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.
By Kang Zhou
arXiv:2606. 00862v1 Announce Type: cross Abstract: Surrogate-assisted evolutionary algorithms (SAEAs) have been widely used for expensive black-box optimization problems.
By Xiao Jin, Yongxiong Wang, Haobo Liu, Yudong Du, Yukun Du
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.
By Xiaolong Huang, Benjamin Th\'erien, James Harrison, Eugene Belilovsky
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...
By Fatih Temiz, Shavbo Salehi, Melike Erol-Kantarci
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.
By Ahmet Nuri Cevik, Sinem Coleri
arXiv:2603. 18853v3 Announce Type: replace-cross Abstract: Autonomous aerial vehicles (AAVs) enable data collection for sixth-generation Internet-of-Things networks, but their trajectories couple nonlinear wireless rates with long-horizon service progress.
By Xiucheng Wang, Zhenye Chen, Nan Cheng, Zhisheng Yin, Xuemin Shen
The paper proposes a CSI‑free hierarchical multi‑agent reinforcement learning framework for controlling reconfigurable reflective surfaces in millimeter‑wave networks. By replacing per‑element channel estimation with user localization data, the system uses a two‑tier neural architecture: a high‑level controller for discrete user‑to‑reflector assignments and low‑level controllers that optimize continuous focal points via MAPPO under a CTDE scheme. Deterministic ray‑tracing tests show RSSI gains of up to 7.79 dB over centralized PPO baselines and robust performance with sub‑meter localization errors for multiple users and reflector arrays.
By Hieu Le, Mostafa Ibrahim, Oguz Bedir, Jian Tao, Sabit Ekin
arXiv:2607. 19759v1 Announce Type: cross Abstract: Federated learning (FL) over wireless networks suffers from significant training latency and degraded convergence due to unreliable wireless transmission, especially under blocked propagation environments.
By Liwei Wang, Wen Chen, Jun Li, Qingqing Wu, Ming Ding, Xusheng Zhu, Qiong Wu
Agentic ESOpt proposes using evolution strategies (ES) instead of reinforcement learning to fine‑tune large language‑model agents for long‑horizon tasks. ES offers model scalability, flexibility, and better long‑horizon credit assignment, enabling full‑parameter optimization with minimal GPU memory. The framework samples parameter perturbations, evaluates agents with rewards, and updates online, achieving notable performance gains on WebArena‑Lite and in test‑time prompt‑parameter co‑evolution.
By Zhi Zheng, Rongsheng Chen, Yunpeng Ba, Zhenkun Wang, Yee Whye Teh, Wee Sun Lee
arXiv:2606. 04328v1 Announce Type: cross Abstract: Future wireless networks demand rapid adaptation to highly heterogeneous environments and dynamic task configurations, necessitating a shift from conventional rule-based and optimization-driven radio resource management (RRM) toward artificial intelligence (AI)-driven RRM.
By Fatih Temiz, Shavbo Salehi, Melike Erol-Kantarci
arXiv:2607. 19331v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards (RLVR) is rapidly advancing the reasoning capabilities of language models, yet the optimization layer that converts reward feedback into weight-space updates remains poorly understood.
By Hanqing Zhu, Wenyan Cong, Zhizhou Sha, Sagnik Mukherjee, Xinyuan Song, David Gonz\'alez-Mart\'inez, Xiaoxia Wu, Yuandong Tian, Shiwei Liu, David Z. Pan, Zhangyang "Atlas" Wang