Cognitive Radio Network Research Projects for Students

A CRN Research topic is one of the significant topics which are used in this research. It is a wireless communication network and is widely used in many applications. Here we used this technique to tackle some difficulties in the previous works and we provide the details about this technology.

1. Define CRN

CRN stands for Cognitive Radio Network. In this we initially start with the definition for our proposed technique. CRNs allow more adaptable and adjustable wireless communication system, which will cause best use of the radio frequency spectrum and assisting different applications like Internet of Things (IoT), public safety communications and wireless broadband access. It is a kind of network in the form of wireless communication, where the devices are also known as cognitive radios and it must have the capacity to cleverly sense and adjustable to their environment to optimize the utilization of possible radio spectrum. These networks are created to enhance the use of spectrum that effectively permits the devices to dynamically allow not utilized or less used spectrum bands advantageously while evading the intervention with licensed users.

2. What is CRN?

Following the definition next onwards we look for the detailed description for this proposed CRN technique. It is a wireless communication network in which the separate nodes or devices like laptops, mobile phones or other wireless devices are prepared with the assistance of cognitive capabilities. These cognitive capabilities permit the nodes to adjust their transmission parameters, take decisions to optimize the utilization of the possible radio spectrum. A cognitive radio network tackles the problems by permitting the devices to dynamically allow and use unoccupied or less used spectrum bands advantageously not affect any dangerous intervention to licensed users. The major objective of CRN is to enhance the use of radio frequency spectrum that is a finite and treasured resource. Traditionally, wireless communication systems work in an allocated frequency band, which frequently causes spectrum to less usage and ineffectiveness because of fixed allocation policies.

3. Where CRN used?

CRN is utilized in many of the places; here we discuss where to utilize this proposed technique. It holds possibilities for improving the spectrum usage effectiveness, allowing more adaptive and assisting different applications like Internet of Things, Public safety interactions and wireless broadband access. Some of the key features for CRNs are Interference Mitigation, Spectrum Mobility, Spectrum Management, Learning and Adaptation and Spectrum Sensing.

4. Why CRN technology proposed? , previous technology issues

Here the CRN technology is proposed in this research to tackle some of the problems that will come across in existing wireless communication technologies. Some of the problems that it will have are Interference and Congestion, Spectrum Scarcity, Limited Spectrum Awareness, Regulatory Constraints and Spectrum Fragmentation.

5. Algorithms / protocols

Now we go for the algorithms to be used for this proposed technique. These methods or techniques are used to overcome the existing technology issues. The methods that are employed for this research are Game Theory, Bellman-Ford Dingo Optimization Algorithm (BFDOA), Hybrid K-hop cluster-based joint optimization strategy and Stochastic Optimization methods.

6. Comparative study / Analysis

For this research we compared various methods or techniques to enhance the findings for this research. Here we proposed a Hybrid K-hop cluster with joint optimization approach for scalability and energy consumption. To decrease the interference we use the Game theory technique on the basis of theory. For optimum path BFDOA technique is utilized and then the Stochastic optimization technique is used for resource allocation.

7. Simulation results / Parameters

We employ the parameters like Total transmission power, Energy Consumption, Routing Overhead, Average network lifetime, Routing overhead, Number of alive nodes and Throughput. These are the metrics that are utilized to improve the accuracy of the findings for this research.

8. Dataset LINKS / Important URL

The subsequent are the links that are offered to go through them, when we have any queries or doubts related to our proposed CRN technique and it will have many details relevant to this CRN technique.

• https://www.sciencedirect.com/science/article/abs/pii/S2542660522000749
• https://www.tandfonline.com/doi/abs/10.1080/0952813X.2020.1818291
• https://ieeexplore.ieee.org/abstract/document/9878087

9. CRN Applications

Our proposed CRN technique has different applications that can be overcome along various domains. Some of the applications are Internet of Things, Wireless Broadband Access, Dynamic Spectrum Access (DSA), Wireless Sensor Networks, Public Safety Communication, Smart Grids, Satellite Communications and Military Communications.

10. Topology for CRN

Let’s look for the topology to be used for CRN, a proposed technique. It is employed in different topologies like hierarchical, satellite, hybrid, ad hoc, cellular, SDC-based networks and mesh, selected on the basis of particular applications which are needed for the dependable interaction and effective spectrum usage.

11. Environment for CRN

The environment for CRN succeeds in heterogeneous or dynamic environments where the differing traffic demands, altering network conditions and spectrum scarcity requires the adjustable spectrum management and effective resource utilization. They excel in situations like emergency response, Wireless broadband, military operations and IoT deployments where the traditional networks handle difficulties in interference mitigation and spectrum allocation.

12. Simulation tools

The software requirements that are required for this proposed CRN technique are as follows. The developmental tool that is utilized to simulate the work is Matlab-R2023b. Then the operating system that is utilized to do the research is Ubuntu 16.04. These are the simulation tools that are possible to do this research.

13. Results

CRN is a wireless communication network and its goal is to enhance the use of radio frequency spectrum. The research is validated by comparing the various performance metrics with the previous research to obtain the increased accuracy for this research. It is simulated by using a developmental tool like Matlab-R2023b.

Cognitive Radio Network Research Topics and Ideas

Succeeding are the topics that are relevant to our proposed CRN technique. We have to utilize these topics to propose our research to obtain the best findings and to improve the results for this research.

