Evidence map›Paper›PMID 33807359›Full record

ReviewSensors (Basel, Switzerland)2021

Spectrum Sensing for Cognitive Radio: Recent Advances and Future Challenge.

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

Abstract readReview
In one paragraph

Review in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.

0numbers the graph read from it
0cells of the map it votes in
27citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

27 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Abbass NasserLABSTICC UMR CNRS 6285, ENSTA-Bretagne, 29806 Brest, France.
Hussein Al Haj HassanCCE Department, American University of Science and Technology, 1100 Beirut, Lebanon.ORCID 0000-0001-5903-0763
Jad Abou ChaayaApplied Physics Lab, Faculty of Science, Lebanese University, 1200 Jdeideh, Lebanon.
Ali MansourLABSTICC UMR CNRS 6285, ENSTA-Bretagne, 29806 Brest, France.ORCID 0000-0003-4144-8832
Koffi-Clément YaoLABSTICC UMR CNRS 6285, Université de Bretagne Occidentale, 29238 Brest, France.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

5GB5Gchannel sensingcognitive radiofull-duplexhalf-duplexInterference Sensinginternet of thingsmachine learningspectrum sensingwireless sensor network

Identifiers

PMID33807359
PMCPMC8037136

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.