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Special Issue "Cognitive Radio Sensing and Sensor Networks"

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

Deadline for manuscript submissions: closed (31 August 2017)

Special Issue Editors

Guest Editor
Prof. Dr. Francisco Javier Falcone Lanas

Associate Professor, Department of Electrical Engineering, Universidad Publica de Navarra, UPNA, Spain
Website1 | Website2 | E-Mail
Interests: wireless networks; performance evaluation; distributed systems; context aware environments; IoT; next generation wireless systems
Guest Editor
Prof. Dr. Ana Alejos

Escuela de Ingeniería de Telecomunicación, Campus Universitario Lagoas-Marcosende, 36310 VIGO (Pontevedra), Spain
Website | E-Mail
Phone: +34- 986811949
Interests: wireless networks; radio propagation; dispersive media; precursors; radar technology
Guest Editor
Prof. Dr. Leyre Azpilicueta

School of Engineering and Sciences, Tecnológico de Monterrey, Monterrey, NL., Mexico
Website | E-Mail
Phone: +5281-582082 (ext. 5362)
Interests: wireless networks; deterministic channel modelling; vehicular networks; intelligent transportation systems; context aware environments

Special Issue Information

Dear Colleagues,

The advent of context aware environments, as well as the evolution of wireless communication systems, is leading towards HetNet operation with a vast amount of potential connected devices. Within this context, spectrum management, as well as coverage/capacity analyses, is a key element in order to provide adequate quality of service metrics. Spectrum management is therefore compulsory, aided by techniques derived from the use of cognitive radio/cognitive network approaches in order to dynamically handle complex multi-system environments. This can be further extended to Device 2 Device (D2D) and Machine 2 Machine (M2M) communications, in which sensor networks are one of the main elements in order to enable context interactivity.

This Special Issue aims to highlight advances in the development, testing, and modeling of Cognitive Radio Sensing and Sensor Networks, within the broad area of potential application of such systems. Topics include, but are not limited to:

  • Cognitive Radio proposals applied to sensor network deployment
  • Cognitive Radio within Cognitive Network architectures
  • Cognitive Radio and Sensor Network testbeds
  • Practical applications of Cognitive Radio and Sensor Networks
  • Spectrum Handling and Energy Efficiency within Sensor Network systems

Prof. Dr. Francisco Falcone
Prof. Dr. Ana Alejos
Prof. Dr. Leyre Azpilicueta

Guest Editors

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 monthly 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 1800 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

  • Cognitive Radio
  • Cognitive Networks
  • Spectrum Brokerage and Handling
  • Interference Analysis and Sensor Aggregation

Published Papers (13 papers)

