Low-Cost GNSS Anti-Jamming Through 2-Bit Phase Shift Beamforming with Machine Learning
Burak Soner, Ekin Uzun, Can Aksoy

TL;DR
This paper explores a low-cost GNSS anti-jamming technique using 2-bit phase shifters and machine learning, demonstrating significant interference suppression and improved tracking under jamming conditions.
Contribution
It introduces a novel ML-aided method for discrete beamforming with 2-bit phase shifters, achieving near-oracle performance with low latency and practical implementation.
Findings
Performance improves with array size, up to 34 dB nulling.
ML method performs similarly to oracle for larger arrays.
Under strong jamming, the proposed method maintains effective GNSS tracking.
Abstract
We investigate low-cost GNSS anti-jamming using beamforming with inexpensive 2-bit phase shifters, constraining each complex array weight to one of four QPSK phase states (real/imaginary = -1 or +1). This severe quantization sharply limits the beampattern solution space, making conventional real-valued beamforming and naive weight quantization highly suboptimal. We formulate a discrete optimization that trades interference suppression against satellite-direction gain, and benchmark known combinatorial optimization methods across array sizes and interference conditions. Simulations show that performance improves with array size, with oracle and greedy search achieving up to 34 dB nulling, but oracle incurs exponential latency and greedy sampling is stochastic. To obtain deterministic low-latency performance, we propose an ML-aided method based on gradient-boosted decision trees followed…
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