Approaching Globally Optimal Energy Efficiency in Interference Networks via Machine Learning

• URL: http://arxiv.org/abs/2212.12329v2
• Date: Thu, 14 Dec 2023 20:21:15 GMT
• Title: Approaching Globally Optimal Energy Efficiency in Interference Networks via Machine Learning
• Authors: Bile Peng, Karl-Ludwig Besser, Ramprasad Raghunath, Eduard A. Jorswieck
• Abstract summary: This work presents a machine learning approach to optimize the energy efficiency (EE) in a multi-cell wireless network. Results show that the method achieves an EE close to the optimum by the branch-and- computation testing.
• Score: 22.926877147296594
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This work presents a machine learning approach to optimize the energy efficiency (EE) in a multi-cell wireless network. This optimization problem is non-convex and its global optimum is difficult to find. In the literature, either simple but suboptimal approaches or optimal methods with high complexity and poor scalability are proposed. In contrast, we propose a machine learning framework to approach the global optimum. While the neural network (NN) training takes moderate time, application with the trained model requires very low computational complexity. In particular, we introduce a novel objective function based on stochastic actions to solve the non-convex optimization problem. Besides, we design a dedicated NN architecture for the multi-cell network optimization problems that is permutation-equivariant. It classifies channels according to their roles in the EE computation. In this way, we encode our domain knowledge into the NN design and shed light into the black box of machine learning. Training and testing results show that the proposed method without supervision and with reasonable computational effort achieves an EE close to the global optimum found by the branch-and-bound algorithm. Hence, the proposed approach balances between computational complexity and performance.

Related papers

• Federated Multi-Level Optimization over Decentralized Networks [55.776919718214224]
  We study the problem of distributed multi-level optimization over a network, where agents can only communicate with their immediate neighbors. We propose a novel gossip-based distributed multi-level optimization algorithm that enables networked agents to solve optimization problems at different levels in a single timescale. Our algorithm achieves optimal sample complexity, scaling linearly with the network size, and demonstrates state-of-the-art performance on various applications.
  arXiv  Detail & Related papers  (2023-10-10T00:21:10Z)
• Federated Conditional Stochastic Optimization [110.513884892319]
  Conditional optimization has found in a wide range of machine learning tasks, such as in-variant learning tasks, AUPRC, andAML. This paper proposes algorithms for distributed federated learning.
  arXiv  Detail & Related papers  (2023-10-04T01:47:37Z)
• A Multi-Head Ensemble Multi-Task Learning Approach for Dynamical Computation Offloading [62.34538208323411]
  We propose a multi-head ensemble multi-task learning (MEMTL) approach with a shared backbone and multiple prediction heads (PHs) MEMTL outperforms benchmark methods in both the inference accuracy and mean square error without requiring additional training data.
  arXiv  Detail & Related papers  (2023-09-02T11:01:16Z)
• Energy Efficiency Maximization in IRS-Aided Cell-Free Massive MIMO System [2.9081408997650375]
  In this paper, we consider an intelligent reflecting surface (IRS)-aided cell-free massive multiple-input multiple-output system, where the beamforming at access points and the phase shifts at IRSs are jointly optimized to maximize energy efficiency (EE) To solve EE problem, we propose an iterative optimization algorithm by using quadratic transform and Lagrangian dual transform to find the optimum beamforming and phase shifts. We further propose a deep learning based approach for joint beamforming and phase shifts design. Specifically, a two-stage deep neural network is trained offline using the unsupervised learning manner, which is then deployed online for
  arXiv  Detail & Related papers  (2022-12-24T14:58:15Z)
• High-Speed Resource Allocation Algorithm Using a Coherent Ising Machine for NOMA Systems [3.6406488220483326]
  A key challenge to fully utilizing the effectiveness of the NOMA technique is the optimization of the resource allocation. We propose the coherent Ising machine (CIM) based optimization method for channel allocation in NOMA systems. We show that our proposed method is superior in terms of speed and the attained optimal solutions.
  arXiv  Detail & Related papers  (2022-12-03T09:22:54Z)
• Deep Learning Methods for Joint Optimization of Beamforming and Fronthaul Quantization in Cloud Radio Access Networks [12.838832724944615]
  Cooperative beamforming across points (APs) and fronthaulization strategies are essential for cloud radio network (C-RAN) systems. Non-dimensional quantity problem is stemmed from per-AP power and fronthaul capacity constraints. We investigate a deep learning optimization module is where well-trained deep neural network (DNN) Numerical results validate the advantages of the proposed learning solution.
  arXiv  Detail & Related papers  (2021-07-06T10:27:43Z)
• Iterative Algorithm Induced Deep-Unfolding Neural Networks: Precoding Design for Multiuser MIMO Systems [59.804810122136345]
  We propose a framework for deep-unfolding, where a general form of iterative algorithm induced deep-unfolding neural network (IAIDNN) is developed. An efficient IAIDNN based on the structure of the classic weighted minimum mean-square error (WMMSE) iterative algorithm is developed. We show that the proposed IAIDNN efficiently achieves the performance of the iterative WMMSE algorithm with reduced computational complexity.
  arXiv  Detail & Related papers  (2020-06-15T02:57:57Z)
• Self-Directed Online Machine Learning for Topology Optimization [58.920693413667216]
  Self-directed Online Learning Optimization integrates Deep Neural Network (DNN) with Finite Element Method (FEM) calculations. Our algorithm was tested by four types of problems including compliance minimization, fluid-structure optimization, heat transfer enhancement and truss optimization. It reduced the computational time by 2 5 orders of magnitude compared with directly using methods, and outperformed all state-of-the-art algorithms tested in our experiments.
  arXiv  Detail & Related papers  (2020-02-04T20:00:28Z)
• Optimizing Wireless Systems Using Unsupervised and Reinforced-Unsupervised Deep Learning [96.01176486957226]
  Resource allocation and transceivers in wireless networks are usually designed by solving optimization problems. In this article, we introduce unsupervised and reinforced-unsupervised learning frameworks for solving both variable and functional optimization problems.
  arXiv  Detail & Related papers  (2020-01-03T11:01:52Z)

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.