Cognitive Radio is a dynamic spectrum sensing/access/sharing technology in wireless communications which resolves the issues of spectrum scarcity and also overutilization. In other words, CRN means that permits the users i.e. secondary users (SUs) to access channels that are not used by the primary users (PUs). On this page, we deliver all possible ideas for doing projects on cognitive radio networks with all the latest updates in it. 

“The primary objective of CRN is to activate the devices or terminals by various communication technologies that co-occur between the same frequency bands”. Further, it offers a huge rate of benefits and performance success for providing high bandwidth, throughput, and delay for end users. 

Major Components of the CRN Models

  • Ad Hoc Model 
    • PUs: Mobile terminals and other nodes are called the spectrum owner which has a high priority for accessing channels and bands. 
    • SUs: These types of users perform the spectrum sensing techniques for free spectrums utilization. 
    • PUBs: It is a primary user base station (PUB) which is to access the PUs, spectrum bands are coordinated 
    • CRBs: It is a cognitive radio base station (CRBs) that is to performs the coordination of coexistence of PUs with CRs. 
  • Infrastructure Model 
    • PUs: working like ad hoc model 
    • SUs: makes the communication link between users in an ad hoc fashion. 
    • PUBs: working like ad hoc model 
    • CRBs: It does not available in the ad hoc model 

Cognitive Radio Functions 

           A life cycle of CRN consists of spectrum sensing white space which chooses the best frequency bands for spectrum access coordination with other users. The primary functions supported in CRN can be as follows, 

  • Spectrum Sensing / Access / Analysis 
  • Spectrum Sharing / Allocation 
  • Spectrum Handoff / Management 

Benefits / Risks / Challenges of CRN 

  • Maximize the Communications 
    • Challenges: Lack of communication optimization
  • Supports Security / Cybersecurity Features 
    • Challenges: Lack of spectrum unavailability to attackers 
  • Maximize the Spectrum Utilization 
    • Challenges: Interference between the receivers and attackers result in low spectrum utilization 

Cognitive-Communication Technologies 

  • Cooperative Communication
    • Cooperative Spectrum Sensing and Sharing 
  • Non-Cooperative Communication 
    • Non-Cooperative Spectrum Sensing and Sharing 

For instance, in the following, we mentioned that core communication technologies make the communication between PUs and SUs. In this case, we mentioned the technologies covered in the OSI layers as follows, 

  • IP Layer 
    • Able to manage the real-time traffic (Video / Audio) for QoS management 
    • Uses sensing-based traffic and transmission control 
  • MAC Layer 
    • Activates spectrum sensing and transmission by MAC IEEE 802.11a 
    • Seamless sharing of spectrum and manage spectrum adaptively 
    • Implements effective scheduling of sensing periods
    • Overlay CRN on MAC IEEE 802.11 based networks   
  • PHY layer 
    • Estimates noise measurements in spectrum sensing 
    • Adopts the sensing metric adaptively by RSSI 
    • Performs IN-BAND sensing for protection of PU and B-SSCT

Based on the architecture, research issues, challenges, and risks of CRN, various research ideas and topics are currently emerging. All these topics are widely researched by world-class engineers and scholars. In particular, we are working on all aspects of CRN to enlarge its real-time applications to mobile and other users. Some of the project ideas and research-based information is highlighted below, 

Top 6 Research Projects on Cognitive Radio Networks

Top 6 Research Ideas for Projects on Cognitive Radio Networks

  • Energy Harvesting 
  • Allocation of Resources 
  • MAC Protocol Design 
  • Multi-Class PUs and SUs 
  • Spectrum Handoff / Sensing 
  • Overlay / Underlay Design Paradigms 

Research Issues in CRN 

  • MAC Protocols Design 
    • Routing
    • Resource Scheduling 
  • Application: Indoor Positioning 
    • Positioning by learning 
    • Triangulation based positioning 
  • Cooperative Spectrum Sensing 
    • Adaptive Threshold Setting 
    • Detection Ratio and False Alarm Rate 
    • Fusion Rules Generation 
  • Classical Spectrum Sensing 
    • Measures the energy, false alarm ratio, and detection ratio
    • Radio power strength measurements

To address these issues, different techniques are used such as queueing model, game theory, and relaying and cooperative schemes. These techniques are vital while implementing any projects on cognitive radio networks. Further from the several set of challenges of CRN, routing is one of the significant issues, which is essential for addressing QoS issues like packet loss probability, delay, and throughput. For that route must be optimum in data transmission from the source to the destination via intermediate hops and available spectrum, channels, and bandwidth are used for it. 