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Research

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Open AccessArticle Robust Weighted Sum Harvested Energy Maximization for SWIPT Cognitive Radio Networks Based on Particle Swarm Optimization
Sensors 2017, 17(10), 2275; doi:10.3390/s17102275
Received: 31 July 2017 / Revised: 27 September 2017 / Accepted: 2 October 2017 / Published: 6 October 2017
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Abstract
In this paper, we consider multiuser simultaneous wireless information and power transfer (SWIPT) for cognitive radio systems where a secondary transmitter (ST) with an antenna array provides information and energy to multiple single-antenna secondary receivers (SRs) equipped with a power splitting (PS) receiving
[...] Read more.
In this paper, we consider multiuser simultaneous wireless information and power transfer (SWIPT) for cognitive radio systems where a secondary transmitter (ST) with an antenna array provides information and energy to multiple single-antenna secondary receivers (SRs) equipped with a power splitting (PS) receiving scheme when multiple primary users (PUs) exist. The main objective of the paper is to maximize weighted sum harvested energy for SRs while satisfying their minimum required signal-to-interference-plus-noise ratio (SINR), the limited transmission power at the ST, and the interference threshold of each PU. For the perfect channel state information (CSI), the optimal beamforming vectors and PS ratios are achieved by the proposed PSO-SDR in which semidefinite relaxation (SDR) and particle swarm optimization (PSO) methods are jointly combined. We prove that SDR always has a rank-1 solution, and is indeed tight. For the imperfect CSI with bounded channel vector errors, the upper bound of weighted sum harvested energy (WSHE) is also obtained through the S-Procedure. Finally, simulation results demonstrate that the proposed PSO-SDR has fast convergence and better performance as compared to the other baseline schemes. Full article
(This article belongs to the Special Issue Cognitive Radio Sensing and Sensor Networks)
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Open AccessArticle On Optimal Cooperative Sensing with Energy Detection in Cognitive Radio
Sensors 2017, 17(9), 2111; doi:10.3390/s17092111
Received: 2 August 2017 / Revised: 10 September 2017 / Accepted: 11 September 2017 / Published: 15 September 2017
Cited by 2 | PDF Full-text (1342 KB) | HTML Full-text | XML Full-text
Abstract
In this paper, we propose an optimal cooperative sensing technique for cognitive radio to maximize sensing performance based on energy detection. In most spectrum sensing research, many cooperation methods have been proposed to overcome the sensitivity of energy detection so that both primary
[...] Read more.
In this paper, we propose an optimal cooperative sensing technique for cognitive radio to maximize sensing performance based on energy detection. In most spectrum sensing research, many cooperation methods have been proposed to overcome the sensitivity of energy detection so that both primary and secondary users are better off in terms of spectral efficiency. However, without assigning a proper sensing threshold to each sensing node, cooperation may not be effective unless the received average primary user signal-to-noise ratio (SNR) is identical. We show that equal threshold energy detection severely degrades sensing performance when cooperative sensing nodes experience diverse average SNRs, and it is not unusual for even single-node sensing to be better than cooperative sensing. To this end, based on the Neyman–Pearson criterion we formulate an optimization problem to maximize sensing performance by using optimized thresholds. Since this is a non-convex optimization problem, we provide a condition that convexifies the problem and thus serves as a sufficient optimality condition. We find that, perhaps surprisingly, in all practical cases one may consider this condition satisfied, and thus optimal sensing performance can be obtained. Through extensive simulations, we demonstrate that the proposed technique achieves a globally optimal solution, i.e., it maximizes the probability of detection under practical operating parameters such as the target probability of false alarm, different SNRs, and the number of cooperative sensing nodes. Full article
(This article belongs to the Special Issue Cognitive Radio Sensing and Sensor Networks)
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Open AccessArticle Achieving Congestion Mitigation Using Distributed Power Control for Spectrum Sensor Nodes in Sensor Network-Aided Cognitive Radio Ad Hoc Networks
Sensors 2017, 17(9), 2132; doi:10.3390/s17092132
Received: 13 August 2017 / Revised: 9 September 2017 / Accepted: 13 September 2017 / Published: 15 September 2017
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Abstract
The data sequence of spectrum sensing results injected from dedicated spectrum sensor nodes (SSNs) and the data traffic from upstream secondary users (SUs) lead to unpredictable data loads in a sensor network-aided cognitive radio ad hoc network (SN-CRN). As a result, network congestion
[...] Read more.
The data sequence of spectrum sensing results injected from dedicated spectrum sensor nodes (SSNs) and the data traffic from upstream secondary users (SUs) lead to unpredictable data loads in a sensor network-aided cognitive radio ad hoc network (SN-CRN). As a result, network congestion may occur at a SU acting as fusion center when the offered data load exceeds its available capacity, which degrades network performance. In this paper, we present an effective approach to mitigate congestion of bottlenecked SUs via a proposed distributed power control framework for SSNs over a rectangular grid based SN-CRN, aiming to balance resource load and avoid excessive congestion. To achieve this goal, a distributed power control framework for SSNs from interior tier (IT) and middle tier (MT) is proposed to achieve the tradeoff between channel capacity and energy consumption. In particular, we firstly devise two pricing factors by considering stability of local spectrum sensing and spectrum sensing quality for SSNs. By the aid of pricing factors, the utility function of this power control problem is formulated by jointly taking into account the revenue of power reduction and the cost of energy consumption for IT or MT SSN. By bearing in mind the utility function maximization and linear differential equation constraint of energy consumption, we further formulate the power control problem as a differential game model under a cooperation or noncooperation scenario, and rigorously obtain the optimal solutions to this game model by employing dynamic programming. Then the congestion mitigation for bottlenecked SUs is derived by alleviating the buffer load over their internal buffers. Simulation results are presented to show the effectiveness of the proposed approach under the rectangular grid based SN-CRN scenario. Full article
(This article belongs to the Special Issue Cognitive Radio Sensing and Sensor Networks)
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Open AccessArticle Joint Resource Optimization for Cognitive Sensor Networks with SWIPT-Enabled Relay
Sensors 2017, 17(9), 2093; doi:10.3390/s17092093
Received: 28 July 2017 / Revised: 24 August 2017 / Accepted: 4 September 2017 / Published: 13 September 2017
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Abstract
Energy-constrained wireless networks, such as wireless sensor networks (WSNs), are usually powered by fixed energy supplies (e.g., batteries), which limits the operation time of networks. Simultaneous wireless information and power transfer (SWIPT) is a promising technique to prolong the lifetime of energy-constrained wireless
