phd research topics in cognitive radio

Open Research Areas in Cognitive Radios

• September 2012
• Conference: Computational Intelligence, Modelling and Simulation (CIMSiM), 2012 Fourth International Conference on

• Center For Advanced Studies In Engineering

• Ghulam Ishaq Khan Institute of Engineering Sciences and Technology

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Spectrum sensing for cognitive radio: recent advances and future challenge.

1. Introduction

• Underlay Access: the SU may transmit simultaneously with the PU over the same channel. However, the transmitted power should not exceed a certain threshold in order to keep the interference on PU below a tolerable value [ 4 , 9 ].
• Overlay Access: the SU may transmit simultaneously with the PU on the same channel up to its maximum power, but at the cost of playing a role of relay between two or more PUs [ 10 , 11 ]. In this case, the SU sends its data while relaying the PUs. This kind of access requires high level of cooperation between PUs and SUs, which may expose the PUs privacy.
• Interweave Access: SU is allowed to transmit using its maximum power only when PU is absent. This paradigm is also known as the classical CR and it is the focus of this paper given its popularity.
• A state of the art on the classical SS techniques is provided
• The operating modes of CR derived from involving the FD tool in CR are detailed and investigated
• The role of Machine and Deep Learning in enhancing the SS is surveyed, where we analyzed the contributions of these techniques from local sensing and cooperative sensing levels
• Using SS in IoT/WSN and the latest achievements in both Spectrum Sensing as a Service and Dynamic Spectrum Sharing for IoT/WSN networks are surveyed.
• The possible application of CR, especially SS, in the 5G and the upcoming technologies is discussed
• New trends and challenges related to the future wireless communication technologies are also discussed and investigated.

2. Half-Duplex Cognitive Radio: Listen Before Talk

Detection criteria.

• Incremental Energy When PU starts to transmit, the energy of the received signal will be incremented compared to the noise-only case. By estimating previously the power of the stationary noise, and by comparing the energy of the received signal to a pre-defined threshold depending on the noise power, SU decides whether the channel is occupied by a PU signal or not. Many detectors are based on this criterion, the most known is the traditional ED [ 30 , 31 ]. Other detectors such as the Cumulative Power Spectral Density (CPSD) detector [ 29 ], cyclo-energy detector [ 32 ] and generalized ED [ 33 , 34 , 35 ] are based on differentiating between the energy of the received signal with and without the presence of PU’s signal. It is worth mentioning that the generalized ED may use a power exponent p ≠ 2 in the definition of it as an extension of the ED Test Statistic, which is based on the energy of the received signal (i.e., p = 2 ). However, the energy-based detectors face the problem of Noise Uncertainty (NU), which occurs when the noise power becomes time-dependent. This phenomena adversely impacts the SS performance of these detectors [ 36 ].
• PU signal pattern The features of the communication signals can be exploited by the SU to distinguish them from the noise. Processes, such as the modulation, oversampling, sine-wave carrier, adding a cyclic prefix (e.g., for the OFDM signal), etc. do not exist in the noise. Several detectors were proposed in the literature by exploiting these characteristics such as the Cyclo-stationary Detector (CSD) [ 37 , 38 , 39 ], which distinguishes the PU signal from the noise based on the cyclic features caused by the modulation, the sinewave carrier etc. Other detectors such as Auto-Correlation Detector (ACD) [ 40 ] and Eigenvalue-based Detector (EVD) [ 41 ] exploit the correlation presented in the PU signal due to the oversampling and cyclic prefix. The main advantage of such detectors is their independence of the noise variance, which also overcome the NU problem. Nevertheless, these detectors are more computationally complicated than the classical ED. Furthermore, cyclic frequencies of the PU signal should be known to apply CSD. This requires cooperation between SU and PU. Moreover, some detectors, such as Goodness of Fit (GoF) test [ 42 , 43 ] and Kurtosis detectors [ 44 ], detect the PU signal using the statistics of the communication signals, which are different from the statistics of noise. Thus, the noise’s distribution should be a priori known to the SU.
• PU signal’s waveform Sending a pilot signal is widely used by telecommunication standards to establish communication with a receiver by ensuring time synchronization, channel estimation, etc. A known PU pilot signal can be used by the SU to detect PU activity. Waveform or Matched filter detector correlates the received signal with the known PU pilot signal in order to analyze the channel opportunity [ 45 , 46 ]. Even though this detector is an optimal one, it requires knowledge of the PU signal with perfect time and frequency synchronization. Therefore, the application of this detector in CR becomes challenging, where the SU may deal with a great variety of signals.
• The SS is not performed during the transmission slot, and thus the SU becomes unaware of the PU activity during this slot. This may lead to harmful interference with PU if the PU starts transmitting in this slot.
• The secondary throughput is affected by the silence duration (sensing time), since the SU should stay silent during the sensing slot.

3. Full-Duplex Cognitive Radio: Listen and Talk

3.1. self-interference cancellation, 3.2. transmit-sense, 3.3. transmit-receive, 3.3.1. is-based transmit-receive, 3.3.2. ss-based transmit-receive, 4. learning techniques for spectrum sensing, 4.1. local spectrum sensing, 4.2. cooperative spectrum sensing, 5. wireless sensor network and cognitive radio, 5.1. spectrum sensing as a service, 5.2. dynamic spectrum sharing for wsn communication, 6. cognitive radio application for 5g and beyond 5g, 6.1. 3gpp technologies, 6.2. compressive sensing, 6.3. beamforming-based communication, 7. future challenges.

