A Gradient Meta-Learning Joint Optimization for Beamforming and Antenna Position in Pinching-Antenna Systems

• URL: http://arxiv.org/abs/2506.12583v1
• Date: Sat, 14 Jun 2025 17:35:27 GMT
• Title: A Gradient Meta-Learning Joint Optimization for Beamforming and Antenna Position in Pinching-Antenna Systems
• Authors: Kang Zhou, Weixi Zhou, Donghong Cai, Xianfu Lei, Yanqing Xu, Zhiguo Ding, Pingzhi Fan,
• Abstract summary: We consider a novel optimization design for multi-waveguide pinching-antenna systems.<n>The proposed GML-JO algorithm is robust to different choices and better performance compared with the existing optimization methods.
• Score: 63.213207442368294
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we consider a novel optimization design for multi-waveguide pinching-antenna systems, aiming to maximize the weighted sum rate (WSR) by jointly optimizing beamforming coefficients and antenna position. To handle the formulated non-convex problem, a gradient-based meta-learning joint optimization (GML-JO) algorithm is proposed. Specifically, the original problem is initially decomposed into two sub-problems of beamforming optimization and antenna position optimization through equivalent substitution. Then, the convex approximation methods are used to deal with the nonconvex constraints of sub-problems, and two sub-neural networks are constructed to calculate the sub-problems separately. Different from alternating optimization (AO), where two sub-problems are solved alternately and the solutions are influenced by the initial values, two sub-neural networks of proposed GML-JO with fixed channel coefficients are considered as local sub-tasks and the computation results are used to calculate the loss function of joint optimization. Finally, the parameters of sub-networks are updated using the average loss function over different sub-tasks and the solution that is robust to the initial value is obtained. Simulation results demonstrate that the proposed GML-JO algorithm achieves 5.6 bits/s/Hz WSR within 100 iterations, yielding a 32.7\% performance enhancement over conventional AO with substantially reduced computational complexity. Moreover, the proposed GML-JO algorithm is robust to different choices of initialization and yields better performance compared with the existing optimization methods.

Related papers

• Adam assisted Fully informed Particle Swarm Optimzation ( Adam-FIPSO ) based Parameter Prediction for the Quantum Approximate Optimization Algorithm (QAOA) [1.024113475677323]
  The Quantum Approximate Optimization Algorithm (QAOA) is a prominent variational algorithm used for solving optimization problems such as the Max-Cut problem.<n>A key challenge in QAOA lies in efficiently identifying suitable parameters that lead to high-quality solutions.
  arXiv  Detail & Related papers  (2025-06-07T13:14:41Z)
• A Near-Optimal Single-Loop Stochastic Algorithm for Convex Finite-Sum Coupled Compositional Optimization [53.14532968909759]
  We introduce an efficient single-loop primal-dual block-coordinate algorithm called ALEXR.<n>We establish the convergence rates of ALEXR in both convex and strongly convex cases under smoothness and non-smoothness conditions.<n> Experimental results on GDRO and partial Area Under the ROC Curve for cFCCO demonstrate the promising performance of our algorithm.
  arXiv  Detail & Related papers  (2023-12-04T19:00:07Z)
• Composite Optimization Algorithms for Sigmoid Networks [3.160070867400839]
  We propose the composite optimization algorithms based on the linearized proximal algorithms and the alternating direction of multipliers. Numerical experiments on Frank's function fitting show that the proposed algorithms perform satisfactorily robustly.
  arXiv  Detail & Related papers  (2023-03-01T15:30:29Z)
• Fast Computation of Optimal Transport via Entropy-Regularized Extragradient Methods [75.34939761152587]
  Efficient computation of the optimal transport distance between two distributions serves as an algorithm that empowers various applications. This paper develops a scalable first-order optimization-based method that computes optimal transport to within $varepsilon$ additive accuracy.
  arXiv  Detail & Related papers  (2023-01-30T15:46:39Z)
• Adaptive Stochastic Optimisation of Nonconvex Composite Objectives [2.1700203922407493]
  We propose and analyse a family of generalised composite mirror descent algorithms. With adaptive step sizes, the proposed algorithms converge without requiring prior knowledge of the problem. We exploit the low-dimensional structure of the decision sets for high-dimensional problems.
  arXiv  Detail & Related papers  (2022-11-21T18:31:43Z)
• Lower Bounds and Optimal Algorithms for Smooth and Strongly Convex Decentralized Optimization Over Time-Varying Networks [79.16773494166644]
  We consider the task of minimizing the sum of smooth and strongly convex functions stored in a decentralized manner across the nodes of a communication network. We design two optimal algorithms that attain these lower bounds. We corroborate the theoretical efficiency of these algorithms by performing an experimental comparison with existing state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-06-08T15:54:44Z)
• Meta-learning based Alternating Minimization Algorithm for Non-convex Optimization [9.774392581946108]
  We propose a novel solution for challenging non-problems of multiple variables. Our proposed approach is able to achieve effective iterations in cases while other methods would typically fail.
  arXiv  Detail & Related papers  (2020-09-09T10:45:00Z)
• On the implementation of a global optimization method for mixed-variable problems [0.30458514384586394]
  The algorithm is based on the radial basis function of Gutmann and the metric response surface method of Regis and Shoemaker. We propose several modifications aimed at generalizing and improving these two algorithms.
  arXiv  Detail & Related papers  (2020-09-04T13:36:56Z)
• Convergence of adaptive algorithms for weakly convex constrained optimization [59.36386973876765]
  We prove the $mathcaltilde O(t-1/4)$ rate of convergence for the norm of the gradient of Moreau envelope. Our analysis works with mini-batch size of $1$, constant first and second order moment parameters, and possibly smooth optimization domains.
  arXiv  Detail & Related papers  (2020-06-11T17:43:19Z)
• Effective Dimension Adaptive Sketching Methods for Faster Regularized Least-Squares Optimization [56.05635751529922]
  We propose a new randomized algorithm for solving L2-regularized least-squares problems based on sketching. We consider two of the most popular random embeddings, namely, Gaussian embeddings and the Subsampled Randomized Hadamard Transform (SRHT)
  arXiv  Detail & Related papers  (2020-06-10T15:00:09Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.