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GPASS: Deep Learning for Beamforming in Pinching-Antenna Systems (PASS)
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A novel GPASS architecture is proposed for jointly learning pinching beamforming and transmit beamforming in pinching antenna systems (PASS). The GPASS is with a staged architecture, where the positions of pinching antennas are first learned by a sub-GNN. Then, the transmit beamforming is learned by another sub-GNN based on the antenna positions. The sub-GNNs are incorporated with the permutation property of the beamforming policy, which helps improve the learning performance. The optimal solution structure of transmit beamforming is also leveraged to simplify the mappings to be learned. Numerical results demonstrate that the proposed architecture can achieve a higher SE than a heuristic baseline method with low inference complexity.
Forward citations
Cited by 4 Pith papers
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Multigroup Multicast Design for Pinching-Antenna Systems: Waveguide-Division or Waveguide-Multiplexing?
Pinching-antenna systems can beat conventional and massive MIMO for multigroup multicast by repositioning antennas along waveguides, with waveguide-multiplexing best for dense users and waveguide-division best for sep...
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Deep Learning Optimization of Two-State Pinching Antennas Systems
A graph neural network with distributed attention selects near-optimal subsets of active pinching antennas, matching a Gurobi solver's rates within a few percent and generalizing from 50 to 1000 antennas.
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Exploiting Pinching-Antenna Systems in Multicast Communications
Optimizing pinching-antenna positions along dielectric waveguides improves multicast rates, with closed-form results for a single antenna and iterative algorithms for multiple antennas or waveguides.
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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 optimizati...
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