• Channel Coding for Interference Sensing: IS is mainly applied in the TR mode of the FDCR. Being able to use only one available channel to establish bidirectional communication between two SUs, TR becomes very attractive since it doubles the frequency efficiency compared to TS mode and HDCR [ 65 , 78 ]. TR uses signal decoding to reveal the PU status, which depends on the adopted channel coding technique. The weak technique may deteriorate the performance of the secondary network, while the strong technique may allow SU to decode the received signal even if PU is active. Here, the challenge becomes how to choose the optimal channel coding technique that matches the quality of service of SUs and, at the same time, does not prevent SU from detecting PU.
• Switching protocols between CR functioning modes: Existing techniques for switching from a CR mode to another only take into account PU statistics [ 72 , 76 , 78 ]. However, other parameters may be taken into consideration such as the energy and frequency resources, since each mode has different requirements. Modes that are based on SIC, such as TS and TR, require more hardware and energy resources. This is not always available, especially for the battery-powered devices, which are planned to serve for several years such as the LPWAN IoT devices. The adoption of a mode depends on the available frequency resources: TR may be one of the good choices since it requires only one channel to establish bidirectional communication between two peer SUs, but it suffers poor sensing performance at low PU SNR. Thus, both frequency and energy efficiencies are important factors that should be taken into account to make a suitable choice of the mode to adopt. With this large number of parameters, learning techniques can be extremely useful to indicate the most suitable mode to adopt by the SUs.
• Access Strategy for IoT/WSN networks: In IoT applications, contention between SUs is high due to the large number of sensors. Thus, the adopted spectrum sharing strategy in such application becomes of high importance to effectively manage the access of different types of sensors [ 206 , 207 , 208 ]. This strategy may be related to the data type to be sent by the sensor, the redundancy of the data (redundant data could be ignored or compressed) and the criticality. Sensors looking for transmitting critical data, especially those related to natural disasters and e-healthcare, may be prioritized over the other sensors. A strategy giving the sensors a weight is a common approach in WSN to alleviate interference [ 209 ]. Such a strategy may be useful in CR-IoT applications to manage the access of the nodes on the available frequency channels and maximize spectral efficiency.
• Exchange Protocol of SS data for IoT/WSN Developing adequate protocols for CR-IoT systems is essential to manage the exchange between the central entity of the IoT network and the nodes [ 145 , 210 , 211 ]. This includes requests for nodes to sense a given channel and informing the concerned nodes with the channel availability updates. For sensing requests, the energy need of the IoT nodes should be highly considered especially when the nodes are battery-based. In this regard, selecting the sensors to sense the channel, the number of sensing processes per day and the maximal sensing observation time of the sensor should be determined by the central entity of the IoT/WSN network to ensure effective utilization of the resources. Moreover, the nodes that want to send data should be informed by the central entity about the available channels a priori. Thus, effective protocols should be designed to ensure the time and the frequency synchronization between the end-nodes and the central entity.
• Use of Intelligent Reflecting Surfaces: SS may benefit from the emerging Intelligent Reflecting Surface (IRS), which is expected to play an essential role in 5G and B5G technologies [ 212 , 213 ]. IRS can passively reflect the signal towards a target receiver. IRS is a potential candidate to help to overcome the hidden PU problem by reflecting the PU signal towards the SUs, which suffer from low PU SNR. Several challenges are expected in using IRS to assist SS, since the optimal configuration of the IRS system depends on the channel between PU and IRS, IRS and SU, and PU and SU. In a context, where no cooperation is available between SU and PU, channel estimation becomes hard to apply. Blind channel estimation and cascaded-channel estimation could be a good candidate to help the IRS application for SS assistance [ 214 ].
• Sensing the Spatial Dimension for CR Beam-based sensing of PUs becomes more and more important for SUs since it provides the SU with the spatial availability of the spectrum. However, the PU’s transmission beam estimation remains challenging for SU especially where no cooperation is available with PU [ 200 , 215 ]. Even when the PU beam is known, adjusting the SU beam is challenging too due to the inevitable interference caused by the SU transmitter to the SU receiver. Thus, the transmit power, the beam direction, and the number of transmit antennas should be carefully adjusted. However, due to the need for multiple antennas to adjust the SU beam, applying beam-based CR is challenging for low-cost IoT/WSN devices.
• Towards Intelligent Spectrum Sensing: With the massive small cell deployment and Massive Machine-Type Communication in 5G and B5G, the binary decision of the SS may not be efficient. In such a deployment the SS output may be vulnerable to a high false alarm rate due to the inter-cell interference, i.e., a given channel is free in the cell where SU exists, but SU may falsely detect the presence of PU due to the inter-cell interference coming from another cell [ 216 ]. For this reason, a more intelligent and flexible SS technique should be adopted to overcome the homogeneity assumption of the PU coverage [ 129 ]. This means that the SU should be able to diagnose the channel as free even though PU is detected in some circumstances. Moreover, SS should be extended to deal with spectrum perception and environment dynamics learning. This is extremely important especially for battery-power devices to enable joint channel sensing and access.

8. Conclusions

Author contributions, conflicts of interest.

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Click here to enlarge figure

Abbreviation	Definition
5G	Fifth Generation
ACD	Autocorrelation Detector
ADC	Analog to Digital Converter
B5G	Beyond 5G
CPSD	Cumulative Power Spectral Density
BS	Base Station
CR	Cognitive Radio
CS	Compressive Sensing
CSAT	Carrier Sensing Adaptive Transmission
CSD	Cyclo-Stationary Detector
DCS	Dynamic Channel Selection
DL	Deep Learning
DSA	Dynamic Spectrum Allocation
ED	Energy Detector
eMBB	enhanced Mobile Broad-Band
EVD	Eigen Value based Detector
FDCR	Full-Duplex Cognitive Radio
FC	Fusion Center
GoF	Goodness of Fit
HDCR	Half-Duplex Cognitive Radio
HSS	Hybrid Spectrum Sensing
IBFD	In-Band Full-Duplex
IS	Intereference Sensing
IoE	Internet of Everything
IoT	Internet of Things
IRS	Intelligent Reflecting Surface
LAT	Listen and Talk
LBT	Listen Before Talk
LPWAN	Low-Power Wide Area Network
LTE	Long Term Evolution
LTE-LAA	LTE-Licensed Assisted Access
LTE-U	LTE-Unlicensed
ML	Machine Learning
mMTC	Massive Machine-Type Communication
NU	Noise Uncertainty
OFDM	Orthogonal Frequency Multiple Access
PU	Primary User
RSI	Residual Self-Interference
SDR	Soft Defined Network
SI	Self-Interference
SIC	Self-Interference Cancellation
SS	Spectrum Sensing
SNIR	Signal to Noise and Interference Ratio
SNR	Signal to Noise Ratio
SSaas	Spectrum Sensing as a Service
SU	Secondary User
SVM	Support-Vector Machine
TR	Transmit-Receive
TS	Transmit-Sense
URLLC	Ultra Reliable and Low Latency Communication
WBS	Wide Band Sensing
WFD	Waveform Detector
WSN	Wireless Sensor Network

