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Cognitive Radio Applications and Spectrum Management

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

Deadline for manuscript submissions: closed (10 May 2021) | Viewed by 33641

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Special Issue Editor

Prof. Ingrid Moerman

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Guest Editor

IDLab, Department of Information Technology, Ghent University - imecGhentBelgium, Ghent, Belgium
Interests: wireless networks; time-sensitive networks; deterministic wireless networks; cognitive and cooperative radio; 5G/xG
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Wireless communication networks suffer from capacity bottlenecks because the amount of available spectrum is fixed, while wireless traffic demands keep growing by approximately 50% a year. This is particularly the case in the lower spectrum bands (< 7 GHz) exhibiting most favorable propagation properties, but mmWave bands are also becoming more crowded, both for terrestrial and satellite communications. Since the early days of wireless communication, wireless spectrum has been allocated according to a static frequency plan leading to many fixed frequency bands. Most of these bands are licensed for exclusive use by specific services or radio technologies, and the process for changing spectrum allocation is extremely slow (cf. spectrum allocation for 5G taking many years). Fixed, exclusive spectrum allocation is further characterized by severe overprovisioning and underutilization both in time and geographically, hence leading to a lot of waste of precious resources. Static frequency planning is obviously not a sustainable spectrum allocation model, leaving no room for future wireless services and new wireless actors.

There is no doubt that in order to increase spectrum utilization, allocation has to become more dynamic and the spectrum needs to be shared across wireless networks and network operators, not only in unlicensed but also in licensed spectrum bands. To this end, new mechanisms need to be explored for more dynamic spectrum allocation. Such techniques do not only involve cognitive radio and spectrum management capabilities but also require strategies for verification of spectrum usage ensuring interference free operation of multiple networks sharing the same spectrum and avoiding inappropriate or unauthorized use of the spectrum.

Prof. Ingrid Moerman
Guest Editor

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Related Special Issue

Cognitive Radio Applications and Spectrum Management II in Sensors (7 articles)

Published Papers (10 papers)

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Research

Jump to: Review

21 pages, 2382 KiB

Open AccessArticle

Intelligent Dynamic Spectrum Resource Management Based on Sensing Data in Space-Time and Frequency Domain

by Deok-Won Yun and Won-Cheol Lee

Sensors 2021, 21(16), 5261; https://doi.org/10.3390/s21165261 - 04 Aug 2021

Cited by 8 | Viewed by 2801

Abstract

Edge computing offers a promising paradigm for implementing the industrial Internet of things (IIoT) by offloading intensive computing tasks from resource constrained machine type devices to powerful edge servers. However, efficient spectrum resource management is required to meet the quality of service requirements of various applications, taking into account the limited spectrum resources, batteries, and the characteristics of available spectrum fluctuations. Therefore, this study proposes intelligent dynamic spectrum resource management consisting of learning engines that select optimal backup channels based on history data, reasoning engines that infer idle channels based on backup channel lists, and transmission parameter optimization engines based genetic algorithm using interference analysis in time, space and frequency domains. The performance of the proposed intelligent dynamic spectrum resource management was evaluated in terms of the spectrum efficiency, number of spectrum handoff, latency, energy consumption, and link maintenance probability according to the backup channel selection technique and the number of IoT devices and the use of transmission parameters optimized for each traffic environment. The results demonstrate that the proposed method is superior to existing spectrum resource management functions. Full article

(This article belongs to the Special Issue Cognitive Radio Applications and Spectrum Management)

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16 pages, 2607 KiB

Open AccessFeature PaperArticle

Densely Deployed Indoor Massive MIMO Experiment: From Small Cells to Spectrum Sharing to Cooperation

by Andrea P. Guevara and Sofie Pollin

Sensors 2021, 21(13), 4346; https://doi.org/10.3390/s21134346 - 25 Jun 2021

Cited by 2 | Viewed by 1589

Abstract

Massive MIMO is a key 5G technology that achieves high spectral efficiency and capacity by significantly increasing the number of antennas per cell. Furthermore, due to precoding, massive MIMO allows co-channel interference cancellation across cells. In this work, based on experimental channel data for an indoor scenario, we analyse the impact of inter and intra-cell interference suppression in terms of spectral efficiency, capacity, user fairness and computational cost for three simulated systems under different cooperation levels. The first scenario assumes a cooperative case where eight neighbouring cells share the spectrum and infrastructure. This scenario provides the highest system performance; however, user fairness is achieved only when there is inter and intra-cell interference suppression. The second scenario considers eight cells that only share the spectrum; with full intra-cell and inter-cell interference cancellation, it is possible to achieve 32% of the optimal capacity with 20% of the computational cost in each distributed CPU, although the total computational cost per system is the highest. The third scenario considers eight independent cells operating in different frequency bands; in this case, intra-cell interference suppression leads to higher spectral efficiency compared to the cooperative case without intra-cell interference suppression. Full article

