Machine Learning enabled Spectrum Sharing in Dense LTE-U/Wi-Fi Coexistence Scenarios

• URL: http://arxiv.org/abs/2003.13652v1
• Date: Wed, 18 Mar 2020 01:26:36 GMT
• Title: Machine Learning enabled Spectrum Sharing in Dense LTE-U/Wi-Fi Coexistence Scenarios
• Authors: Adam Dziedzic, Vanlin Sathya, Muhammad Iqbal Rochman, Monisha Ghosh and Sanjay Krishnan
• Abstract summary: We focus on the LTE-Unlicensed (LTE-U) specification, which uses the duty-cycle approach for fair coexistence. Without decoding the Wi-Fi packets, detecting the number of Wi-Fi basic service sets operating on the channel in real-time is a challenging problem. We propose a novel ML-based approach which solves this problem by using energy values observed during the LTE-U OFF duration.
• Score: 10.228746210951533
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The application of Machine Learning (ML) techniques to complex engineering problems has proved to be an attractive and efficient solution. ML has been successfully applied to several practical tasks like image recognition, automating industrial operations, etc. The promise of ML techniques in solving non-linear problems influenced this work which aims to apply known ML techniques and develop new ones for wireless spectrum sharing between Wi-Fi and LTE in the unlicensed spectrum. In this work, we focus on the LTE-Unlicensed (LTE-U) specification developed by the LTE-U Forum, which uses the duty-cycle approach for fair coexistence. The specification suggests reducing the duty cycle at the LTE-U base-station (BS) when the number of co-channel Wi-Fi basic service sets (BSSs) increases from one to two or more. However, without decoding the Wi-Fi packets, detecting the number of Wi-Fi BSSs operating on the channel in real-time is a challenging problem. In this work, we demonstrate a novel ML-based approach which solves this problem by using energy values observed during the LTE-U OFF duration. It is relatively straightforward to observe only the energy values during the LTE-U BS OFF time compared to decoding the entire Wi-Fi packet, which would require a full Wi-Fi receiver at the LTE-U base-station. We implement and validate the proposed ML-based approach by real-time experiments and demonstrate that there exist distinct patterns between the energy distributions between one and many Wi-Fi AP transmissions. The proposed ML-based approach results in a higher accuracy (close to 99\% in all cases) as compared to the existing auto-correlation (AC) and energy detection (ED) approaches.

Related papers

• Lightweight Federated Learning over Wireless Edge Networks [83.4818741890634]
  Federated (FL) is an alternative at network edge, but an alternative in wireless networks.<n>We derive a closed-form expression FL convergence gap transmission power, model pruning error, and quantization.<n> LTFL outperforms state-the-art schemes in experiments on real-world datasets.
  arXiv  Detail & Related papers  (2025-07-13T09:14:17Z)
• WDMoE: Wireless Distributed Mixture of Experts for Large Language Models [68.45482959423323]
  Large Language Models (LLMs) have achieved significant success in various natural language processing tasks. We propose a wireless distributed Mixture of Experts (WDMoE) architecture to enable collaborative deployment of LLMs across edge servers at the base station (BS) and mobile devices in wireless networks.
  arXiv  Detail & Related papers  (2024-11-11T02:48:00Z)
• Online-Score-Aided Federated Learning: Taming the Resource Constraints in Wireless Networks [26.74283774805648]
  We propose a new FL algorithm called OSAFL, specifically designed to learn tasks relevant to wireless applications. Our extensive simulation results on two different tasks -- each with three different datasets -- with four popular ML models validate the effectiveness of OSAFL.
  arXiv  Detail & Related papers  (2024-08-12T01:27:06Z)
• WDMoE: Wireless Distributed Large Language Models with Mixture of Experts [65.57581050707738]
  We propose a wireless distributed Large Language Models (LLMs) paradigm based on Mixture of Experts (MoE) We decompose the MoE layer in LLMs by deploying the gating network and the preceding neural network layer at base station (BS) and mobile devices. We design an expert selection policy by taking into account both the performance of the model and the end-to-end latency.
  arXiv  Detail & Related papers  (2024-05-06T02:55:50Z)
• Multiagent Reinforcement Learning with an Attention Mechanism for Improving Energy Efficiency in LoRa Networks [52.96907334080273]
  As the network scale increases, the energy efficiency of LoRa networks decreases sharply due to severe packet collisions. We propose a transmission parameter allocation algorithm based on multiagent reinforcement learning (MALoRa) Simulation results demonstrate that MALoRa significantly improves the system EE compared with baseline algorithms.
  arXiv  Detail & Related papers  (2023-09-16T11:37:23Z)
• Distributed Multi-Agent Deep Q-Learning for Fast Roaming in IEEE 802.11ax Wi-Fi Systems [8.057006406834466]
  Wi-Fi 6, IEEE 802.11ax, was be approved as the next sixth-generation (6G) technology of wireless local area networks (WLANs) In this paper, we propose a multi-agent deep Q-learning for fast roaming (MADAR) algorithm to effectively minimize the latency during the station roaming for Smart Warehouse in Wi-Fi 6 system.
  arXiv  Detail & Related papers  (2023-03-25T04:39:59Z)
• Bayesian Nonparametric Modelling for Model-Free Reinforcement Learning in LTE-LAA and Wi-Fi Coexistence [2.8427946758947304]
  This work features a Nonparametric Bayesian reinforcement learning algorithm to cope with the coexistence between Wi-Fi and LTE licensed assisted access (LTE-LAA) agents in 5 GHz unlicensed spectrum. A fairness measure is introduced in the reward function to encourage fair sharing between agents.
  arXiv  Detail & Related papers  (2021-07-06T07:11:34Z)
• Transfer Learning for Future Wireless Networks: A Comprehensive Survey [49.746711269488515]
  This article aims to provide a comprehensive survey on applications of Transfer Learning in wireless networks. We first provide an overview of TL including formal definitions, classification, and various types of TL techniques. We then discuss diverse TL approaches proposed to address emerging issues in wireless networks.
  arXiv  Detail & Related papers  (2021-02-15T14:19:55Z)
• Wireless for Machine Learning [91.13476340719087]
  We give an exhaustive review of the state-of-the-art wireless methods that are specifically designed to support machine learning services over distributed datasets. There are two clear themes within the literature, analog over-the-air computation and digital radio resource management optimized for ML. This survey gives a comprehensive introduction to these methods, reviews the most important works, highlights open problems, and discusses application scenarios.
  arXiv  Detail & Related papers  (2020-08-31T11:09:49Z)
• Communication Efficient Federated Learning with Energy Awareness over Wireless Networks [51.645564534597625]
  In federated learning (FL), the parameter server and the mobile devices share the training parameters over wireless links. We adopt the idea of SignSGD in which only the signs of the gradients are exchanged. Two optimization problems are formulated and solved, which optimize the learning performance. Considering that the data may be distributed across the mobile devices in a highly uneven fashion in FL, a sign-based algorithm is proposed.
  arXiv  Detail & Related papers  (2020-04-15T21:25:13Z)

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.