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Special Issue "AI-Enabled Cognitive Radio Networks"

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Communications".

Deadline for manuscript submissions: closed (15 November 2020).

Special Issue Editor

Dr. Ferran Adelantado
E-Mail
Guest Editor
Universitat Oberta de Catalunya (UOC), Barcelona, Spain
Interests: wireless access networks; 5G; dynamic spectrum access; artificial intelligence; radio resource management; opportunistic spectrum access; sensor wireless networks; wireless communications protocols; low-power wide-area networks; IoT.

Special Issue Information

Dear colleagues,

The unprecedented surge in data traffic experienced over the last decade has stretched the telecommunications wireless networks to their capacity, and, thus, making more efficient ways of spectrum utilization increasingly necessary. In this context, cognitive radio (CR) technologies have attracted the interest of the research community over the last years, as an enabler for dynamic spectrum access.

Recently, the integration of artificial intelligence (AI) technologies in communications networks has revolutionized how networks are managed. The adoption of AI promises self-adaptive and reconfigurable networks able to provide reliability, energy and spectrum efficiency, etc. Based on its potential, AI is expected to be a cornerstone of future cognitive radio and spectrum sharing solutions.

This Special Issue targets novel research contributions on AI-Enabled Cognitive Radio Networks. Specific topics of interest include, but are not limited to the following:

  • Challenges and issues in designing AI-enabled cognitive radio networks and communications
  • Cross-layer design and optimization of AI-enabled cognitive radio networks
  • Machine learning, deep learning or (Deep) reinforcement learning algorithms for spectrum access
  • Traffic and/or interference prediction for spectrum sharing
  • AI-based algorithms to improve wireless communications systems coexistence
  • Novel spectrum sensing algorithms
  • Quality of service in AI-enabled cognitive radio networks

Dr. Ferran Adelantado
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2200 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • artificial intelligence (AI)
  • cognitive radio (CR)
  • spectrum sharing
  • spectrum sensing

Published Papers (2 papers)

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Research

Article
Efficient Spectrum Occupancy Prediction Exploiting Multidimensional Correlations through Composite 2D-LSTM Models
Sensors 2021, 21(1), 135; https://doi.org/10.3390/s21010135 - 28 Dec 2020
Cited by 2 | Viewed by 795
Abstract
In cognitive radio systems, identifying spectrum opportunities is fundamental to efficiently use the spectrum. Spectrum occupancy prediction is a convenient way of revealing opportunities based on previous occupancies. Studies have demonstrated that usage of the spectrum has a high correlation over multidimensions, which [...] Read more.
In cognitive radio systems, identifying spectrum opportunities is fundamental to efficiently use the spectrum. Spectrum occupancy prediction is a convenient way of revealing opportunities based on previous occupancies. Studies have demonstrated that usage of the spectrum has a high correlation over multidimensions, which includes time, frequency, and space. Accordingly, recent literature uses tensor-based methods to exploit the multidimensional spectrum correlation. However, these methods share two main drawbacks. First, they are computationally complex. Second, they need to re-train the overall model when no information is received from any base station for any reason. Different than the existing works, this paper proposes a method for dividing the multidimensional correlation exploitation problem into a set of smaller sub-problems. This division is achieved through composite two-dimensional (2D)-long short-term memory (LSTM) models. Extensive experimental results reveal a high detection performance with more robustness and less complexity attained by the proposed method. The real-world measurements provided by one of the leading mobile network operators in Turkey validate these results. Full article
(This article belongs to the Special Issue AI-Enabled Cognitive Radio Networks)
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Article
A Graph Convolutional Network-Based Deep Reinforcement Learning Approach for Resource Allocation in a Cognitive Radio Network
Sensors 2020, 20(18), 5216; https://doi.org/10.3390/s20185216 - 13 Sep 2020
Cited by 4 | Viewed by 1174
Abstract
Cognitive radio (CR) is a critical technique to solve the conflict between the explosive growth of traffic and severe spectrum scarcity. Reasonable radio resource allocation with CR can effectively achieve spectrum sharing and co-channel interference (CCI) mitigation. In this paper, we propose a [...] Read more.
Cognitive radio (CR) is a critical technique to solve the conflict between the explosive growth of traffic and severe spectrum scarcity. Reasonable radio resource allocation with CR can effectively achieve spectrum sharing and co-channel interference (CCI) mitigation. In this paper, we propose a joint channel selection and power adaptation scheme for the underlay cognitive radio network (CRN), maximizing the data rate of all secondary users (SUs) while guaranteeing the quality of service (QoS) of primary users (PUs). To exploit the underlying topology of CRNs, we model the communication network as dynamic graphs, and the random walk is used to imitate the users’ movements. Considering the lack of accurate channel state information (CSI), we use the user distance distribution contained in the graph to estimate CSI. Moreover, the graph convolutional network (GCN) is employed to extract the crucial interference features. Further, an end-to-end learning model is designed to implement the following resource allocation task to avoid the split with mismatched features and tasks. Finally, the deep reinforcement learning (DRL) framework is adopted for model learning, to explore the optimal resource allocation strategy. The simulation results verify the feasibility and convergence of the proposed scheme, and prove that its performance is significantly improved. Full article
(This article belongs to the Special Issue AI-Enabled Cognitive Radio Networks)
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