[...] Read more.
Energy-constrained wireless networks, such as wireless sensor networks (WSNs), are usually powered by fixed energy supplies (e.g., batteries), which limits the operation time of networks. Simultaneous wireless information and power transfer (SWIPT) is a promising technique to prolong the lifetime of energy-constrained wireless networks. This paper investigates the performance of an underlay cognitive sensor network (CSN) with SWIPT-enabled relay node. In the CSN, the amplify-and-forward (AF) relay sensor node harvests energy from the ambient radio-frequency (RF) signals using power splitting-based relaying (PSR) protocol. Then, it helps forward the signal of source sensor node (SSN) to the destination sensor node (DSN) by using the harvested energy. We study the joint resource optimization including the transmit power and power splitting ratio to maximize CSN’s achievable rate with the constraint that the interference caused by the CSN to the primary users (PUs) is within the permissible threshold. Simulation results show that the performance of our proposed joint resource optimization can be significantly improved. Full article
(This article belongs to the Special Issue Cognitive Radio Sensing and Sensor Networks)
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Open AccessArticle Energy-Efficient Optimal Power Allocation in Integrated Wireless Sensor and Cognitive Satellite Terrestrial Networks
Sensors 2017, 17(9), 2025; doi:10.3390/s17092025
Received: 22 July 2017 / Revised: 29 August 2017 / Accepted: 31 August 2017 / Published: 4 September 2017
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Abstract
This paper proposes novel satellite-based wireless sensor networks (WSNs), which integrate the WSN with the cognitive satellite terrestrial network. Having the ability to provide seamless network access and alleviate the spectrum scarcity, cognitive satellite terrestrial networks are considered as a promising candidate for
[...] Read more.
This paper proposes novel satellite-based wireless sensor networks (WSNs), which integrate the WSN with the cognitive satellite terrestrial network. Having the ability to provide seamless network access and alleviate the spectrum scarcity, cognitive satellite terrestrial networks are considered as a promising candidate for future wireless networks with emerging requirements of ubiquitous broadband applications and increasing demand for spectral resources. With the emerging environmental and energy cost concerns in communication systems, explicit concerns on energy efficient resource allocation in satellite networks have also recently received considerable attention. In this regard, this paper proposes energy-efficient optimal power allocation schemes in the cognitive satellite terrestrial networks for non-real-time and real-time applications, respectively, which maximize the energy efficiency (EE) of the cognitive satellite user while guaranteeing the interference at the primary terrestrial user below an acceptable level. Specifically, average interference power (AIP) constraint is employed to protect the communication quality of the primary terrestrial user while average transmit power (ATP) or peak transmit power (PTP) constraint is adopted to regulate the transmit power of the satellite user. Since the energy-efficient power allocation optimization problem belongs to the nonlinear concave fractional programming problem, we solve it by combining Dinkelbach’s method with Lagrange duality method. Simulation results demonstrate that the fading severity of the terrestrial interference link is favorable to the satellite user who can achieve EE gain under the ATP constraint comparing to the PTP constraint. Full article
(This article belongs to the Special Issue Cognitive Radio Sensing and Sensor Networks)
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Open AccessArticle Full-Duplex Cooperative Sensing for Spectrum-Heterogeneous Cognitive Radio Networks
Sensors 2017, 17(8), 1773; doi:10.3390/s17081773
Received: 10 July 2017 / Revised: 28 July 2017 / Accepted: 31 July 2017 / Published: 2 August 2017
Cited by 1 | PDF Full-text (1778 KB) | HTML Full-text | XML Full-text
Abstract
In cognitive radio networks (CRNs), spectrum sensing is critical for guaranteeing that the opportunistic spectrum access by secondary users (SUs) will not interrupt legitimate primary users (PUs). The application of full-duplex radio to spectrum sensing enables SU to carry out sensing and transmission
[...] Read more.
In cognitive radio networks (CRNs), spectrum sensing is critical for guaranteeing that the opportunistic spectrum access by secondary users (SUs) will not interrupt legitimate primary users (PUs). The application of full-duplex radio to spectrum sensing enables SU to carry out sensing and transmission simultaneously, improving both spectrum awareness and CRN throughput. However, the issue of spectrum sensing with full-duplex radios deployed in heterogeneous environments, where SUs may observe different spectrum activities, has not been addressed. In this paper, we give a first look into this problem and develop a light-weight cooperative sensing framework called PaCoSIF, which involves only a pairwise SU transmitter (SU-Tx) and its receiver (SU-Rx) in cooperation. A dedicated control channel is not required for pairwise cooperative sensing with instantaneous feedback (PaCoSIF) because sensing results are collected and fused via the reverse channel provided by full-duplex radios. We present a detailed protocol description to illustrate how PaCoSIF works. However, it is a challenge to optimize the sensing performance of PaCoSIF since the two sensors suffer from spectrum heterogeneity and different kinds of interference. Our goal is to minimize the false alarm rate of PaCoSIF given the bound on the missed detection rate by adaptively adjusting the detection threshold of each sensor. We derive an expression for the optimal threshold using the Lagrange method and propose a fast binary-searching algorithm to solve it numerically. Simulations show that, with perfect signal-to-interference-and-noise-ratio (SINR) information, PaCoSIF could decrease the false alarm rate and boost CRN throughput significantly against conventional cooperative sensing when SUs are deployed in spectrum-heterogeneous environments. Finally, the impact of SINR error upon the performance of PaCoSIF is evaluated via extensive simulations. Full article
(This article belongs to the Special Issue Cognitive Radio Sensing and Sensor Networks)
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Open AccessArticle In-Network Computation of the Optimal Weighting Matrix for Distributed Consensus on Wireless Sensor Networks
Sensors 2017, 17(8), 1702; doi:10.3390/s17081702
Received: 17 May 2017 / Revised: 3 July 2017 / Accepted: 21 July 2017 / Published: 25 July 2017
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Abstract
In a network, a distributed consensus algorithm is fully characterized by its weighting matrix. Although there exist numerical methods for obtaining the optimal weighting matrix, we have not found an in-network implementation of any of these methods that works for all network topologies.
[...] Read more.
In a network, a distributed consensus algorithm is fully characterized by its weighting matrix. Although there exist numerical methods for obtaining the optimal weighting matrix, we have not found an in-network implementation of any of these methods that works for all network topologies. In this paper, we propose an in-network algorithm for finding such an optimal weighting matrix. Full article
