Distributed Conditional Generative Adversarial Networks (GANs) for Data-Driven Millimeter Wave Communications in UAV Networks

• URL: http://arxiv.org/abs/2102.01751v1
• Date: Tue, 2 Feb 2021 20:56:46 GMT
• Title: Distributed Conditional Generative Adversarial Networks (GANs) for Data-Driven Millimeter Wave Communications in UAV Networks
• Authors: Qianqian Zhang, Aidin Ferdowsi, Walid Saad, Mehdi Bennis
• Abstract summary: A novel framework is proposed to perform data-driven air-to-ground (A2G) channel estimation for millimeter wave (mmWave) communications in an unmanned aerial vehicle (UAV) wireless network. An effective channel estimation approach is developed, allowing each UAV to train a stand-alone channel model via a conditional generative adversarial network (CGAN) along each beamforming direction. A cooperative framework, based on a distributed CGAN architecture, is developed, allowing each UAV to collaboratively learn the mmWave channel distribution.
• Score: 116.94802388688653
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper, a novel framework is proposed to perform data-driven air-to-ground (A2G) channel estimation for millimeter wave (mmWave) communications in an unmanned aerial vehicle (UAV) wireless network. First, an effective channel estimation approach is developed to collect mmWave channel information, allowing each UAV to train a stand-alone channel model via a conditional generative adversarial network (CGAN) along each beamforming direction. Next, in order to expand the application scenarios of the trained channel model into a broader spatial-temporal domain, a cooperative framework, based on a distributed CGAN architecture, is developed, allowing each UAV to collaboratively learn the mmWave channel distribution in a fully-distributed manner. To guarantee an efficient learning process, necessary and sufficient conditions for the optimal UAV network topology that maximizes the learning rate for cooperative channel modeling are derived, and the optimal CGAN learning solution per UAV is subsequently characterized, based on the distributed network structure. Simulation results show that the proposed distributed CGAN approach is robust to the local training error at each UAV. Meanwhile, a larger airborne network size requires more communication resources per UAV to guarantee an efficient learning rate. The results also show that, compared with a stand-alone CGAN without information sharing and two other distributed schemes, namely: A multi-discriminator CGAN and a federated CGAN method, the proposed distributed CGAN approach yields a higher modeling accuracy while learning the environment, and it achieves a larger average data rate in the online performance of UAV downlink mmWave communications.

Related papers

• SINR-Aware Deep Reinforcement Learning for Distributed Dynamic Channel Allocation in Cognitive Interference Networks [10.514231683620517]
  This paper focuses on real-world systems experiencing inter-carrier interference (ICI) and channel reuse by multiple large-scale networks. We propose a novel multi-agent reinforcement learning framework for distributed DCA, named Channel Allocation RL To Overlapped Networks (CARLTON) Our results demonstrate exceptional performance and robust generalization, showcasing superior efficiency compared to alternative state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-02-17T20:03:02Z)
• Over-the-Air Federated Learning and Optimization [52.5188988624998]
  We focus on Federated learning (FL) via edge-the-air computation (AirComp) We describe the convergence of AirComp-based FedAvg (AirFedAvg) algorithms under both convex and non- convex settings. For different types of local updates that can be transmitted by edge devices (i.e., model, gradient, model difference), we reveal that transmitting in AirFedAvg may cause an aggregation error. In addition, we consider more practical signal processing schemes to improve the communication efficiency and extend the convergence analysis to different forms of model aggregation error caused by these signal processing schemes.
  arXiv  Detail & Related papers  (2023-10-16T05:49:28Z)
• A Federated Channel Modeling System using Generative Neural Networks [8.424767096311928]
  The paper proposes a data-driven approach to air-to-ground channel estimation in a millimeter-wave wireless network on an unmanned aerial vehicle. We use Federated Learning (FL) for channel estimation and can predict the air-to-ground path loss between a low-altitude platform and a terrestrial terminal. To evaluate the effectiveness of the proposed model, we evaluate its performance using Kullback-Leibler divergence (KL), and Wasserstein distance between the synthetic data distribution generated by the model and the actual data distribution.
  arXiv  Detail & Related papers  (2023-05-30T08:50:22Z)
• Decentralized Learning over Wireless Networks: The Effect of Broadcast with Random Access [56.91063444859008]
  We investigate the impact of broadcast transmission and probabilistic random access policy on the convergence performance of D-SGD. Our results demonstrate that optimizing the access probability to maximize the expected number of successful links is a highly effective strategy for accelerating the system convergence.
  arXiv  Detail & Related papers  (2023-05-12T10:32:26Z)
• Over-the-Air Design of GAN Training for mmWave MIMO Channel Estimation [35.62977046569772]
  We develop an unsupervised over-the-air (OTA) algorithm that utilizes noisy received pilot measurements to train a deep generative model. We then formulate channel estimation from a limited number of pilot measurements as an inverse problem. Our proposed framework has the potential to be trained online using real noisy pilot measurements.
  arXiv  Detail & Related papers  (2022-05-25T02:26:34Z)
• Multi-task Learning Approach for Modulation and Wireless Signal Classification for 5G and Beyond: Edge Deployment via Model Compression [1.218340575383456]
  Future communication networks must address the scarce spectrum to accommodate growth of heterogeneous wireless devices. We exploit the potential of deep neural networks based multi-task learning framework to simultaneously learn modulation and signal classification tasks. We provide a comprehensive heterogeneous wireless signals dataset for public use.
  arXiv  Detail & Related papers  (2022-02-26T14:51:02Z)
• Learning to Perform Downlink Channel Estimation in Massive MIMO Systems [72.76968022465469]
  We study downlink (DL) channel estimation in a Massive multiple-input multiple-output (MIMO) system. A common approach is to use the mean value as the estimate, motivated by channel hardening. We propose two novel estimation methods.
  arXiv  Detail & Related papers  (2021-09-06T13:42:32Z)
• Distributed CNN Inference on Resource-Constrained UAVs for Surveillance Systems: Design and Optimization [43.9909417652678]
  Unmanned Aerial Vehicles (UAVs) have attracted great interest in the last few years owing to their ability to cover large areas and access difficult and hazardous target zones. Thanks to the advancements in computer vision and machine learning, UAVs are being adopted for a broad range of solutions and applications. Deep Neural Networks (DNNs) are progressing toward deeper and complex models that prevent them from being executed on-board.
  arXiv  Detail & Related papers  (2021-05-23T20:19:43Z)
• Distributional Reinforcement Learning for mmWave Communications with Intelligent Reflectors on a UAV [119.97450366894718]
  A novel communication framework that uses an unmanned aerial vehicle (UAV)-carried intelligent reflector (IR) is proposed. In order to maximize the downlink sum-rate, the optimal precoding matrix (at the base station) and reflection coefficient (at the IR) are jointly derived.
  arXiv  Detail & Related papers  (2020-11-03T16:50:37Z)
• Federated Learning in the Sky: Joint Power Allocation and Scheduling with UAV Swarms [98.78553146823829]
  Unmanned aerial vehicle (UAV) swarms must exploit machine learning (ML) in order to execute various tasks. In this paper, a novel framework is proposed to implement distributed learning (FL) algorithms within a UAV swarm.
  arXiv  Detail & Related papers  (2020-02-19T14:04:01Z)

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.