1. A Study Of Dynamic Thresholds Power Detection Spectrum Sensing Techniques In CRN
2. A Hybrid Architecture Based on Conformer and CRN Parallelism for Regulated Speech Quality Measurement Model
3. Blockchain Based Spectrum Mapping and Access to Secondary Users for Enhanced Channel Security Efficiency in CRN
4. Secure and Reliable IoT Communication in Underlay CRN With Imperfect CSI
5. CRN: Camera Radar Net for Accurate, Robust, Efficient 3D Perception
6. Reconfigurable Intelligent Surfaces-Assisted Overlay Energy Harvesting-CRN Under Resilient Primary User Outages
7. Secrecy Analysis of Underlay CRN in the Presence of Correlated and Imperfect Channel
8. Throughput Maximization for RSMA-Empowered CRN under Short-Packet Communications: A DRL-Based Approach
9. Spectrum-Efficient User and Qos-Aware Resource Allocation With Enhanced Uplink Transmission in U-LTE Networks Co-Occurrence With Wi-Fi by CRN
10. Multi-Objective Spectrum Allocation Scheme for Multi-User mmWave-assisted CRN-5G Networks
11. Clustering in Cognitive Radio Networks Using Blockchain: Issues & Challenges
12. Energy-Efficient Resource Allocation in Multiple UAVs-Assisted Energy Harvesting-Powered Two-Hop Cognitive Radio Network
13. Neighbour Discovery in Cognitive Radio Networks using Blockchain: Issues & Challenges
14. Resource Allocation in Multi-Cluster Cognitive Radio Networks With Energy Harvesting for Hybrid Multi-Channel Access
15. Robust Beamformer Design in Active RIS-Assisted Multiuser MIMO Cognitive Radio Networks
16. RIS-Aided Physical Layer Security Improvement in Underlay Cognitive Radio Networks
17. Spectral–Temporal Model for Opportunistic Spectrum Access in Cognitive Radio Networks
18. Efficient Game Theory Based Resource Allocation and Cluster Based ANT Optimization for IoT based Cognitive Ratio Networks
19. DDPG-Based Joint Time and Energy Management in Ambient Backscatter-Assisted Hybrid Underlay CRNs
20. Resnet Associated Cross-Layered Routing in Cognitive Radio Network
21. Trust-based Energy Aware Routing Protocol to Improve Network Lifetime in CRNs
22. Cooperative Spectrum Sensing Deployment for Cognitive Radio Networks for Internet of Things 5G Wireless Communication
23. Convergence of IoT and Cognitive Radio Networks: A Survey of Applications, Techniques, and Challenges
24. Joint Sensing and Transmission Optimization for IRS-Assisted Cognitive Radio Networks
25. Multiuser NOMA With Multiple Reconfigurable Intelligent Surfaces for Backscatter Communication in a Symbiotic Cognitive Radio Network
26. Secondary Network Throughput Optimization of NOMA Cognitive Radio Networks Under Power and Secure Constraints
27. Secrecy Rate Maximization for Multicarrier-Based Cognitive Radio Networks With an Energy Harvesting Jammer
28. A Channel Allocation Framework Under Responsive Pricing in Heterogeneous Cognitive Radio Network
29. An Adaptive Neural Network for Primary User Emulation Attacks in Cognitive Radio Network
30. Secrecy Performance of SWIPT Cognitive Radio Networks with Eavesdropper’s Decoding Capability
31. Optimal Deep Learning Empowered Malicious User Detection for Spectrum Sensing in Cognitive Radio Networks
32. Timely Target Tracking: Distributed Updating in Cognitive Radar Networks
33. An Effective Hybrid Spectrum Sensing Methodology in Cognitive Radio Network Using Deep Temporal Convolution Network
34. Rule-based Routing in the Cognitive Radio Network for the Data Routing
35. Artificial Noise-Aided Secure Cognitive Radio Networks: Design and Performance Analysis
36. Securing Communications for IRSs-Assisted mmWave Cognitive Radio Networks
37. Trusted Channel Selection in Cognitive Radio Network using VIKOR method
38. Unsupervised Learning-Based Resource Allocation for Cognitive Radio Networks
39. A Reliable Spectrum Sensing Method Based on Deep Learning for Primary User Emulation Attack Detection in Cognitive Radio Network
40. Performance of Energy Harvesting Cooperative Cognitive Radio Network Under Higher Order QAM Schemes
41. Elite Oppositional Hunger Games Search Optimization Based Cooperative Spectrum Sensing Scheme for 6G Cognitive Radio Networks
42. Security Issues in Centralized Spectrum Allocation in Cognitive Radio Networks
43. Impacts of Sensing Energy and Data Availability on Throughput of Energy Harvesting Cognitive Radio Networks
44. Short-Packet Covert Communication in Interweave Cognitive Radio Networks
45. Robust Beamforming for RIS Enhanced Transmissions in Cognitive Radio Networks
46. Constructing Connected-Dominating-Set with Maximum Lifetime in Cognitive Radio Networks
47. Frequency Selective Hybrid Beamforming and Optimal Power Loading for Multiuser Millimeter Wave Cognitive Radio Networks
48. Performance Optimization of Energy-Harvesting Underlay Cognitive Radio Networks Using Reinforcement Learning
49. End to End Model to Reduce the Inference, Jamming, and to Increase the Trust from the Compromised Secondary Nodes in Cognitive Radio Networks
50. Securing Cognitive Radio Networks via Relay and Jammer-Based Energy Harvesting on Cascaded Channels