(This article belongs to the Special Issue Cognitive Radio Sensing and Sensor Networks)
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Open AccessArticle Joint Optimization of Receiver Placement and Illuminator Selection for a Multiband Passive Radar Network
Sensors 2017, 17(6), 1378; doi:10.3390/s17061378
Received: 10 March 2017 / Revised: 31 May 2017 / Accepted: 1 June 2017 / Published: 14 June 2017
Cited by 1 | PDF Full-text (1966 KB) | HTML Full-text | XML Full-text
Abstract
The performance of a passive radar network can be greatly improved by an optimal radar network structure. Generally, radar network structure optimization consists of two aspects, namely the placement of receivers in suitable places and selection of appropriate illuminators. The present study investigates
[...] Read more.
The performance of a passive radar network can be greatly improved by an optimal radar network structure. Generally, radar network structure optimization consists of two aspects, namely the placement of receivers in suitable places and selection of appropriate illuminators. The present study investigates issues concerning the joint optimization of receiver placement and illuminator selection for a passive radar network. Firstly, the required radar cross section (RCS) for target detection is chosen as the performance metric, and the joint optimization model boils down to the partition p-center problem (PPCP). The PPCP is then solved by a proposed bisection algorithm. The key of the bisection algorithm lies in solving the partition set covering problem (PSCP), which can be solved by a hybrid algorithm developed by coupling the convex optimization with the greedy dropping algorithm. In the end, the performance of the proposed algorithm is validated via numerical simulations. Full article
(This article belongs to the Special Issue Cognitive Radio Sensing and Sensor Networks)
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Open AccessArticle Spectrum Sensing Using Co-Prime Array Based Modulated Wideband Converter
Sensors 2017, 17(5), 1052; doi:10.3390/s17051052
Received: 6 March 2017 / Revised: 29 April 2017 / Accepted: 2 May 2017 / Published: 6 May 2017
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Abstract
As known to us all, it is challenging to monitor wideband signals in frequency domain due to the restriction of hardware. Several practical sampling schemes, such as multicoset sampling and the modulated wideband converter (MWC), have been proposed. In this work, a co-prime
[...] Read more.
As known to us all, it is challenging to monitor wideband signals in frequency domain due to the restriction of hardware. Several practical sampling schemes, such as multicoset sampling and the modulated wideband converter (MWC), have been proposed. In this work, a co-prime array (CA) based modulated wideband converter (MWC) spectrum sensing method is suggested. Our proposed method has the same sampling principle as the MWC but has some advantages compared to MWC. Firstly, CA-based MWC is an array-based MWC system. Each sensor is usually corrupted by independent noise for an array system which can be used for noise averaging, while all channels in conventional MWC have the same receiving noise. Secondly, by incorporating the co-prime array, we can estimate the power spectrum of signal directly employing its second-order statistical properties. Moreover, the system minimal sampling rate can be reduced further because of the reduction of sampling channels. Simulation results show that our method has better performance than traditional methods. Full article
(This article belongs to the Special Issue Cognitive Radio Sensing and Sensor Networks)
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Open AccessArticle Design of a Single Channel Modulated Wideband Converter for Wideband Spectrum Sensing: Theory, Architecture and Hardware Implementation
Sensors 2017, 17(5), 1035; doi:10.3390/s17051035
Received: 3 March 2017 / Revised: 13 April 2017 / Accepted: 2 May 2017 / Published: 4 May 2017
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Abstract
In a cognitive radio sensor network (CRSN), wideband spectrum sensing devices which aims to effectively exploit temporarily vacant spectrum intervals as soon as possible are of great importance. However, the challenge of increasingly high signal frequency and wide bandwidth requires an extremely high
[...] Read more.
In a cognitive radio sensor network (CRSN), wideband spectrum sensing devices which aims to effectively exploit temporarily vacant spectrum intervals as soon as possible are of great importance. However, the challenge of increasingly high signal frequency and wide bandwidth requires an extremely high sampling rate which may exceed today’s best analog-to-digital converters (ADCs) front-end bandwidth. Recently, the newly proposed architecture called modulated wideband converter (MWC), is an attractive analog compressed sensing technique that can highly reduce the sampling rate. However, the MWC has high hardware complexity owing to its parallel channel structure especially when the number of signals increases. In this paper, we propose a single channel modulated wideband converter (SCMWC) scheme for spectrum sensing of band-limited wide-sense stationary (WSS) signals. With one antenna or sensor, this scheme can save not only sampling rate but also hardware complexity. We then present a new, SCMWC based, single node CR prototype System, on which the spectrum sensing algorithm was tested. Experiments on our hardware prototype show that the proposed architecture leads to successful spectrum sensing. And the total sampling rate as well as hardware size is only one channel’s consumption of MWC. Full article
(This article belongs to the Special Issue Cognitive Radio Sensing and Sensor Networks)
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Open AccessArticle Combined Pre-Distortion and Censoring for Bandwidth-Efficient and Energy-Efficient Fusion of Spectrum Sensing Information
Sensors 2017, 17(3), 654; doi:10.3390/s17030654
Received: 13 January 2017 / Revised: 23 February 2017 / Accepted: 14 March 2017 / Published: 22 March 2017
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Abstract
This paper describes a novel scheme for the fusion of spectrum sensing information in cooperative spectrum sensing for cognitive radio applications. The scheme combines a spectrum-efficient, pre-distortion-based fusion strategy with an energy-efficient censoring-based fusion strategy to achieve the combined effect of reduction in
[...] Read more.
This paper describes a novel scheme for the fusion of spectrum sensing information in cooperative spectrum sensing for cognitive radio applications. The scheme combines a spectrum-efficient, pre-distortion-based fusion strategy with an energy-efficient censoring-based fusion strategy to achieve the combined effect of reduction in bandwidth and power consumption during the transmissions of the local decisions to the fusion center. Expressions for computing the key performance metrics of the spectrum sensing of the proposed scheme are derived and validated by means of computer simulations. An extensive analysis of the overall energy efficiency is made, along with comparisons with reference strategies proposed in the literature. It is demonstrated that the proposed fusion scheme can outperform the energy efficiency attained by these reference strategies. Moreover, it attains approximately the same global decision performance of the best among these strategies. Full article
(This article belongs to the Special Issue Cognitive Radio Sensing and Sensor Networks)
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Review