Detector	Requires PU-SU Cooperation?	Affected by NU?	Computational Complexity	Remarks
	No	Yes		[ , ]
	No	Yes		is related to the adopted power exponent. Please refer to [ ]
	Yes	No		L is an odd number and stands for the length of a unit window used in CSD [ , , , ]
	No	No		K is the smoothing factor, is the oversampling factor [ , ]
	No	No		is the oversampling factor [ , ]
	Yes	No		M is the number of blocks used to evaluate the WFD [ ]
	No	Yes		[ ]
	No	No		[ ]
	No	No		[ , ]

Mode	Reliable SS at Low SNR	Collision Time	Needs SIC for Sensing	Bidirectional Communication	Notes
	Yes	Long	No	No	Classical HDCR does not have SIC circuit. Thus bidirectional communication and TS are not applicable [ , , ].
	Yes	Short	Yes	No	SIC is used to apply simultaneous Transmit-Sense strategy. No simultaneous bidirectional communication is applied in this mode [ , , , ].
	No	Short	No	Yes	SIC is used in this mode to establish bidirectional communication. The PU sensing is done based on the IS [ , , , ].
	Yes	Short	No [ , ]/Yes [ ]	Yes	Even though SIC is used in this mode to apply simultaneous bidirectional communication, SS remains applicable with the help of ensuring a certain level of cooperation between the communicating SUs [ , , ].

Research Papers	Local SS	Cooperative SS	Spectrum Prediction	Resource Allocation
[ , , , , , , ]
[ , , , , , , , , ]
[ , , , , , , ]
[ , , , , , , , , , , , ]
[ , , , , ]
[ , , , , ]

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Nasser, A.; Al Haj Hassan, H.; Abou Chaaya, J.; Mansour, A.; Yao, K.-C. Spectrum Sensing for Cognitive Radio: Recent Advances and Future Challenge. Sensors 2021 , 21 , 2408. https://doi.org/10.3390/s21072408

Nasser A, Al Haj Hassan H, Abou Chaaya J, Mansour A, Yao K-C. Spectrum Sensing for Cognitive Radio: Recent Advances and Future Challenge. Sensors . 2021; 21(7):2408. https://doi.org/10.3390/s21072408

Nasser, Abbass, Hussein Al Haj Hassan, Jad Abou Chaaya, Ali Mansour, and Koffi-Clément Yao. 2021. "Spectrum Sensing for Cognitive Radio: Recent Advances and Future Challenge" Sensors 21, no. 7: 2408. https://doi.org/10.3390/s21072408

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Research and Development in the Networks of Cognitive Radio: A Survey

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• First Online: 26 January 2021
• Cite this conference paper

• G. T. Bharathy 6 ,
• V. Rajendran 6 ,
• M. Meena 6 &
• T. Tamilselvi 7  

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 55))

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6 Citations

The cognitive radio (CR) prototype that has been intended to scheme the future wireless communication structures is emerging progressively by utilizing its various features within the existing wireless system models. A considerable quantity of research attempts has been carried out for resolving CR disputes, and numerous technologies associated with CR in addition to vibrant accessibility of the spectrum have also been incorporated. Furthermore, software-defined radio [SDR] systems have progressed to a larger extent, where it can be utilized for implementing the CR networks. This paper is intended to provide wide-ranging investigation for deploying the increasing exploration in the field of CR systems by including all features like spectrum sensing, evaluations, numerical designing of spectrum utilization and concepts of physical layer including the modulation scheme, multiple access techniques, resource distribution, cognitive learning and strength and safety measures in CR networks. The evolving developments of CR research and disputes associated to the cost-effective CR systems are also summarized in this research study.

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G. T. Bharathy, V. Rajendran & M. Meena

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Bharathy, G.T., Rajendran, V., Meena, M., Tamilselvi, T. (2021). Research and Development in the Networks of Cognitive Radio: A Survey. In: Karuppusamy, P., Perikos, I., Shi, F., Nguyen, T.N. (eds) Sustainable Communication Networks and Application. Lecture Notes on Data Engineering and Communications Technologies, vol 55. Springer, Singapore. https://doi.org/10.1007/978-981-15-8677-4_39

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RESEARCH ISSUES IN COGNITIVE RADIO NETWORKS

Cognitive radio networks are instantaneous networks in which real-time variations are made in the network operating parameters with respect to the changes in the environment. Our researchers incorporated advanced technologies to solve the research issues in cognitive radio networks. Cognitive radio networks for CRN use the method of construction by understanding and working on the basis of the following two objectives.

• No Interference:  As cognitive radio network users use the unlicensed spectrum, they have an objective of not interfering with the spectrum of licensed users
• High Reliability in Communication:  Communication has to be the most reliable one

In cognitive radio networks, there are three major types.

• Procedural cognitive radios
• Policy radios
• Ontological cognitive radios

Here is a complete overview of cognitive radio network research where our experts have provided you with the top practical solutions to the common research issues in it. let us first start with the top functions in cognitive radio.

