Cognitive Radio Network Research Topics

Cognitive Radio Network Research topics are a dynamic and fastly emerging technique which occupies an extensive range of topics. In this work we utilize the CRN network and this document overviews the definition, its uses, applications, where it was utilized and the parameters etc. Here we see the CRNs works and its applications:

1. Define Cognitive Radio Network?

At first we see the definition of Cognitive Radio Network; It is a wireless communication system which uses an accessible radio frequency spectrum through constantly adjusting transmission parameters on the basis of real-time environmental situations, optimizing spectrum usage, and improving the whole network effectively.

2. What is Cognitive Radio Network?

Next we discuss the in-depth explanation of CRN, it is a wireless communication system and the tools are knowledgeably and adjust their communication parameters to optimize spectrum utilization, permit for effective and well organized use of possible radio frequencies.

3. Where Cognitive Radio Network used?

After the definition of CRN we talk about the applications of CRN such as wireless communication systems, to improve spectrum usage. Some popular applications like military interactions, emergency services, smart grids, WSN and dynamic spectrum access in common wireless environments. In some situations the changeable and intellectual nature of CRNs make them worthwhile where the effective utilization of the radio frequency spectrum is important.

4. Why Cognitive Radio Network technology proposed? , previous technology issues

Cognitive Radio Network technology is a wireless communication system and we utilize this technology to our work by overcoming the existing issues. We overcame the lack of ability and difficulty by proposing the CRN method and the problems in the existing methods are spectrum usage, lack in allocation etc. Our proposed CRN method presents knowledge to constantly sense, change, and improves utilization of spectrum, and allows more effective and flexible usage of radio frequency spectrum.

5. Algorithms/protocols

The CRN network is proposed in this work and it overcomes some difficulties in the previous techniques, here we provide some methods that utilized for CRN techniques are game theory, algorithmic state machine, Fuzzy c means clustering and Ad hoc on demand distance vector.

6. Comparative study/ Analysis

Succeeding the algorithms that we used in this work we discuss about the comparative study section that compares different existing methods to obtain the possible outcomes some of the methods that we compared are described below:

• The fuzzy c means clustering and Adaptive linear combination rules are used by the CRN secondary user which enhances Cognitive Spectrum Sensing (CSS) achievements and resistance to some malicious users.
• We utilize Deep Q Learning (DQL) technique which increases the spectrum by constantly changing the agent policy on the basis of Q-values, leveling short term incomes with long term throughput success and reacting to modifying network conditions and improving PU density.
• In the IoV system the game theory creates Nash equilibrium that decreases obstacles and increases the material usage. We enhance the spectrum reutilization and radio materials effectively by manageable and supportive game models.
• Among primary and secondary IoT users, the homomorphic encryption among spectrum sharing protects the complex data and this also safeguards security, minimizes unwanted access, and gains both user groups.
• Dynamic node transitions and intelligent path selection over node calculations are possible via AODV routing protocol and ASM, this improves the interaction rules dependability and efficiency by dynamically adjusting the network surroundings.

7. Simulation results/ Parameters

In this research after completing the comparison of different methods with our proposed technique we utilize some of the parameters to compare our results for CRN based networks. The parameters that we used are authentication time, throughput (mbps), packet delivery ratio (%), delay (s) and execution time (s) are compared with the number of devices.

8. Dataset LINKS/ Important URL

Here we provide some important links to see all the explanations, applications, previous issues and additional references we refer the following links to clarify the uncertainties of Cognitive radio networks:

• https://link.springer.com/article/10.1007/s11276-022-03210-7
• https://www.sciencedirect.com/science/article/pii/S2352864823000329
• https://ieeexplore.ieee.org/abstract/document/9905718/
• https://www.sciencedirect.com/science/article/pii/S1084804522001059

9. Cognitive Radio Network Applications

• Wireless
• Smart Grids
• Emergency Service

Above we specified some of the cognitive radio network applications.

10. Topology for Cognitive Radio Network

Now we discuss the topology for CRN, The Cognitive Radio Network commonly uses constant and reliable topologies and it uses a group of centralized and decentralized format, it also modifies to alter network conditions, improves spectrum utilization and permitting effective interactions in dynamic wireless environments.

11. Environment in Cognitive Radio Network

Following the topology next we see the environment of CRN technology, In dynamic and heterogeneous surroundings Cognitive radio networks are operated at which point the radio frequency spectrum conditions may differ. For developing the interactions and improving whole efficiency in various wireless settings CRN networks are knowledgeably modified to alter the environmental factors like spectrum availability, interference levels and network traffic.

12. Simulation tools

The proposed methods follow the subsequent simulation tools for CRN networks like, the software requirement tool namely NS3.26 python and this incorporates the OS Ubuntu 16.04 LTS and the language here we used to implement the work is C++. These are the simulation tools that we employ for CRN to get the results.

13. Results

In this work we utilized CRN, a wireless sensor network by accessing the radio frequency networks. These CRN networks can obtain the accurate and correct possible results by comparing various parameters and these networks can be implemented in a tool NS3.26 Python to get the outcome.