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Open AccessReview Medium Access Control Protocols for Cognitive Radio Ad Hoc Networks: A Survey
Sensors 2017, 17(9), 2136; doi:10.3390/s17092136
Received: 21 July 2017 / Revised: 26 August 2017 / Accepted: 7 September 2017 / Published: 16 September 2017
PDF Full-text (461 KB) | HTML Full-text | XML Full-text
Abstract
New wireless network paradigms will demand higher spectrum use and availability to cope with emerging data-hungry devices. Traditional static spectrum allocation policies cause spectrum scarcity, and new paradigms such as Cognitive Radio (CR) and new protocols and techniques need to be developed in
[...] Read more.
New wireless network paradigms will demand higher spectrum use and availability to cope with emerging data-hungry devices. Traditional static spectrum allocation policies cause spectrum scarcity, and new paradigms such as Cognitive Radio (CR) and new protocols and techniques need to be developed in order to have efficient spectrum usage. Medium Access Control (MAC) protocols are accountable for recognizing free spectrum, scheduling available resources and coordinating the coexistence of heterogeneous systems and users. This paper provides an ample review of the state-of-the-art MAC protocols, which mainly focuses on Cognitive Radio Ad Hoc Networks (CRAHN). First, a description of the cognitive radio fundamental functions is presented. Next, MAC protocols are divided into three groups, which are based on their channel access mechanism, namely time-slotted protocol, random access protocol and hybrid protocol. In each group, a detailed and comprehensive explanation of the latest MAC protocols is presented, as well as the pros and cons of each protocol. A discussion on future challenges for CRAHN MAC protocols is included with a comparison of the protocols from a functional perspective. Full article
(This article belongs to the Special Issue Cognitive Radio Sensing and Sensor Networks)
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Other