TOP COGNITIVE RADIO FUNCTIONS

The main function of cognitive radio is the detection of usage of the communication channel . Despite this, there are many other important functions for which CRN gains advantages. The following are the top cognitive radio functions involved.

• Receiver of every user has to be enabled for sensing of the environment in real-time (continuously)
• Self-organized cooperation is used for the facilitation of communication among different users
• Intention experience and self-awareness is created
• CRN is able to learn from the environment and helps in performance adaptation by every transceiver (variations in statistical of the RF stimuli that are incoming)
• Controlling communication processes among various users competing users

Our research experts have been guiding projects related to cognitive radio network functions. We are thus highly confident to solve any kind of issues that arise out of cognitive radio research.  Are you searching for expert guidance for your CRN project? Then you are at the right place where you can find the best possible solutions to all your research issues in Cognitive Radio Networks and challenges along with the huge amount of reliable research data which you can use to carry out your research. Now let us see the key features of CRN

KEY FEATURES OF COGNITIVE RADIO NETWORKS

The key characteristic features of cognitive radio networks are the major reasons for their popularity among users and researchers . The following is a brief note on CRN characteristics.

• As the radios take advantage of joining and leaving the network at any time, radio networks have to fulfill the following 
• Authentication 
• Authorization
• Data protection (as information has flowed among the participants)
• Policy adherence
• Environment sensing
• Constraints of configuration
• Peer to peer negotiation 
• Optimal utilization of spectrum
• Meeting the demands of the user
• The examples include the following
• Digital signal processor
• Radiofrequency front end
• Control processor
• Self – describing modules are configuring automatically by themselves
• This is called plug–and–play 
• Used frequency range
• Time in which that frequency is used
• Receiver and transmitter location is specified
• Determination of modulation of signals
• Determination of radio settings is made using the results obtained by environment sensing
• In CRN information is exchanged to the local environment
• Demand of the users 
• Regular maintenance of performance among themselves
• Settings of operations are determined using local and peer information  
• Various requirements of operations can be met using the software
• SDR configuration for meeting the requirements 
• Operations inside the building
• Long-distance operation 
• High-speed movement operations
• Substantial capability
• Selection of radio software modules
• It is selected from the module library and then connected to operational radio
• Policies for radio operation 
• Radio operation limitations are stored in the radio (its availability is ensured in the network)
• Frequencies specific locations are defined by the policies
• Configuration databases – characteristics of operation of the radio 
• Physical operations are limited using these databases

Cognitive radio networks are used in various applications involving one or more of their features. The data on different CRN aspects specific to particular applications are available with us. Our developers are regularly updating this list from real-time examples and instances from around the world.

So you can get all that you need for your research in CRN at the same spot with us. We are one of the highly sought online research guidance facilities in the world. Our experts are ready to render support for your CRN research . Now let us look into the common research issues in cognitive radio networks .

WHAT ARE THE CHALLENGES OF COGNITIVE RADIO NETWORKS?

           You will have to overcome some very common challenges during your cognitive radio network research . These issues can be readily solved with the help of our experts. The following are the major challenges of cognitive radio networks

• Spectrum decision 
• Time varied characteristics of the signal
• Controlling power
• MAC protocols
• Spectrum sensing coupling

Approaches :

• Opportunistic access
• Time slotting
• Random access
• Controlling the channel
• Controlling the connection of channel
• Jamming Security Attacks 
• UWM and ISM bands
• Licensed spectrum (dedicated)
• Transport protocol
• Spectrum awareness
• TP – CRAHN 
• Application 
• Methods of applications that are novel
• Spectrum agnostic application issues
• Spectrum sensing
• Time-varying properties of the signal
• Condition of the channel
• Accuracy of sensing
• Non – Cooperative approaches
• Cooperative approaches
• Spectrum sharing 
• Mechanisms for coordination (novel methods)
• Management of energy
• Sharing of spectrum based on consensus
• Game theory
• Management of connection 
• Delay 
• Reactive and proactive handoff
• Activity of the primary user
• Novel metrics by considering the following 
• Channel characteristics
• Switching delay
• Route maintenance mechanism
• Approaches based on middleware
• Utilization of spectrum data
• Masquerading
• Authorization 
• Implementation
• Performance 

Approaches:

In the above section, we mentioned the different approaches used by researchers across the world in the field of CRN research.

So, the following aspects can be considered as the major research issues in cognitive radio networks,

• Allocation of resources
• SSDF and PUEA detection
• Access to dynamic spectrum
• Selection of target channel and controlling error
• Architecture (software and hardware)
• Network Security (trusted access)
• Strategies for sharing spectrum
• Energy harvesting
• Sharing spectrum
• Multi-access schemes
• Cooperative sharing of spectrum
• Cross-layer design
• Hidden node terminal problem
• Issues in sharing
• Usage of unlicensed spectrum

You can get the technical details of all the projects based on solutions for highly sensitive research issues in Cognitive Radio Networks . Our subject experts will collect all the research data on recently devised solutions using any aspect of CRN from basic to advanced methodologies. 

Connect with us at any time to get your queries solved by our experts. Let us now talk about the spectrum sensing process along with its types and working in CRN.

SPECTRUM SENSING IN CRN

Spectrum usage awareness along with the primary users’ existence data is quite important to be analyzed in CRN . The following parameters are also measured in the Cognitive radio network.

• Measurement
• Learning 
• Data on certain parameters (availability of spectrum and power, radio channel features, the infrastructure of the network, etc)

There are different methods involved in sensing the spectrum. It includes the usage of different aspects of cognitive radio networks . The following are the popular techniques for sensing spectrum.

• Detecting energy
• Cyclostationary based
• Waveform based
• Cooperative sensing
• Prediction based
• Interference based

Out of these methods, the cooperative sensing method has important merits associated with it. Cooperative Spectrum Sensing or CSS consumes extra energy as it is involved in fusion centers reporting and sensing. The energy utilized in this case is proportional to the time of sensing of CR nodes, FC selection, and the number of nodes involved in CR sensing . We give you expert solutions for some of the frequently asked questions in CRN research. As proof of this claim, let us look into the following question.