(This article belongs to the Special Issue Cognitive Radio Applications and Spectrum Management)

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21 pages, 6459 KiB

Open AccessArticle

ATARI: A Graph Convolutional Neural Network Approach for Performance Prediction in Next-Generation WLANs

by Paola Soto, Miguel Camelo, Kevin Mets, Francesc Wilhelmi, David Góez, Luis A. Fletscher, Natalia Gaviria, Peter Hellinckx, Juan F. Botero and Steven Latré

Sensors 2021, 21(13), 4321; https://doi.org/10.3390/s21134321 - 24 Jun 2021

Cited by 15 | Viewed by 2988

Abstract

IEEE 802.11 (Wi-Fi) is one of the technologies that provides high performance with a high density of connected devices to support emerging demanding services, such as virtual and augmented reality. However, in highly dense deployments, Wi-Fi performance is severely affected by interference. This problem is even worse in new standards, such as 802.11n/ac, where new features such as Channel Bonding (CB) are introduced to increase network capacity but at the cost of using wider spectrum channels. Finding the best channel assignment in dense deployments under dynamic environments with CB is challenging, given its combinatorial nature. Therefore, the use of analytical or system models to predict Wi-Fi performance after potential changes (e.g., dynamic channel selection with CB, and the deployment of new devices) are not suitable, due to either low accuracy or high computational cost. This paper presents a novel, data-driven approach to speed up this process, using a Graph Neural Network (GNN) model that exploits the information carried in the deployment’s topology and the intricate wireless interactions to predict Wi-Fi performance with high accuracy. The evaluation results show that preserving the graph structure in the learning process obtains a 64% increase versus a naive approach, and around 55% compared to other Machine Learning (ML) approaches when using all training features. Full article

(This article belongs to the Special Issue Cognitive Radio Applications and Spectrum Management)

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24 pages, 3941 KiB

Open AccessArticle

Spectrum Occupancy Model Based on Empirical Data for FM Radio Broadcasting in Suburban Environments

by Ajalawit Chantaveerod, Kampol Woradit and Charernkiat Pochaiya

Sensors 2021, 21(12), 4015; https://doi.org/10.3390/s21124015 - 10 Jun 2021

Cited by 4 | Viewed by 2324

Abstract

It is well-known that the analog FM radio channels in suburban areas are underutilized. Before reallocating the unused channels for other applications, a regulator must analyze the spectrum occupancy. Many researchers proposed the spectrum occupancy models to find vacant spectrum. However, the existing models do not analyze each channel individually. This paper proposes an approach consisting (i) a spectrum measurement strategy, (ii) an appropriate decision threshold, and (iii) criteria for channel classification, to find the unused channels. The measurement strategy monitors each channel’s activity by capturing the power levels of the passband and the guardband separately. The decision threshold is selected depending on the monitored channel’s activity. The criteria classifies the channels based on the passband’s and guardband’s duty cycles. The results show that the proposed channel classification can identify 42 unused channels. If the power levels of wholebands (existing model) were analyzed instead of passband’s and guardband’s duty cycles, only 24 unoccupied channels were found. Furthermore, we propose the interference criteria, based on relative duty cycles across channels, to classify the abnormally used channels into interference sources and interference sinks, which have 16 and 15 channels, respectively. This information helps the dynamic spectrum sharing avoid or mitigate the interferences. Full article

(This article belongs to the Special Issue Cognitive Radio Applications and Spectrum Management)

