Machine Learning Towards Enabling Spectrum-as-a-Service Dynamic Sharing

• URL: http://arxiv.org/abs/2009.03756v1
• Date: Fri, 4 Sep 2020 15:41:02 GMT
• Title: Machine Learning Towards Enabling Spectrum-as-a-Service Dynamic Sharing
• Authors: Abdallah Moubayed and Tanveer Ahmed and Anwar Haque and Abdallah Shami
• Abstract summary: This paper provides an overview of the different spectrum sharing levels and techniques that have been proposed in the literature. It also discusses the potential of adopting dynamic sharing mechanisms by offering Spectrum-as-a-Service architecture.
• Score: 8.149212297123016
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The growth in wireless broadband users, devices, and novel applications has led to a significant increase in the demand for new radio frequency spectrum. This is expected to grow even further given the projection that the global traffic per year will reach 4.8 zettabytes by 2022. Moreover, it is projected that the number of Internet users will reach 4.8 billion and the number of connected devices will be close 28.5 billion devices. However, due to the spectrum being mostly allocated and divided, providing more spectrum to expand existing services or offer new ones has become more challenging. To address this, spectrum sharing has been proposed as a potential solution to improve spectrum utilization efficiency. Adopting effective and efficient spectrum sharing mechanisms is in itself a challenging task given the multitude of levels and techniques that can be integrated to enable it. To that end, this paper provides an overview of the different spectrum sharing levels and techniques that have been proposed in the literature. Moreover, it discusses the potential of adopting dynamic sharing mechanisms by offering Spectrum-as-a-Service architecture. Furthermore, it describes the potential role of machine learning models in facilitating the automated and efficient dynamic sharing of the spectrum and offering Spectrum-as-a-Service.

