Cognitive Radio Network Seminar Topics for Students

A cognitive radio network is a type of network where the behavior of every radio is simply controlled by a cognitive control mechanism to adapt to changes in operating conditions, topology, or user requirements. These networks are vulnerable to usual wireless network-specific attacks like radio frequency jamming, medium access control address snooping, spurious MAC frame transmission, eavesdropping, unique security attacks & cheating on contention. The cognitive radio networks working mainly depends on four different kinds of operations like spectrum decision, spectrum detection, mobility spectrum, and spectrum sharing. These are the different operations where the cognitive radio spectrum is acquired and used. This article provides a list of cognitive radio network seminar topics for engineering students.

Cognitive Radio Network Seminar Topics for Engineering Students

The list of cognitive radio networks seminar topics for engineering students which are very helpful in selecting from these topics.

Cognitive Radio Networks Seminar Topics
Cognitive Radio Networks Seminar Topics

Spectrum Sensing Methods with Cognitive Radio

Cognitive Radio is a very famous dynamic spectrum utilization method because of the underutilization of the radio spectrum assigned to main users & ever-rising spectrum demand. In cognitive radio, spectrum sensing is a fundamental part that allows the user to detect the grey & white spaces within the RF environment.

Spectrum Inference within CRN

Spectrum inference is also called spectrum prediction and it is a promising method of inferring the free or occupied condition of radio spectrum from previously recognized or measured spectrum occupancy statistics by exploiting the inherent correlations efficiently among them. Spectrum inference has been gaining attention in a wide range of applications within CRN that ranges from predictive spectrum mobility & adaptive spectrum sensing to smart topology control & dynamic spectrum access.

Cognitive Radio Role in 5G

The cognitive radio with 5G wireless communication is used in data-intensive based applications. The 5G networks provide higher speed data transfer, ubiquitous connectivity, less end-to-end latency, energy efficiency improvement, very high system capacity, etc. A cognitive radio network simply provides sharing of the dynamic spectrum to get higher spectrum efficiencies as necessary within 5G architecture. Cognitive Radio is capable of adapting & learning its functional & operating parameters based on the environment where it operates. To make the 5G network concept realistic & also to overcome the 5G challenges, Cognitive Radio adaptability & flexibility is used.

Cognitive Radio in Health Care

Wireless communications are used mainly for supporting various electronic health-based applications to transmit patient & medical data. A cognitive radio system is mainly used for e-health-based applications within a hospital environment to defend medical devices from unsafe interference by adjusting wireless devices’ transmit power based on EMI constraints. So the cognitive radio system performance for e-health-based applications is estimated throughout simulations.

Sensing of Compressive Spectrum for CRN

Compressive spectrum sensing is a promising technique that improves the compressible & sparse signals from harshly under-sampled measurements. This technique is simply applied to wireless communication to enhance its capabilities. The compressive sensing technique describes a signal with a small no. of measurements & after that recovers the signal from these measurements.

In the compressive spectrum process, the original signal recovering from the compressed data plays a very important role. The number of samples necessary was huge, and sensing operation making is difficult & costlier. To defeat these issues compressive sensing technique is applied within 5G CRN.

Cognitive Wireless Networks

The cognitive wireless network is the next-generation wireless network used to demonstrate the intelligent behavior of a network where network nodes are included through cognitive engines. The cognitive wireless network concept mainly aims at developing the utilization of radio resources by taking benefit of idle licensed spectrum through the right interference mitigation methods.

Cognitive Computing & Its Applications

The combination of cognitive science & computer sciences is known as cognitive computing. Here, cognitive science is the study of the human brain & its functions whereas computer science’s main goal is to reproduce human thought processes within a computerized model. Cognitive computing builds algorithms with cognitive science theories. So, these results impact healthcare, personal lives, energy & utilities, the retail industry, banking & finance, enterprise management, transportation and logistics, education, security, etc.

Cognitive computing uses data mining, machine learning algorithms, visual recognition & neural networks to perform different human-like tasks cleverly. Cognitive computing mainly focuses on imitating human behavior & reasoning to resolve difficult problems. Cognitive Computing techniques frequently depend on deep learning techniques & neural networks.

Cognitive Robotic Process Automation

Cognitive robotic process automation or cognitive RPA is a term used for Robotic Process Automation tools & solutions that control Artificial Intelligence technologies like Text Analytics, Machine Learning & Optical Character Recognition to enhance the workforce & customer experience. This highly advanced form of RPA gets its name from how it mimics human actions while humans are executing various tasks within a process. Such processes include learning (acquiring information and contextual rules for using the information), reasoning (using context and rules to reach conclusions), and self-correction (learning from successes and failures).

Not like usual unattended robotic process automation, cognitive RPA is an expert in handling exceptions without human interference. For instance, nearly all RPA solutions cannot provide for issues like a date presented in the incorrect format, information missing within a form or very slow response times on the Internet or network.

