Sum-Rate Maximization for UAV-assisted Visible Light Communications using NOMA: Swarm Intelligence meets Machine Learning

• URL: http://arxiv.org/abs/2101.03498v1
• Date: Sun, 10 Jan 2021 08:21:49 GMT
• Title: Sum-Rate Maximization for UAV-assisted Visible Light Communications using NOMA: Swarm Intelligence meets Machine Learning
• Authors: Quoc-Viet Pham, Thien Huynh-The, Mamoun Alazab, Jun Zhao, Won-Joo Hwang
• Abstract summary: We consider a UAV-assisted visible light communications (VLC) using non-orthogonal multiple-access networks. We formulate a problem of power allocation and UAVs placement to maximize the sum of service users and UAV's position. We propose using harris hawks optimization (HHO) to solve formulated problem and obtain an efficient solution.
• Score: 15.385078410753986
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: As the integration of unmanned aerial vehicles (UAVs) into visible light communications (VLC) can offer many benefits for massive-connectivity applications and services in 5G and beyond, this work considers a UAV-assisted VLC using non-orthogonal multiple-access. More specifically, we formulate a joint problem of power allocation and UAV's placement to maximize the sum rate of all users, subject to constraints on power allocation, quality of service of users, and UAV's position. Since the problem is non-convex and NP-hard in general, it is difficult to be solved optimally. Moreover, the problem is not easy to be solved by conventional approaches, e.g., coordinate descent algorithms, due to channel modeling in VLC. Therefore, we propose using harris hawks optimization (HHO) algorithm to solve the formulated problem and obtain an efficient solution. We then use the HHO algorithm together with artificial neural networks to propose a design which can be used in real-time applications and avoid falling into the "local minima" trap in conventional trainers. Numerical results are provided to verify the effectiveness of the proposed algorithm and further demonstrate that the proposed algorithm/HHO trainer is superior to several alternative schemes and existing metaheuristic algorithms.

Related papers

• Wireless Federated Learning over UAV-enabled Integrated Sensing and Communication [2.8203310972866382]
  This paper studies a new latency optimization problem in unmanned aerial vehicles (UAVs)-enabled federated learning (FL) with integrated sensing and communication. We develop a simple yet efficient iterative algorithm to find a high-quality approximate solution, saving system latency up to 68.54% compared to benchmark schemes.
  arXiv  Detail & Related papers  (2024-11-01T14:25:24Z)
• UAV-enabled Collaborative Beamforming via Multi-Agent Deep Reinforcement Learning [79.16150966434299]
  We formulate a UAV-enabled collaborative beamforming multi-objective optimization problem (UCBMOP) to maximize the transmission rate of the UVAA and minimize the energy consumption of all UAVs. We use the heterogeneous-agent trust region policy optimization (HATRPO) as the basic framework, and then propose an improved HATRPO algorithm, namely HATRPO-UCB.
  arXiv  Detail & Related papers  (2024-04-11T03:19:22Z)
• Joint User Association, Interference Cancellation and Power Control for Multi-IRS Assisted UAV Communications [80.35959154762381]
  Intelligent reflecting surface (IRS)-assisted unmanned aerial vehicle (UAV) communications are expected to alleviate the load of ground base stations in a cost-effective way. Existing studies mainly focus on the deployment and resource allocation of a single IRS instead of multiple IRSs. We propose a new optimization algorithm for joint IRS-user association, trajectory optimization of UAVs, successive interference cancellation (SIC) decoding order scheduling and power allocation.
  arXiv  Detail & Related papers  (2023-12-08T01:57:10Z)
• 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)
• Efficient Real-Time Image Recognition Using Collaborative Swarm of UAVs and Convolutional Networks [9.449650062296824]
  We present a strategy aiming at distributing inference requests to a swarm of resource-constrained UAVs that classifies captured images on-board. We formulate the model as an optimization problem that minimizes the latency between acquiring images and making the final decisions. We introduce an online solution, namely DistInference, to find the layers placement strategy that gives the best latency among the available UAVs.
  arXiv  Detail & Related papers  (2021-07-09T19:47:02Z)
• Towards Optimally Efficient Tree Search with Deep Learning [76.64632985696237]
  This paper investigates the classical integer least-squares problem which estimates signals integer from linear models. The problem is NP-hard and often arises in diverse applications such as signal processing, bioinformatics, communications and machine learning. We propose a general hyper-accelerated tree search (HATS) algorithm by employing a deep neural network to estimate the optimal estimation for the underlying simplified memory-bounded A* algorithm.
  arXiv  Detail & Related papers  (2021-01-07T08:00:02Z)
• Multi-Agent Reinforcement Learning in NOMA-aided UAV Networks for Cellular Offloading [59.32570888309133]
  A novel framework is proposed for cellular offloading with the aid of multiple unmanned aerial vehicles (UAVs) Non-orthogonal multiple access (NOMA) technique is employed at each UAV to further improve the spectrum efficiency of the wireless network. A mutual deep Q-network (MDQN) algorithm is proposed to jointly determine the optimal 3D trajectory and power allocation of UAVs.
  arXiv  Detail & Related papers  (2020-10-18T20:22:05Z)
• NOMA in UAV-aided cellular offloading: A machine learning approach [59.32570888309133]
  A novel framework is proposed for cellular offloading with the aid of multiple unmanned aerial vehicles (UAVs) Non-orthogonal multiple access (NOMA) technique is employed at each UAV to further improve the spectrum efficiency of the wireless network. A mutual deep Q-network (MDQN) algorithm is proposed to jointly determine the optimal 3D trajectory and power allocation of UAVs.
  arXiv  Detail & Related papers  (2020-10-18T17:38:48Z)
• Energy Minimization in UAV-Aided Networks: Actor-Critic Learning for Constrained Scheduling Optimization [30.742052801257998]
  In unmanned aerial vehicle (UAV) applications, the UAV's limited energy supply and storage have triggered the development of intelligent energy-conserving solutions. In this paper, we investigate energy-DSOS solution jointly optimizing data-transmission scheduling hovering time.
  arXiv  Detail & Related papers  (2020-06-24T10:44:28Z)
• Reinforcement Learning Based Vehicle-cell Association Algorithm for Highly Mobile Millimeter Wave Communication [53.47785498477648]
  This paper investigates the problem of vehicle-cell association in millimeter wave (mmWave) communication networks. We first formulate the user state (VU) problem as a discrete non-vehicle association optimization problem. The proposed solution achieves up to 15% gains in terms sum of user complexity and 20% reduction in VUE compared to several baseline designs.
  arXiv  Detail & Related papers  (2020-01-22T08:51:05Z)

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.