Deep Reinforcement Learning-Based User Association in Hybrid LiFi/WiFi Indoor Networks

• URL: http://arxiv.org/abs/2503.01803v1
• Date: Mon, 03 Mar 2025 18:33:11 GMT
• Title: Deep Reinforcement Learning-Based User Association in Hybrid LiFi/WiFi Indoor Networks
• Authors: Peijun Hou, Nan Cen,
• Abstract summary: Hybrid light fidelity (LiFi) and wireless fidelity (WiFi) indoor networks has been envisioned as a promising technology to alleviate the ever-increasing data rate demand in indoor scenarios.<n>The hybrid LiFi/WiFi indoor networks can leverage the advantages of fast data transmission from LiFi and wider coverage of the spectrum compared to standalone networks.<n>The objective of the paper is to design a new user-access point association algorithm for the hybrid networks.
• Score: 1.086544864007391
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Hybrid light fidelity (LiFi) and wireless fidelity (WiFi) indoor networks has been envisioned as a promising technology to alleviate radio frequency spectrum crunch to accommodate the ever-increasing data rate demand in indoor scenarios. The hybrid LiFi/WiFi indoor networks can leverage the advantages of fast data transmission from LiFi and wider coverage of WiFi, thus complementing well with each other and further improving the network performance compared with the standalone networks. However, to leverage the co-existence, several challenges should be addressed, including but not limited to user association, mobility support, and efficient resource allocation. Therefore, the objective of the paper is to design a new user-access point association algorithm to maximize the sum throughput of the hybrid networks. We first mathematically formulate the sum data rate maximization problem by determining the AP selection for each user in indoor networks with consideration of user mobility and practical capacity limitations, which is a nonconvex binary integer programming problem. To solve this problem, we then propose a sequential-proximal policy optimization (S-PPO) based deep reinforcement learning method. Extensive simulations are conducted to evaluate the proposed method by comparing it with exhaustive search (ES), signal strength strategy (SSS), and trust region policy optimization (TRPO) methods. Comprehensive simulation results demonstrate that our solution algorithm can outperform SSS by about 32.25% of the sum throughput and 19.09% of the fairness on average, and outperform TRPO by about 10.34% and 10.23%, respectively.

Related papers

• Access Point Deployment for Localizing Accuracy and User Rate in Cell-Free Systems [22.49391459228811]
  Next-generation mobile networks are designed to provide ubiquitous coverage and networked sensing.<n>Cell-free is a promising technique to realize this prospect.<n>This paper aims to tackle the problem of point (AP) deployment in cell-free systems.
  arXiv  Detail & Related papers  (2024-12-10T01:22:32Z)
• Hyperdimensional Computing Empowered Federated Foundation Model over Wireless Networks for Metaverse [56.384390765357004]
  We propose an integrated federated split learning and hyperdimensional computing framework for emerging foundation models. This novel approach reduces communication costs, computation load, and privacy risks, making it suitable for resource-constrained edge devices in the Metaverse.
  arXiv  Detail & Related papers  (2024-08-26T17:03:14Z)
• Multi Agent DeepRL based Joint Power and Subchannel Allocation in IAB networks [0.0]
  Integrated Access and Backhauling (IRL) is a viable approach for meeting the unprecedented need for higher data rates of future generations. In this paper, we show how we can use Deep Q-Learning Network to handle problems with huge action spaces associated with fractional nodes.
  arXiv  Detail & Related papers  (2023-08-31T21:30:25Z)
• FiFo: Fishbone Forwarding in Massive IoT Networks [3.7813000491275233]
  Massive Internet of Things (IoT) networks have a wide range of applications, including but not limited to the rapid delivery of emergency and disaster messages. benchmark algorithms have been developed to date for message delivery in such applications, but they pose several practical challenges. We propose a novel and effective forwarding method, fishbone forwarding (FiFo), which aims to improve the forwarding efficiency with acceptable computational complexity.
  arXiv  Detail & Related papers  (2022-11-02T15:55:17Z)
• Predictive GAN-powered Multi-Objective Optimization for Hybrid Federated Split Learning [56.125720497163684]
  We propose a hybrid federated split learning framework in wireless networks. We design a parallel computing scheme for model splitting without label sharing, and theoretically analyze the influence of the delayed gradient caused by the scheme on the convergence speed.
  arXiv  Detail & Related papers  (2022-09-02T10:29:56Z)
• Adaptive Target-Condition Neural Network: DNN-Aided Load Balancing for Hybrid LiFi and WiFi Networks [19.483289519348315]
  Machine learning has the potential to provide a complexity-friendly load balancing solution. The state-of-the-art (SOTA) learning-aided LB methods need retraining when the network environment changes. A novel deep neural network (DNN) structure named adaptive target-condition neural network (A-TCNN) is proposed.
  arXiv  Detail & Related papers  (2022-08-09T20:46:13Z)
• Federated Learning for Energy-limited Wireless Networks: A Partial Model Aggregation Approach [79.59560136273917]
  limited communication resources, bandwidth and energy, and data heterogeneity across devices are main bottlenecks for federated learning (FL) We first devise a novel FL framework with partial model aggregation (PMA) The proposed PMA-FL improves 2.72% and 11.6% accuracy on two typical heterogeneous datasets.
  arXiv  Detail & Related papers  (2022-04-20T19:09:52Z)
• Offline Contextual Bandits for Wireless Network Optimization [107.24086150482843]
  In this paper, we investigate how to learn policies that can automatically adjust the configuration parameters of every cell in the network in response to the changes in the user demand. Our solution combines existent methods for offline learning and adapts them in a principled way to overcome crucial challenges arising in this context.
  arXiv  Detail & Related papers  (2021-11-11T11:31:20Z)
• Low-Latency Federated Learning over Wireless Channels with Differential Privacy [142.5983499872664]
  In federated learning (FL), model training is distributed over clients and local models are aggregated by a central server. In this paper, we aim to minimize FL training delay over wireless channels, constrained by overall training performance as well as each client's differential privacy (DP) requirement.
  arXiv  Detail & Related papers  (2021-06-20T13:51:18Z)
• Data-Driven Random Access Optimization in Multi-Cell IoT Networks with NOMA [78.60275748518589]
  Non-orthogonal multiple access (NOMA) is a key technology to enable massive machine type communications (mMTC) in 5G networks and beyond. In this paper, NOMA is applied to improve the random access efficiency in high-density spatially-distributed multi-cell wireless IoT networks. A novel formulation of random channel access management is proposed, in which the transmission probability of each IoT device is tuned to maximize the geometric mean of users' expected capacity.
  arXiv  Detail & Related papers  (2021-01-02T15:21:08Z)

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.