How is the spectrum shared by the nodes?

It depends primarily on the spectrum band that two CRN should be used for transmission. This in turn depends on the following.

• Channel width

For this method of spectrum sharing, we will need an efficient protocol for allocation of the blocks of time–spectrum . Determination of throughput of the network along with the utilization of the overall network is made using these protocols. There are many methods used in real-time to solve the research challenges in CRN for which we have done a thorough performance analysis that can be of great significance to your research. Contact us to get those project details. Now let us see the different types of spectrum sensing systems.

TYPES OF SPECTRUM SENSING

Primarily the spectrum sensing systems can be c lassified based on certain spectrum parameters . The following are the different types of systems involved in spectrum sensing

• Transmitter – centric interference management 
• Receiver – centric interference management 
• Centralized (coordinated)
• Decentralized (coordinated)
• Decentralized coordinated
• Cyclostationary detection
• Energy detection 
• Matched filter detection 

We have delivered project PhD Guidance and thesis writing support in all the above types of systems involved in spectrum sensing. Connect with us to know the technical glitches that we faced in terms of our project. Now let us see more about the challenges in spectrum sensing.

CHALLENGES IN SENSING SPECTRUM

There are some common challenges faced cd by researchers while designing spectrum sensing system aspects of CR networks . The following are such problems for which you can get our expert guidance regarding the best implementable solutions.

• Traffic modeling
• Multiple CUs
• Optimal sensing
• Proactive and reactive sensing of spectrum
• Frequency of sensing 
• Period selection 
• Standards in sensing
• PU problem (hidden)
• Security issues
• Decision fusion 
• Blind spectrum sensing
• Period of hopping
• Uncertainty in noise
• Constraints in software and hardware

As we said before, these challenges are easily overcome by our expert advice. We faced many kinds of research issues in cognitive radio networks. The challenges, that we faced, involved in spectrum sensing due to some of the technical constraints include the following.

• DSA method evaluation along the algorithms related to adaptation 
• Distributed spectrum coordination protocols are specified for multiple radio standards 
• Evaluating performance in the environments of dense radios
• Protocol interfaces and spectrum server database is specified 
• Efficient algorithms for adaptation of frequency, rate, and power

With the huge amount of experience that we gained through r esearch support for top CRN spectrum sensing project topics , we are highly equipped to guide you through various practical solutions for the above-mentioned problems. Get in touch with us to know more about the specificities of the solution models. Now let us see about the requirements of CRN spectrum sensing.

REQUIREMENTS FOR SPECTRUM SENSING

The following are the important requirements for the spectrum sensing process in the cognitive radio network.

• Intelligent algorithm
• Accuracy in sensing results
• Simple methods for sensing
• Security in communication 
• Reliability 

Based on the area of application, the requirements for spectrum sensing vary widely. Accordingly, there are many problems associated with them. The different processes of spectrum sensing involve issues that are specific to them. Now let us see about the problems in spectrum allocation.

PROBLEMS IN SPECTRUM ALLOCATION 

In order to understand the issues in the allocation of spectrum, you must first recall the aims to be kept in mind before allocating spectrum.  The following are the objectives for the allocation of spectrum

• Optimization of fairness
• Optimization of connectivity
• Maximizing reliability
• Efficiency of spectrum
• Minimizing the interference
• Maximizing the throughput
• Minimizing delay

The following are the techniques involved in solving spectrum allocation issues

• Fuzzy logic
• Nonlinear programming
• Evolutionary algorithms 
• Graph theory
• Linear programming
• Markov random field
• Actor – critic learning automata-based CA algorithms
• SA algorithms based On – policy reinforcement 
• SA algorithms based improved Q – learning 
• Policy gradient-based SA algorithms
• Deep Q – networks based SA algorithms
• Q – learning-based SA algorithms

So the major challenges in spectrum sensing and allocation lie in the use of multiple advanced algorithms and techniques involved in their processes. The main point of concern is understanding the performance of the system for which different metrics are used for analysis. Let us see the metrics for the evaluation of CRN performance below.

IMPORTANT PERFORMANCE METRICS FOR CRN

The factors related to the design of CRN are the major metrics used for evaluating the system performance . The following are the metrics used for evaluating the performance of Cognitive Radio Networks. 

• Type of signal 
• Size of the data
• Time for transmission
• Rate of data 
• Range of frequency
• MAC protocol
• Standard certificates
• Assignment of frequency
• Constraints of power

You can seek our experts’ guidance readily regarding the above performance evaluation metrics. There are also other metrics specific to different CRN applications which you can know in detail from our technical team. Get in touch with us at any time to know more details about research issues in cognitive radio networks and get the details of our projects.

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Cognitive Radio Network (CRN) is a fast-growing technology to allocate the radio spectrum based on the dynamic requests of the users. It is flexible to adjust the data transmission flow in the spectrum. For that, it first senses the environment to know the current spectrum state and analyze the historical information of the communication channels regarding their occupancy. Then, it takes an effective decision to assign the spectrum for users. This article intended to give valuable information on Current Cognitive Radio Networks Thesis Topics with their latest Research Areas!!!

As a matter of fact, cognitive radio has the unique capability of an intelligent dynamic configuration. Generally, there are two types of users as primary and secondary. The PU is referred to as licensed spectrum users. The SU is referred to as non-licensed spectrum users. FCC has the right to assign the spectrum to the PUs. But, it also makes SUs use the unused spectrum of PUs through cognitive radio technology.

 This technology senses the spectrum and identifies the unoccupied spectrum . Then decide to hand over the spectrum to demanding users. Based on the spectrum utilization, it directs the transmission parameters to use the available channels and enhance the concurrent communication.

