A gradient meta-learning algorithm with two unrolled neural networks jointly optimizes beamforming and pinching-antenna positions, reporting 5.6 bits/s/Hz weighted sum rate and a 32.7% gain over alternating optimization in simulation.
Two-Timescale Joint Transmit and Pinching Beamforming for Pinching-Antenna Systems
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abstract
Pinching antenna systems (PASS) have been proposed as a revolutionary flexible antenna technology which facilitates line-of-sight links via numerous low-cost pinching antennas with adjustable activation positions over waveguides. This letter proposes a two-timescale joint transmit and pinching beamforming design for the maximization of sum rate of a PASS-based downlink multi-user multiple input single output system. A primal dual decomposition method is developed to decouple the two-timescale problem into two sub-problems: 1) A Karush-Kuhn-Tucker-guided dual learning-based approach is proposed to solve the short-term transmit beamforming design sub-problem; 2) The long-term pinching beamforming design sub-problem is tackled by adopting a stochastic successive convex approximation method. Simulation results demonstrate that the proposed two-timescale algorithm achieves a significant performance gain compared to other baselines.
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A Gradient Meta-Learning Joint Optimization for Beamforming and Antenna Position in Pinching-Antenna Systems
A gradient meta-learning algorithm with two unrolled neural networks jointly optimizes beamforming and pinching-antenna positions, reporting 5.6 bits/s/Hz weighted sum rate and a 32.7% gain over alternating optimization in simulation.