Cognitive Radar

Cognitive radar is a system that depends on the perception-action cognition cycle that senses the surroundings and learns from related information regarding the objective & the background after that adapts the radar sensor satisfies the requirements optimally for their mission based on a preferred goal. The cognitive radar concept was originally introduced for only active radar.

Cognitive Cybersecurity

Cognitive Cybersecurity is used to describe the procedure of defending computer systems from illegal access, utilization, disclosure, interruption, destruction, or modification. There are several names for Cognitive Cybersecurity like human factors security or behavioral security. It protects the computer systems from both internal & external threats.

Internal threats are; malicious insiders or negligent employees whereas external threats are; malicious actors like thieves or hackers. Cognitive cybersecurity is the study of human behavior like how different people interact with devices & software, how they react to security alerts or warnings, & how they manage security credentials & passwords. Based on the behavior of humans, organizations can design safer systems.

Security Challenges in CRN

A cognitive radio network is an evolving concept that aims more efficiently to exploit the accessible spectrum for the usage of opportunistic networks. Cognitive Radio Networks (CRNs) deploying increases numerous security concerns & open issues. Cognitive radio networks experience both typical wireless networks’ liabilities & threats related to their inbuilt functionalities.

Cognitive Radio Networks for IoT

A Cognitive Radio Network is a smart & emerging technology to deal with spectrum scarcity problems. This network aims to utilize the unoccupied spectrum band once it is not utilized by the qualified user. A wide investigation has been carried out since the beginning of this technology wherever different challenges have been broadly explored like spectrum sensing, CR networks applicability & cooperation among cognitive radio users. The new CR technology applications for the Internet of Things & proposal of suitable solutions to the actual challenges within this technology will make the internet of things more reasonable & applicable.

Cognitive Radio Impact on Radio Astronomy

The introduction of new communication techniques requires an increase in the efficiency of spectrum usage. Cognitive radio is one of the new techniques that foster spectrum efficiency by using an unoccupied frequency spectrum for communications. However, cognitive radio will increase the transmission power density and cause an increased level of Radio Frequency Interference (RFI), which may impact other services and particularly passive users of the spectrum. In this paper, we present the principles of cognitive radio and introduce a model for its impact on radio astronomy.

STRS (Space Telecommunications Radio System) Cognitive Radio

An SDR or software-defined radio provides the most capability to integrate autonomic decision-making capability & also allows the incremental evolution to a cognitive radio. So, this cognitive radio technology impacts NASA space communications in different areas like interoperability, spectrum utilization, radio resource management & network operations above a large range of operating conditions.

The cognitive radio of NASA builds upon the infrastructure being developed by STRS (Space Telecommunication Radio System) SDR technology. The architecture of STRS describes techniques that can notify the cognitive engine regarding the radio surroundings so that the cognitive engine can separately learn from experience & take suitable actions to adapt the radio operating characteristics & enhance performance.

Energy-Aware Cognitive Radio Systems

The concept of energy-aware communication has encouraged the research community interest in the most current years because of different economical & environmental reasons. For wireless communication systems, it becomes essential to move their resource allocation troubles from optimizing fixed metrics like latency & throughput. Even though these systems introduce spectrum efficient utilization methods and employ new complex technologies, especially for spectrum sensing & sharing that use additional energy to compensate overhead & feedback costs.

A literature study of current resource allocation methods based on energy-efficient is presented for cognitive radio systems. So these methods’ energy efficiency performances are analyzed & evaluated in power budget, adjacent-channel & co-channel interferences, quality-of-service, channel estimation errors, etc.

Listen & talk full-duplex CRN

The use of full-duplex radio within cognitive radio networks presents a novel spectrum-sharing protocol to allow the secondary users to sense & access the vacant spectrum simultaneously. Protocol like LAT (listen & talk) is assessed through both mathematical analysis & computer simulations as compared to other access protocols like the listen-before-talk protocol. In addition to signal processing based on LAT & resource allocation, it discusses methods like spectrum sensing & dynamic spectrum access. It proposes LAT protocol as an appropriate access system for CRNs for supporting the quality-of-service requirements of high-priority applications.

Radio Systems Adaptation with Hybrid Cognitive Engine

Network efficiency & its resource’s proper utilization are crucial requirements for operating wireless n/ws optimally. Cognitive radio targets perform these requirements by developing artificial intelligence (AI) methods to make an entity known as a cognitive engine.

The cognitive engine develops awareness regarding the nearby radio environment to optimize the utilization of radio resources & adapt related transmission parameters. Here, a hybrid cognitive engine is proposed that employs CBR (Case-Based Reasoning) & DTs (Decision Trees) to execute radio adaptation within multi-carrier wireless n/s. The complexity of the engine is decreased by employing decision trees to enhance the indexing method utilized in CBR case retrieval.

Application of Cognitive Radio for Vehicular Ad Hoc Networks

The cognitive radio technology application within vehicular ad-hoc networks mainly targets enhancing the communications between vehicles themselves, between vehicles & roadside infrastructure. Because of the dynamic spectrum access approach, cognitive radio technology allows more efficient usage of the RF spectrum. In vehicular networks, the research on cognitive radio applications is developing still & there are not several experimental platforms because of their complex arrangements.