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Open AccessConcept Paper Cooperation Techniques between LTE in Unlicensed Spectrum and Wi-Fi towards Fair Spectral Efficiency
Sensors 2017, 17(9), 1994; doi:10.3390/s17091994
Received: 7 July 2017 / Revised: 21 August 2017 / Accepted: 28 August 2017 / Published: 31 August 2017
Cited by 1 | PDF Full-text (4784 KB) | HTML Full-text | XML Full-text
Abstract
On the road towards 5G, a proliferation of Heterogeneous Networks (HetNets) is expected. Sensor networks are of great importance in this new wireless era, as they allow interaction with the environment. Additionally, the establishment of the Internet of Things (IoT) has incredibly increased
[...] Read more.
On the road towards 5G, a proliferation of Heterogeneous Networks (HetNets) is expected. Sensor networks are of great importance in this new wireless era, as they allow interaction with the environment. Additionally, the establishment of the Internet of Things (IoT) has incredibly increased the number of interconnected devices and consequently the already massive wirelessly transmitted traffic. The exponential growth of wireless traffic is pushing the wireless community to investigate solutions that maximally exploit the available spectrum. Recently, 3rd Generation Partnership Project (3GPP) announced standards that permit the operation of Long Term Evolution (LTE) in the unlicensed spectrum in addition to the exclusive use of the licensed spectrum owned by a mobile operator. Alternatively, leading wireless technology developers examine standalone LTE operation in the unlicensed spectrum without any involvement of a mobile operator. In this article, we present a classification of different techniques that can be applied on co-located LTE and Wi-Fi networks. Up to today, Wi-Fi is the most widely-used wireless technology in the unlicensed spectrum. A review of the current state of the art further reveals the lack of cooperation schemes among co-located networks that can lead to more optimal usage of the available spectrum. This article fills this gap in the literature by conceptually describing different classes of cooperation between LTE and Wi-Fi. For each class, we provide a detailed presentation of possible cooperation techniques that can provide spectral efficiency in a fair manner. Full article
(This article belongs to the Special Issue Cognitive Radio Sensing and Sensor Networks)
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