Important Components in CRN 

Compare to other wireless networks , cognitive networks has some special feature that as it categorizes the spectrum users into two classifications as primary users and secondary users . Below, let’s see them in detail:

• Primary users:  The licensed wireless sensors/devices which have rights to access the specific spectrum band with an assurance of the quality of service (QoS) are called primary users. Most importantly, they only have the access the higher priority to access the spectrum rather than secondary users.
• Secondary users:  The secondary users are unlicensed users who can then access the licensed primary user’s spectrum without affecting their QoS. The cognitive radio mainly makes these kinds of users avail themselves of the spectrum at their need. So, they are also called cognitive users who get the benefit of cognitive service. 

Further, we have also given you the other important terminologies that are highly used in the cognitive radio network. Also, we have specified the general purpose of those entities for your ease. Let’s have a quick look over them,

• Primary User (PU) –  Particular Spectrum Licensed Incumbent devices
• Secondary Service Provider (SSP) –  Service Provider to save the spectrum and optimize the spectrum allocation
• Cognitive Radio Mesh –  Support multi-hop communication using mesh backhaul for guiding BS for provisioning requested services to users. Also, it includes both basic and harvested bands.
• Basic Band –  SSP licensed frequency bands
• Cognitive Radio Router –  Relay node with multi-interfaces to facilitate existing spectrum bands for effective data transmission
• Base Station (BS) –  Environmental node to support SSP for spectrum management and other coverage services

Research Challenges in CRN 

Due to the unique factor of CRN , it has several security and privacy threats, vulnerabilities . Further, it also includes other kinds of research issues and we will discuss those challenges in the coming section. Here, we have given how we assess the CRN challenges to get the expected outcome are as follows, 

• Effectual resource consumption
• Deployment of hardware 
• Statistical quality of service provisioning
• Launch of shared control channel 
• Auction of spectrum

Next, we can see the limitations that we need to focus on in the time of designing a cognitive radio network . If we take reliable precaution measures in a development phase, then it will yield the desired outcome. 

Design Constraints in Cognitive Radio Network

• Lessening Transmit Power 
• SU Transmit Power 
• Energy Causality 
• SU Outage Probability 
• Energy Collision 
• Channel Condition Estimation
• Sensing Throughput-Reliability Tradeoff
• Channel QoS of SU needs to be Good (primary user)

Certainly, we assure you that our Cognitive Radio Networks Thesis ideas will help you to untie all the research challenges in CRN. Since we will provide you the fullest support from our side under the guidance of experts to gain a top-quality result. Moreover, we also encourage our clients to come up with their ideas in their interested area. Here, we have listed the current research areas in CRN for your reference.

Research Areas in Cognitive Radio Networks Thesis 

• Collaboration Software Defined Network in CRN 
• Network Topology Variation and Node Mobility
• Privacy-Preserving Cognitive Radio Network 
• System Construction and Software Abstraction in CRN
• Smart Spectrum Sensing and Handovers
• Optimization of Spectrum Sensing Techniques
• Relay Detection and Spectrum Allocation 
• Innovations in Spectrum Policy Models
• Energy-Efficient Routing Protocols Designs
• Frequency band and Radio propagation Interdependency
• Optimization in Multiple Relay Selection 
• Cognitive Radio Protocol Verification and Validation
• Multimedia Data Transfer in Healthcare Applications
• Efficient Spectrum Mobility and Handover in CRN 
• Cooperative CRN for Massive MIMO communication
• Prevention of Real-time Proactive Interference 
• Efficient OFDMA-CRN based Resource Management 
• Improved techniques for Network Congestion and Bandwidth Shortage
• Integration of Vehicular Ad hoc Network with CRN 
• Cognitive Radio Design and Routing Protocol
• Adaptive Intelligent Techniques for Resource Provisioning
• Enhanced Spectrum Decision and Selection Approaches in CRN

How to Write the Master Thesis?

Now, we give our Cognitive Radio Networks Thesis writing support to your research. At first, we precisely write the thesis statement in two sentences that describe your handpicked research topic. Then, we reveal the current position of your research concerning the research idea. Next, we make sure that this statement directs the reader/follower to know what this writing going to argue in the rest of the thesis . Also, it explicitly should say what the research is about. Below, we can see what are things that we need to include in the perfect thesis are:

• State the research aim and objective that you repeat throughout the thesis 
• Prove that the proposed objective is associated with the research
• Point out the idea that what you are attempting to focus on in research 
• Give the proper and sufficient background context that makes you to start this research
• Elaborate the prominence and worth of the research
• Show your unique research contributions to the society
• Discuss the methodologies and algorithms used in the study 
• Present the overview of the findings and experiment results 
• Suggest the future study recommendations

In addition, our native writers have shown you that how the following points will elevate your research result in your thesis . Since the presentation of research findings is the most significant phase in thesis writing which needs extra concentration. Let’s see a few of the critical points in the result discussion.

How do we write the results in thesis writing? 

• Provide context for research findings : This section is regarding the introduction part of the thesis. Here, we focus on the background data for your findings. This will help the readers to understand the study through background context. So, we make sure that you made this point clearly in the introduction
• Debate on your outcome:  First of all, we give elucidation on your experimental results. Make certain that obtained result is appropriately related to the research aim. Then, we compare the result with the previous work to evidently prove that your proposed work is efficient than others
• Replication of same data:  We ensure that we have highlighted the same research point in all the sections to insist how strong we are in to take a stand for that point
• Appropriate tables and figures:  We assure you that we correctly included the tables and figures with clear specifications.
• Attach raw information:  If the reviewers suggest including the raw data like files and transcripts in your document, then we add that data and give proper appendices
• Disregard the undesirable outcome:  When the obtained result fails to fully take a stand for the hypothesis. Then there is no need to eliminate that result. Instead, we report in what way that outcome supports our research. Certainly, we make that point to increase the engagement of the readers
• Assurance of truth for research findings : Provide suitable proofs for your findings that are highly acceptable by all readers

On the whole, reach us to avail Best PhD research guidance in the field of the cognitive radio network . We will sure to give you the complete backing to your research career in all aspects. Reach us for best cognitive radio networks thesis writing guidance.  Furthermore, to put our service in simple terms we will say “Give less and take more”.

