Accelerating Distributed Beamforming with the Frank-Wolfe Algorithm

Many traditional feedback-based beamforming techniques can pose risks in environments where adversaries try to jam or detect the beamforming agents. Several low- or no-feedback approaches to beamforming have been proposed to remedy this. Still, problems such as limited transmitter power budgets and inaccurate Channel State Information (CSI) complicate this problem. To that end, this work investigates the use of constrained optimization techniques to allow a team of distributed Unmanned Ground Vehicles (UGVs) to form a beam in the absence of feedback without accurate knowledge of CSI. Specifically, we tune each transmitting agent's signal amplitude and phase offset to selectively build higher-power beams at allied receivers and lower-power nulls at adversarial receivers. To achieve this, we implement a decentralized version of the Frank-Wolfe algorithm, which does not rely on a central coordinator to solve the optimal signal amplitudes and phase offsets without accurate CSI measurements. Numerical results are also provided to validate our approach.