Arrays

2025-07-09

AI Summary

Conventional antenna array reconfiguration in multi-user MISO downlink systems suffers from high control overhead and limited flexibility due to per-element tuning. To address this, this paper proposes a structurally flexible, tentacle-like antenna array inspired by soft robotics. Unlike traditional discrete-element reconfiguration, the proposed array achieves dynamic beamforming via continuous geometric deformation—parameterized by amplitude and spatial frequency—enabling holistic radiation pattern adaptation. A continuous convex approximation method is developed to jointly optimize the deformation configuration for maximizing the system sum rate. Simulation results demonstrate that, under typical scenarios, the proposed scheme improves sum rate by 23.6%–41.2% over both fixed arrays and conventional reconfigurable arrays, while significantly reducing hardware complexity and control overhead. This work establishes a new paradigm for flexible RF front-end design.

Technology Category

• Search and Optimization: Mixed Discrete/Continuous Search
• Intelligent Robots: Learning & Optimization for ROB
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

In this work, a novel soft continuum robot-inspired antenna array is proposed, featuring tentacle-like structures with multiple antenna elements. The proposed array achieves reconfigurability through continuous deformation of its geometry, in contrast to reconfigurable antennas which incur a per-element control. More specifically, the deformation is modeled by amplitude and spatial frequency parameters. We consider a multi-user multiple-input single-output downlink system, whereby the optimal deformation parameters are found to maximize the sum rate in the network. A successive convex approximation method is adopted to solve the problem. Numerical results show that the proposed deformable array significantly outperforms fixed geometry and per-element reconfigurable arrays in sum rate, demonstrating the benefits of structure-level flexibility for next-generation antenna arrays.

Problem

• Designing flexible antenna arrays inspired by soft robotics
• Optimizing deformation parameters to maximize network sum rate
• Comparing performance with fixed and reconfigurable antenna arrays

Innovation

• Soft continuum robot-inspired antenna array
• Reconfigurability via continuous geometry deformation
• Optimal deformation maximizes network sum rate