Related papers

• SpectrumFM: A New Paradigm for Spectrum Cognition [65.65474629224558]
  We propose a spectrum foundation model, termed SpectrumFM, which provides a new paradigm for spectrum cognition.<n>An innovative spectrum encoder that exploits the convolutional neural networks is proposed to effectively capture both fine-grained local signal structures and high-level global dependencies in the spectrum data.<n>Two novel self-supervised learning tasks, namely masked reconstruction and next-slot signal prediction, are developed for pre-training SpectrumFM, enabling the model to learn rich and transferable representations.
  arXiv  Detail & Related papers  (2025-08-02T14:40:50Z)
• SpectrumFM: A Foundation Model for Intelligent Spectrum Management [99.08036558911242]
  Existing intelligent spectrum management methods, typically based on small-scale models, suffer from notable limitations in recognition accuracy, convergence speed, and generalization.<n>This paper proposes a novel spectrum foundation model, termed SpectrumFM, establishing a new paradigm for spectrum management.<n>Experiments demonstrate that SpectrumFM achieves superior performance in terms of accuracy, robustness, adaptability, few-shot learning efficiency, and convergence speed.
  arXiv  Detail & Related papers  (2025-05-02T04:06:39Z)
• Highly Dynamic and Flexible Spatio-Temporal Spectrum Management with AI-Driven O-RAN: A Multi-Granularity Marketplace Framework [8.664317254613792]
  Current spectrum-sharing frameworks struggle with adaptability, often being either static or insufficiently dynamic. We propose an adaptive, AI-driven spectrum-sharing framework within the O-RAN architecture, integrating discriminative and generative AI (AI) to forecast spectrum needs across multiple timescales and spatial granularities.
  arXiv  Detail & Related papers  (2025-02-19T17:21:10Z)
• Data-and-Semantic Dual-Driven Spectrum Map Construction for 6G Spectrum Management [65.78866395226449]
  The proposed method can infer the spectrum utilization status of missing frequencies and improve the completeness of the spectrum map construction. The accuracy of spectrum map construction achieved by the proposed data-and-semantic dual-driven method outperforms the benchmark schemes.
  arXiv  Detail & Related papers  (2025-01-22T13:03:38Z)
• Dynamic Spectrum Access for Ambient Backscatter Communication-assisted D2D Systems with Quantum Reinforcement Learning [68.63990729719369]
  The wireless spectrum is becoming scarce, resulting in low spectral efficiency for D2D communications. This paper aims to integrate the ambient backscatter communication technology into D2D devices to allow them to backscatter ambient RF signals. We develop a novel quantum reinforcement learning (RL) algorithm that can achieve a faster convergence rate with fewer training parameters.
  arXiv  Detail & Related papers  (2024-10-23T15:36:43Z)
• Security and Privacy of 6G Federated Learning-enabled Dynamic Spectrum Sharing [9.199924426745945]
  Federated learning (FL)-enabled spectrum sensing technology has garnered wide attention. This article first examines the latest developments in FL-enabled spectrum sharing for prospective 6G scenarios. It then identifies practical attack vectors in 6G to illustrate potential AI-powered security and privacy threats.
  arXiv  Detail & Related papers  (2024-06-18T06:54:15Z)
• HyperSIGMA: Hyperspectral Intelligence Comprehension Foundation Model [88.13261547704444]
  Hyper SIGMA is a vision transformer-based foundation model for HSI interpretation. It integrates spatial and spectral features using a specially designed spectral enhancement module. It shows significant advantages in scalability, robustness, cross-modal transferring capability, and real-world applicability.
  arXiv  Detail & Related papers  (2024-06-17T13:22:58Z)
• Multi-Agent Hybrid SAC for Joint SS-DSA in CRNs [17.162697767466085]
  Opportunistic spectrum access has the potential to increase the efficiency of spectrum utilization in cognitive radio networks (CRNs) In CRNs, both spectrum sensing and resource allocation (SSRA) are critical to maximizing system throughput. We develop a novel multi-agent implementation of hybrid soft actor critic, MHSAC.
  arXiv  Detail & Related papers  (2024-04-22T16:30:03Z)
• 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 Deep Reinforcement Learning for Adaptive Medium Access and Modulation in Shared Spectrum [42.54329256803276]
  We study decentralized contention-based medium access for base stations operating on unlicensed shared spectrum. We devise a learning-based algorithm for both contention and adaptive modulation that attempts to maximize a network-wide downlink throughput objective. Empirically, we find the (proportional fairness) reward accumulated by the policy gradient approach to be significantly higher than even a genie-aided adaptive energy detection threshold.
  arXiv  Detail & Related papers  (2021-09-24T03:33:45Z)
• Optimal Power Allocation for Rate Splitting Communications with Deep Reinforcement Learning [61.91604046990993]
  This letter introduces a novel framework to optimize the power allocation for users in a Rate Splitting Multiple Access network. In the network, messages intended for users are split into different parts that are a single common part and respective private parts.
  arXiv  Detail & Related papers  (2021-07-01T06:32:49Z)
• Cooperative Multi-Agent Reinforcement Learning Based Distributed Dynamic Spectrum Access in Cognitive Radio Networks [46.723006378363785]
  Dynamic spectrum access (DSA) is a promising paradigm to remedy the problem of inefficient spectrum utilization. In this paper, we investigate the distributed DSA problem for multi-user in a typical cognitive radio network. We employ the deep recurrent Q-network (DRQN) to address the partial observability of the state for each cognitive user.
  arXiv  Detail & Related papers  (2021-06-17T06:52:21Z)
• Machine Learning Framework for Sensing and Modeling Interference in IoT Frequency Bands [2.6839965970551276]
  There is growing need for better understanding of the spectrum occupancy with newly emerging access technologies supporting the Internet of Things. We present a framework to capture and model the traffic behavior of short-time spectrum occupancy for IoT applications in the shared bands to determine the existing interference.
  arXiv  Detail & Related papers  (2021-06-10T19:10:40Z)
• A Hybrid Model-based and Data-driven Approach to Spectrum Sharing in mmWave Cellular Networks [37.00906872828011]
  Inter-operator spectrum sharing in millimeter-wave bands has the potential of substantially increasing the spectrum utilization. Traditional model-based spectrum sharing schemes make idealistic assumptions about inter-operator coordination mechanisms. We propose hybrid model-based and data-driven multi-operator spectrum sharing mechanisms.
  arXiv  Detail & Related papers  (2020-03-19T07:34:56Z)

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.