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Research Challenges in Cognitive Radio Networks

A Cognitive Radio (CR) is a network that often senses and interacts with the surroundings to know the volatile spectrum handiness. It dynamically changes the network topology through dynamic spectrum handovers based on the user service requirements. In this article, you can get information on recent research challenges in cognitive radio networks!!!

In general, the two major components of CRN are the spectrum engine and policy engine. Here, the spectrum engine only collects the details about the available spectrum and decision to assign channels for services. Then, the policy engine follows up and processes the rules and regulations of CRN. As a unique property, the CRN utilizes frequency hopping to offer security. Also, it uses a proactive technique rather than using the reactive technique to hop among various channels. Now, we can see the important and fundamental operations of the CRN.

Wh at are the functions of cognitive radio?

• Easy to identify the geo-positioning by using a transceiver
• Support the modification of modulation and transmit power features
• Ability to sense the environment to detect the nearby sensors/devices while processing
• Simple to perform user authentication and authorization processes
• Ensure the security of the signals by cryptographic functions (encode and decode)

Next, we can see the working flow of the cognitive radio mechanism in four different stages. Here, it senses the environment and detects the unused spectrum. Then it assigns the spectrum to the secondary users who need the spectrum.

Life Cycle for Cognitive Radio Network

• It sense the real-world environment for monitoring wideband radio frequency
• It performs the feature detection and characterization for generating appropriate responses for service request
• Select the optimal response based on performance metrics
• Enable secondary users to access the spectrum for communications

For more clarity, here we have given the process involved in the OSI layers . These functions are very important in developing CRN-related applications and systems. Below, we specified the physical layer, transport layer, link layer, network layer, and application layer.  

OSI Stack for CRN 

• Collect the sensing details
• Sense the available spectrum
• Manage the sensed data and reconfiguration
• Perform the reconfiguration process
• Manage the loss and handoff latency
• Holds the scheduling data and reconfiguration
• Link-layer latency
• Reconfigure the network in the case of dynamic changes
• Manage the route data for packet forwarding
• Meet the needs of quality of services (QoS)
• Control and manage the whole application   

Research Challenges in CRN 

Now, we can see the key Research Challenges in Cognitive Radio Networks . These issues are very important when we deal with real-time applications. So, the current scholars seek effective solutions to break down these problems into pieces. Our researchers are ready to guide you in the following special cases. Further, if you are interested, we also let you know the other side of the current research areas.

• No efficient algorithms, tools and techniques
• Require to solve the graph coloring issue in single-channel and multi-channel systems
• Not adaptive with varying channel-width contiguous channels
• Availability of unparalleled different spectrum
• Spectrum Fragmentation
• Designed Protocol need to be load-ware, efficiency-ware, opportunistic, distributed, and more  

Detailed Research challenges of Cognitive Radio Network can be as follows. 

• It is the foremost process of CRN to identify the available primary and secondary users to assign the unused spectrum to SUs without interfering with PUs
• For this sensing process, it focuses on several factors such as temporal metrics, power, modulation, frequency, etc. to identify the gaps in the spectrum band
• In the previous study, the sensing methods detect the energy and features in every narrow frequency band. But, it is not a promising strategy in terms of energy since it faces several interference and noise issues.
• Further, this technique also fails to identify and distinguish modulated signals, time-varying signals, frequency hopping signals, etc.
• To overcome the energy detector problem, cyclostationary models are proposed for feature detection. But, it has complexity in computation and requires more time
• Overall, the most challenging task in spectrum sensing is identifying the vulnerable primary signal in fast and low-cost environs.
• In cognitive radio, we improve the spectrum consumption by letting unlicensed secondary users access the spectrum without affecting licensed primary users.
• The major issue in this process is dynamic power and spectrum allocation. So, we need to use adaptive access control techniques to automate this process efficiently
• In the previous study, heuristic methods are proposed which are not effective and centralized too.
• There is an inconsistency in standard FCC spectrum distributions and real usage which indicates that a new approach to spectrum licensing is needed.
• So, it requires the adaptive technique to increase the spectrum access and provide efficient incentives for the unlicensed spectrum utilization
• It is the assignment of an unparalleled spectrum for the unlicensed service requests received from the users.
• Here, the interference aware opportunistic network faces many research challenges in cognitive radio networks in sharing of resources among multiple users.
• Spectrum sharing issues in primary and secondary users are unique than the traditional networks
• In specific, the secondary user’s spectrum access has an infinite number of research ideas
• For preventing the hidden node issue from the secondary user, we need to take effective defense measures to make sure that spectrum allocation does not affect the licensed primary users.
• Due to the distributed nature of the cognitive radios, it is essential to protect system security.
• Particularly to maintain the high-end security against the event attacks, application-specific privacy actions need to be taken in CRN
• CRN requires an advanced cross-layer approach to enhance its scalability nature.
• This approach needs to meet the requirements of network structure, physical link quality, radio node density/interference to manage the data sharing in a cross-layer system.
• In addition, it also has the threat of spectrum handoff in a dynamic cognitive radio environment. This can be prevented using an effective cross-layer design which ensures the QoS.  

What is 5G cognitive radio?

In recent days, 5G is considered to be the dominating technology in communication due to its incredible benefits . Compare to 4G, 5G is vastly better in terms of low cost, high speed, low power usage, and more. On the other side, CRN is effective in independently allocating the spectrum in the time of huge network traffic and service requests for optimal interaction. These two sophisticated technologies are currently tied up together to rule mobile communication.