Cognitive Radio Network Research Ideas:

Below we provided are some of the research topics related to Cognitive Radio Networks that are useful when we go through the descriptions of CRN:

1. A Critical Survey on Security Issues in Cognitive Radio Networks
2. Implementation of Spectrum Sensing Algorithms for Cognitive Radio Network in FPGA
3. Spectrum Reallocation Algorithm in Cognitive radio Networks Based on Secondary User Mobility Model
4. Clustering in Cognitive Radio Networks Using Blockchain: Issues & Challenges
5. Machine Learning based Spectrum Prediction in Cognitive Radio Networks
6. Approaches for Advanced Spectrum Sensing in Cognitive Radio Networks
7. Cooperative Spectrum Sensing in Cognitive Radio Network Using Selective Soft-Information Fusion Scheme
8. Security Issues in Centralized Spectrum Allocation in Cognitive Radio Networks
9. An Effective Hybrid Spectrum Sensing Methodology in Cognitive Radio Network Using Deep Temporal Convolution Network
10. Performance analysis of undersampled autocorrelation based spectrum sensing for cognitive radio networks
11. Quality of Service Optimization for Green Cognitive Radio Network: a Comparative Study of Flower Pollination and AHP-TOPSIS Algorithm
12. Spectrum Sensing Based on CNN-LSTM with Attention for Cognitive Radio Networks
13. Deep Deterministic Policy Gradient for Throughput Maximization in Energy Harvesting NOMA-Cognitive Radio Network
14. Cooperative Rate Splitting Multiple Access in Cognitive Radio Networks: Power Allocation and Location Optimization
15. Unsupervised Learning-Based Resource Allocation for Cognitive Radio Networks
16. Novel Methodological Proposal for Decision-Making in Decentralized Cognitive Radio Networks Based on Information Exchange Between SU
17. Secondary Network Throughput Optimization of NOMA Cognitive Radio Networks Under Power and Secure Constraints
18. Resource Allocation in Multi-Cluster Cognitive Radio Networks With Energy Harvesting for Hybrid Multi-Channel Access
19. An Improved Cooperative Spectrum Sensing Scheme for Emulation Attacker Detection in Cognitive Radio Network
20. Exploiting Secure Multihop Transmission in Underlying Cognitive Radio Networks: Analysis and Deep Learning Approaches
21. Interference-Aware Channel Allocation using Cournot Game Comparing with Potential Game Algorithm to Maximize the Transmission Data Rate of Secondary Users in Cognitive radio Network
22. Optimization of Spectrum Utilization Efficiency in Cognitive Radio Networks
23. Deep Learning Aided Multi-Level Transmit Power Recognition in Cognitive Radio Networks
24. Identifier-Based Rendezvous Scheme for Asynchronous Cognitive Radio Wireless Networks
25. Performance Optimization of Energy-Harvesting Underlay Cognitive Radio Networks Using Reinforcement Learning
26. Relaying Communications in Cognitive Radio Networks with Energy Scavenging and Artificial Noise: Reliability-Security Trade-off Analysis
27. Rule-based Routing in the Cognitive Radio Network for the Data Routing
28. On the Secrecy Analysis of Dual-Hop SWIPT-Based Multi-Source Underlay Cognitive Radio Networks
29. Interference Modelling for Heterogeneous Cognitive Radio Networks
30. Energy-Efficient Opportunistic Spectrum Access in Multichannel Cognitive Radio Networks
31. Coalition Formation for Outsourced Spectrum Sensing in Cognitive Radio Network
32. Maximizing the Sum-Rate of Secondary Cognitive Radio Networks by Jointly Optimizing Beamforming and Energy Harvesting Time
33. Convergence of IoT and Cognitive Radio Networks: A Survey of Applications, Techniques, and Challenges
34. Robust Beamformer Design in Active RIS-Assisted Multiuser MIMO Cognitive Radio Networks
35. Securing Cognitive Radio Networks via Relay and Jammer-Based Energy Harvesting on Cascaded Channels
36. Performance Analysis of Full-Duplex Multirelaying Energy Harvesting Scheme in Presence of Multiuser Cognitive Radio Network
37. Power Control and Trajectory Optimization for a THz-Enabled UAV-Relay in Cognitive Radio Network
38. Enabling Cooperative Routing in Cognitive Radio Networks via Overlapping Coalitions by using Coalition Formation Games
39. Spectrum and Power Allocation Scheme Using HoDEPSO-RP Approach for Cognitive Radio Network
40. Cooperative Cognitive Radio Networks with Active SUS Game-Theoretical Approach Based on the Stackelberg Model
41. Uplink Multiuser Scheduling in Cognitive Radio Network using Group-based SVM Scheduling and Comparing with a Linear Classifier
42. Neighbour Discovery in Cognitive Radio Networks using Blockchain: Issues & Challenges
43. Massive SSDF Attackers Identification in Cognitive Radio Networks by Using Consistent Property
44. Short-Packet Covert Communication in Interweave Cognitive Radio Networks
45. Energy-Efficient in Cognitive Radio network by optimizing sensing and transmission time
46. RIS-Aided Physical Layer Security Improvement in Underlay Cognitive Radio Networks
47. QoS-Dependent Event-Triggered Control for UAVs on Cognitive Radio Networks Subject to Deception Attacks
48. Analysis of Spectrum Sensing in cognitive Radio Networks using Generalized Orthogonal Matching pursuit
49. A Channel Allocation Framework Under Responsive Pricing in Heterogeneous Cognitive Radio Network
50. A Graph Convolution Network Based Adaptive Cooperative Spectrum Sensing in Cognitive Radio Network