Monitoring VHF Spectrum with Meraka Cognitive Radio (CR) Platform

A natural resource like the Radio Frequency spectrum is used extensively by the operators of the wireless network for providing radio transmission systems or communications. RF spectrums shortage has led to the improvement of new methods for better usage of the RF spectrums. So, the MCRP (Meraka Cognitive Radio Platform) was developed with the second version of the USRP2 (Universal Serial Radio Peripheral) hardware as well as the GNU Radio software.

Sharing of Distributed Opportunistic Spectrum in CRN

Whenever the licensed radio spectrum is underutilized then cognitive radio technology allows cognitive devices simply for detecting & after that access this scarce resource dynamically. Here, a simple, instinctive, efficient, and yet powerful method allows opportunistic channels within cognitive radio systems in a distributed manner.

This proposed technique attains extremely high spectrum utilization & throughput value. And, it also reduces interference between cognitive base stations & the main licensed users to utilize the spectrum. The algorithm quickly & efficiently reacts to differences within the parameters of the network & also attains a high amount of fairness among cognitive base stations.

Defense Mechanism Design to Mitigate the Spectrum Sensing Data Falsification Attack within Cognitive Radio Ad Hoc Networks

Cognitive radio networks address the spectrum scarcity problem by allowing simply unlicensed users called secondary users to use the licensed user’s unused spectrum band called primary users without causing intrusion to the primary users. However, this results in some safety challenges where malicious secondary users report wrong spectrum observations which are known as the SSDF (spectrum sensing data falsification) attack. Here, we study the SSDF attack within a cognitive radio ad hoc network. So the reputation & the q-out-of-m rule schemes are integrated to lessen the SSDF attack effects.

Adaptive Decision-Making System for CRNs

In the present wireless networks, Radio resource management has become an important feature due to spectrum scarcity as well as application heterogeneity. For resource management, Cognitive radio (CR) is a very potential candidate due to its ability to satisfy the growing wireless demand & develop network efficiency. The radio resources management process’s main function is Decision-making because it decides the radio parameters that manage the utilization of these resources.

An ADMS or adaptive decision-making scheme is proposed for radio resources management of various types of network applications like emergency, power consuming, spectrum sharing & multimedia. This scheme uses a genetic algorithm like an optimization tool especially for making decisions. It includes different objective functions for the process of decision-making like reducing power consumption, packet error rate, interference & delay. On the other hand, spectral efficiency and throughput are maximized.

Some More Cognitive Radio Network Seminar Topics

The list of some more cognitive radio network seminar topics is listed below.

  • Network Defined by Collaboration Software in Cognitive Radio Network.
  • Variation & Node Mobility of Network Topology.
  • Privacy-Preserving CRN.
  • Construction of System & Abstraction of Software within CRN.
  • Sensing Smart Spectrum & Handovers.
  • Spectrum Sensing Techniques Optimization.
  • Detection of Relay & Allocation of Spectrum.
  • Innovations within Spectrum Policy Models.
  • Designs of Energy-Efficient Routing Protocols.
  • Frequency band & Radio Propagation Interdependency.
  • Optimization within Multiple Relay Selection.
  • Verification & Validation of Cognitive Radio Protocol.
  • Multimedia Data Transfer within Healthcare Applications.
  • Efficient Spectrum Mobility & Handover within CRN.
  • Real-time Proactive Interference Prevention.
  • Integration of Ad hoc Network of Vehicular by CRN.
  • Resource Management based on Efficient OFDMA-CRN.
  • Improved Methods for Bandwidth Shortage & Network Congestion.
  • Design of Cognitive Radio & Routing Protocol.
  • Enhanced Spectrum Decision & Selection Approaches within CRN.
  • Adaptive Intelligent Methods for Resource Provisioning.
  • Cooperative CRN intended for Massive MIMO Communication.
  • Machine Learning for Cognitive Radio Network.
  • Cognitive Computing intended for Smart Grids.
  • Cognitive Robotics intended for Assistive Technology.
  • Cognitive Radio & Spectrum Sensing.
  • Cognitive Radio & mmWave Technology with 5G.
  • Design of Massive MIMO Antenna for CRN-5G.
  • FANET enabled by Cognitive.
  • Cognitive-based ad-hoc networks.
  • HetHetNets based on Cognitive.
  • Sensing of Full-duplex Spectrum in LTE & WLAN Bands.
  • Cognitive Radio Network for V2V, V2X & D2D Communication.
  • CRN-based Smart Sensing Networks.
  • Handoff & Routing Protocols for Cognitive Radio Network.

Thus, this is all about the list of cognitive radio network seminar topics. These cognitive radio network seminar topics are very helpful for engineering students in selecting a topic. Here is a question for you, what are the main functions of cognitive radio?