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This paper proposes a particle‑swarm‑assisted gradient meta‑learning (PSA‑GML) algorithm to jointly optimize the transmit precoder and the transmission/reflection coefficients of a simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR‑RIS) for maximizing weighted sum rate in a multi‑user downlink. The method first transforms the non‑convex problem via amplitude‑split parameterization and collapsed precoder representation, then uses particle swarm optimization to generate a robust warm start for the STAR‑RIS coefficients, and finally refines both coefficients and precoder with a coordinate‑wise LSTM meta‑optimizer trained by first‑order gradient meta‑learning. Numerical results demonstrate that PSA‑GML achieves an 11.06 bits/s/Hz weighted sum rate at 10 dB, outperforming conventional alternating optimization by 13.1 % and the random‑phase scheme by 35.1 %, while also showing strong zero‑shot transfer across regimes.
arXiv:2606. 00862v1 Announce Type: cross Abstract: Surrogate-assisted evolutionary algorithms (SAEAs) have been widely used for expensive black-box optimization problems.
Learned optimization aims to improve upon hand-designed optimizers (e. g.
arXiv:2607. 06772v1 Announce Type: new Abstract: Learned optimization aims to improve upon hand-designed optimizers (e.
arXiv:2609.00284v1 Announce Type: cross Abstract: Emerging 6G wireless networks are expected to operate across diverse deployment scenarios, where variations in network topology, user mobility, traff...
arXiv:2607. 00860v1 Announce Type: cross Abstract: Millimeter-wave (mmWave) beam alignment plays a critical role in next-generation wireless systems, yet its efficient implementation remains challenging.