Conversely, CRN undergoes security vulnerabilities because of the rapid growth of wireless technologies. So, it is difficult to attain high-level system performance . In the last couple of years, many security threats prevention measures taken been proposed to enhance the security level of CRN. The objective the CRN security is to safeguard the primary and secondary users in both licensed and unlicensed aspects. By knowing the importance of this area, our resource team has spent more time designing innovative research ideas that guarantee CRN security. These notions are sure to concentrate on CRN physical layer utilizing their CR cycle and varieties along with network protocol layers.  

What are the security aspects of cognitive radio?

It has several threats of domain-specific attacks due to its unique model and features. Furthermore, it has the threat of traditional attacks such as man-in-middle, eavesdropping, forgery, impersonation, tampering, etc. So, compare to other wireless communication networks, CRN has more risks in the network security and privacy of a system. Here, we have given the most common attacks that occurred in different layers with their characteristics.

• Software-defined radio (SDR) attacks
• Cross-Layer attacks
• Interference on SDR hardware and software packages
• Indented to attack the multiple layers in CRN
• Beacon falsification (BF) attack
• Common control channel (CCC) attacks
• Synchronization Interference in IEEE 802.22 WRANs
• Aiming for CCC via jamming, MAC spoofing, and congestion attacks
• Spectrum sense-data falsification (SSDF) attack
• Primary use emulation attack (PUEA)
• Erroneous monitoring related to spectrum sensing
• Transmitters RF signal Emulation

In addition, we have also highlighted a list of security issues in the below section. Through this, we can design any number of research ideas. Further, if you need more information, you can approach us.

Security issues

• Learning-based Spectrum Sensing Techniques
• Improved Trust based Sensing Mechanisms
• Validation of Transmitter
• Realization of User/Device Identity
• Identification of Primary user
• Observation of Intervention Level
• Advance Energy Detection Techniques
• Adaptive Spectrum Sensing
• Signal Strength-based Intrusion Detection
• Location Identification
• Secure Data Transmission
• Automated Routing Techniques
• Trust-based User Authentication  

Security Attacks in CRN 

Categories 

• Attack Effect on Victim  – Induced and Direct attacks  
• Adversarial attacker   Objective  – Malicious and Selfish attacks
• Sybil Operation
• Synchronization and Control for Messages
• Belief Operation
• Protocol Vulnerability in Exploitation
• Sensory Operation

In the above, we have specified the general classification of security in cognitive radio networks. In this, we were also given the primary attacks that have a high possibility of occurrence while spectrum processing. Let’s have a look over them,

• Collaborative
• Byzantine / SSDF Attacks
• Objective Function (OF) Attacks
• ESCAPE and BOOST protocols security threats
• Spectrum Aggression and Fragmentation attacks
• Frame offset falsification
• Beacon falsification
• Software-based – Sybil-SSDF and Sybil-PUEA
• Hardware-based –Sybil-SSDF and Sybil-PUEA
• Induced Sensory
• Cooperation disruption (induced SSDF)
• Link disruption (induced PUEA)
• Direct Sensory
• Cooperation disruption (denial SSDF)
• Link disruption (denial PUEA)

Our research team is well-established in handling current security issues. So, we guarantee you that we support you in all aspects. Though CRN is successful in spectrum sensing, allocation, and accessing, it is at the risk of security threats. Many attackers are attempting to hack the channel while processing and handovers. So, it is highly essential to improve security in possible ways. Here, we have given you a few research ideas on a CRN security basis.  

• Advance Cognitive Radios Anti-jamming Mechanisms
• Optimized cross-layer design and security
• Enhancement of CRN Security through Incentive Methods
• Joint Connection and Power Control in System learning
• Trust-based Learning Approach Adoption in Spectrum Sensing
• Periodic based Distributed Spectrum Accessing Scheduling in CRN

To know more about current research issues in cognitive radio networks , reach our experts. We believe that you will make use of this opportunity of holding up our hands to have your successful project.

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Home » PhD Research Topics in Cognitive Radio

PhD Research Topics in Cognitive Radio

          The research scholars can feel free to select a research topic in based on the research field cognitive radio network. In addition to that, the researchers can select any of the below mentioned research topics based on cognitive radio network to develop their own research project.

• Target channel scheduling and sensing
• Dynamic spectrum access
• Error control and fault prediction
• Spectrum and power allocation
• PUEA detection
• Secondary users routing

Cognitive Radio Projects

        Moreover, the research professionals in cognitive radio network have highlighted some research projects in cognitive radio.

• Preventing cognitive radio networks against SSDH attacks in sequential compressed spectrum sensing algorithm
• Cooperative cognitive radio non orthogonal multiple access networks using robust secure beamforming technique
• An adaptive transmit power in cognitive radio networks with reflecting surfaces
• Utilization of cognitive radio in RPL smart grid network for multi criteria parent selection
• CRN based malicious users tackle using double adaptive approach
• Combating SSDF attack in CRN using SETM algorithm
• Routing protocol for joint channel selection in cognitive radio network
• Cognitive radio vehicular ad hoc networks using spectrum handoff aware AODV routing protocol
• Parallel sensing in CRN for self-organized efficient spectrum management
• Rural and suburban area in cognitive radio network using dynamic interference optimization
• Secured cognitive radio networks with SWIPT using AN aided transmit beamforming design
• Cognitive radio network based spectrum sensing using reliable machine learning
• Spectrum sensing in cognitive radio network using ensemble classifier
• Cognitive radio ad hoc networks using load balancing opportunistic routing
• UAV based cognitive radio network through spectrum efficiency optimization
• Emerging 5G systems via full duplex and cognitive radio networking
• Full duplex cognitive radio network parameter optimization based on throughput
• Cognitive radio network based process of spectrum mobility and handoff
• Spectrum sensing method for energy detection in cognitive radio network
• An optimal relay selection in cognitive radio network based on QoS

         We people are always ready to provide the complete support the research scholars, so make use of it just by reaching us.

cognitive radio NETWORKS research topics

Phd research topic in cognitive radio networks.

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• Cognitive Radio Networks