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21 pages, 2234 KiB

Open AccessArticle

Performance Analysis of Underlay Cognitive Radio System with Self-Sustainable Relay and Statistical CSI

by Nadica Kozić, Vesna Blagojević and Predrag Ivaniš

Sensors 2021, 21(11), 3727; https://doi.org/10.3390/s21113727 - 27 May 2021

Cited by 3 | Viewed by 1766

Abstract

The relentlessly increasing number of small-sized devices with limited powering and computational capabilities requires the adoption of new approaches to spectrum access. In this paper, we analyze an underlay cooperative cognitive wireless system based on available statistical channel state information (CSI) that is applicable to the cognitive system with limited computational resources due to its low complexity. We considered the scenario where the primary and the cognitive network coexist in the same spectrum band, under the constraints of interference threshold and maximal tolerable outage permitted by the primary user. The communication in the secondary decode-and-forward (DF) relaying system is established via a self-sustainable relay, which harvests energy from both cognitive and primary transmitters. The closed-form expressions for the outage probability of the cognitive network are derived, which are valid for both time-switching relaying (TSR) and power-splitting relaying (PSR) protocols. We analyze the influence of both cognitive and primary systems as well as the impact of channel parameters on the cognitive system outage performance. The derived analytical results are corroborated by an independent simulation method. Full article

(This article belongs to the Special Issue Cognitive Radio Applications and Spectrum Management)

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21 pages, 16917 KiB

Open AccessArticle

Real-Time Implementation of Multiband Spectrum Sensing Using SDR Technology

by Yanqueleth Molina-Tenorio, Alfonso Prieto-Guerrero and Rafael Aguilar-Gonzalez

Sensors 2021, 21(10), 3506; https://doi.org/10.3390/s21103506 - 18 May 2021

Cited by 12 | Viewed by 3990

Abstract

In this work, a novel multiband spectrum sensing technique is implemented in the context of cognitive radios. This technique is based on multiresolution analysis (wavelets), machine learning, and the Higuchi fractal dimension. The theoretical contribution was developed before by the authors; however, it has never been tested in a real-time scenario. Hence, in this work, it is proposed to link several affordable software-defined radios to sense a wide band of the radioelectric spectrum using this technique. Furthermore, in this real-time implementation, the following are proposed: (i) a module for the elimination of impulsive noise, with which the appearance of sudden changes in the signal is reduced through the detail coefficients of the multiresolution analysis, and (ii) the management of different devices through an application that updates the information of each secondary user every 100 ms. The performance of these linked devices was evaluated with encouraging results: 95% probability of success for signal-to-noise ratio (SNR) values greater than 0 dB and just five samples (mean) in error of the edge detection (start and end) for a primary user transmission. Full article

(This article belongs to the Special Issue Cognitive Radio Applications and Spectrum Management)

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14 pages, 3030 KiB

Open AccessCommunication

Calculation of AeroMACS Spectrum Requirements Based on Traffic Simulator

by Hong-Gi Shin, Hyung-Jung Kim, Sang-Wook Lee, Hyun-Goo Yoon and Yong-Hoon Choi

Sensors 2021, 21(10), 3343; https://doi.org/10.3390/s21103343 - 11 May 2021

Cited by 3 | Viewed by 1663

Abstract

In this paper, we propose a methodology for calculating the necessary spectrum requirements of aeronautical mobile airport communication system (AeroMACS) to provide various airport communication services. To accurately calculate the spectrum requirement, it is necessary to evaluate the AeroMACS traffic demand of the peak time and statistical data on the packet traffic generated at the airport. Because there is no AeroMACS traffic model and real trace data, we have developed the AeroMACS traffic simulator based on the report of Single European Sky Air Traffic Management Research (SESAR). To calculate the spectrum requirements, the AeroMACS traffic simulator is combined with the methodology of ITU-R M.1768-1. The developed traffic simulator reflects AeroMACS traffic priorities and can generate the required traffic according to its location in the airport. We observed the spectrum requirement by changing the number of sectors and the spectral efficiency. To show the feasibility of our methodology, we applied it to the case of Incheon International Airport in Korea. The simulation results show that the average bandwidth of 0.94 MHz is required in the ground area and 8.59 MHz is required in the entire airport. Full article

(This article belongs to the Special Issue Cognitive Radio Applications and Spectrum Management)

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13 pages, 6166 KiB

Open AccessArticle

Automatic Modulation Classification Based on Deep Feature Fusion for High Noise Level and Large Dynamic Input

by Hui Han, Zhiyuan Ren, Lin Li and Zhigang Zhu

Sensors 2021, 21(6), 2117; https://doi.org/10.3390/s21062117 - 17 Mar 2021

Cited by 11 | Viewed by 2246

Abstract

Automatic modulation classification (AMC) is playing an increasingly important role in spectrum monitoring and cognitive radio. As communication and electronic technologies develop, the electromagnetic environment becomes increasingly complex. The high background noise level and large dynamic input have become the key problems for AMC. This paper proposes a feature fusion scheme based on deep learning, which attempts to fuse features from different domains of the input signal to obtain a more stable and efficient representation of the signal modulation types. We consider the complementarity among features that can be used to suppress the influence of the background noise interference and large dynamic range of the received (intercepted) signals. Specifically, the time-series signals are transformed into the frequency domain by Fast Fourier transform (FFT) and Welch power spectrum analysis, followed by the convolutional neural network (CNN) and stacked auto-encoder (SAE), respectively, for detailed and stable frequency-domain feature representations. Considering the complementary information in the time domain, the instantaneous amplitude (phase) statistics and higher-order cumulants (HOC) are extracted as the statistical features for fusion. Based on the fused features, a probabilistic neural network (PNN) is designed for automatic modulation classification. The simulation results demonstrate the superior performance of the proposed method. It is worth noting that the classification accuracy can reach 99.8% in the case when signal-to-noise ratio (SNR) is 0 dB. Full article

(This article belongs to the Special Issue Cognitive Radio Applications and Spectrum Management)

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Review

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26 pages, 3111 KiB

Open AccessReview

Design Guidelines for Database-Driven Internet of Things-Enabled Dynamic Spectrum Access

by Dayan A. Guimarães, Elivander J. T. Pereira, Antônio M. Alberti and Jonas V. Moreira

Sensors 2021, 21(9), 3194; https://doi.org/10.3390/s21093194 - 04 May 2021

Cited by 6 | Viewed by 2663

Abstract

The radio-frequency spectrum shortage, which is primarily caused by the fixed allocation policy, is one of the main bottlenecks to the deployment of existing wireless communication networks, and to the development of new ones. The dynamic spectrum access policy is foreseen as the solution to this problem, since it allows shared spectrum usage by primary licensed and secondary unlicensed networks. In order to turn this policy into reality, the secondary network must be capable of acquiring reliable, real-time information on available bands within the service area, which can be achieved by means of spectrum sensing, spectrum occupancy databases, or a combination of them. This Review presents guidelines related to the design of a framework that can be adopted to foster dynamic spectrum access policies. The framework applies special-purpose Internet of Things (IoT) devices that perform spectrum sensing, subsequently feeding a spectrum occupancy database, which in turn will be used by the secondary network to gather information on location-dependent spectrum availability. The guidelines address technological enablers capable of making the framework feasible, reliable and secure. Full article

(This article belongs to the Special Issue Cognitive Radio Applications and Spectrum Management)

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29 pages, 710 KiB

Open AccessReview

Spectrum Sensing for Cognitive Radio: Recent Advances and Future Challenge

by Abbass Nasser, Hussein Al Haj Hassan, Jad Abou Chaaya, Ali Mansour and Koffi-Clément Yao

Sensors 2021, 21(7), 2408; https://doi.org/10.3390/s21072408 - 31 Mar 2021

Cited by 94 | Viewed by 9855

Abstract

Spectrum Sensing (SS) plays an essential role in Cognitive Radio (CR) networks to diagnose the availability of frequency resources. In this paper, we aim to provide an in-depth survey on the most recent advances in SS for CR. We start by explaining the Half-Duplex and Full-Duplex paradigms, while focusing on the operating modes in the Full-Duplex. A thorough discussion of Full-Duplex operation modes from collision and throughput points of view is presented. Then, we discuss the use of learning techniques in enhancing the SS performance considering both local and cooperative sensing scenarios. In addition, recent SS applications for CR-based Internet of Things and Wireless Sensors Networks are presented. Furthermore, we survey the latest achievements in Spectrum Sensing as a Service, where the Internet of Things or the Wireless Sensor Networks may play an essential role in providing the CR network with the SS data. We also discuss the utilisation of CR for the 5th Generation and Beyond and its possible role in frequency allocation. With the advancement of telecommunication technologies, additional features should be ensured by SS such as the ability to explore different available channels and free space for transmission. As such, we highlight important future research axes and challenging points in SS for CR based on the current and emerging techniques in wireless communications. Full article

(This article belongs to the Special Issue Cognitive Radio Applications and Spectrum Management)

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