Cognitive Radio Network (CRN) Routing Schemes

  • Local Spectrum Knowledge
    • Link Quality 
    • Throughput based
    • Latency based
    • Stability based
    • Intervention based
    • Probabilistic Schemes
    • Energy-based 
  • Full Spectrum Knowledge
    • Mixed-integer nonlinear programming (MINLP)-NIMLP Optimization
    • Graph-based

If your topic is routing for your research or final year projects on cognitive radio networks, then you can contact us. There are a wide variety of issues in routing. Performance tradeoff occurs in demonstration of network uses shortest path routing with random techniques. Particular research challenges and risks for routing in CRN can be as follows. 

Routing Challenges in CRN

  • Host-based
    • Secondary Users (SUs) Mobility in Heterogeneous Networks
    • Scheduling in Multicast Multimedia Transmissions
    • Channel-based Switching and Latency Control using Backoff Algorithm
  • Network-based
    • Minimization of Energy Utilization in Network-wide 
    • Fast Resource assisted Route Recovery 
    • Performance Tradeoffs Mechanism for Network-wide and Number of Hops
  • Channel-based 
    • Insufficient Shared Control Channel
    • On-demand Route Discovery based on the Channel Decision
    • Prediction of Channel Availability in Dynamic Spectrum
    • Operation on Single-Channel and Multi-Channel Diversity

Routing Metrics in CRN

  • Multi-Path
    • Shared Nodes
    • Ad-hoc
    • Path Stability
    • Resource Usage 
    • Latency
    • Interference of Secondary User 
    • Number of Hops
    • Closeness Distance of Route
  • Single-Path 
    • Power Utilization
    • Probabilistic
    • Latency
    • Route Accessibility
    • Number of Hop 
    • Location-based
    • Availability of Spectrum

Routing protocols are different based on the routing metrics, performance, and working paradigms, and also services forwarding from the source to the destination is identified by the routing metrics, which are highlighted above. Further, the detailed descriptions of routing metrics are as follows, 


It refers to the fundamental needs to enhance the performance of the routing protocol 

  • Number of Hops: Total count of hops present between source and destination
  • Network Throughput: Capacity of the network to transmit the data packets
  • End-to-end Latency: Overall time delay in data transmission from one end to another 
  • Packet delivery rate: Possibility of number of packets reached at the end-user 


It clearly describes the basic operations of the routing protocols

  • Comprise the backup routes for the best routing protocol
  • Optimum route selection in available spectrum
  • Non-cooperative MAC of SUs can be harmonized 
  • Assure the channel allocation for SUs to improve the QoS 
  • Eliminate ultra-dense area to minimize the interference among multiple users 
  • Maintain the reliability between the links for stable link


This feature represents the routing protocol ability, which satisfies the application demands. 

  • The complexity between space and time on using a routing technique 
  • Complexity in route and traffic load of each node and link is established in CRN 
  • Route control complexity is adjusted which measures the control packets transmission for every primary and secondary user. 

Routing Challenges in CRN 

  • Spectrum Availability 
  • Allocate various issues for different channels using interruption probability and availability 
  • Interruption Time 
  • Considers the channel switching time for making the decision 
  • Deafness and Signalling Issue 
  • Routing metrics are affected by deafness issues and the routing protocol is not adequate. 
  • Inheritance by conventional networks
  • The common issues of routing metrics address the CRNs for addressing it. 

Machine learning is one of the solutions for finding the optimum path from the source to the destination node. We can also use these techniques for other operations of CRN. In the following, the most important machine learning schemes are listed. 

Machine Learning Schemes for CRN Routing 

  • Reinforcement Learning
    • Deep Q-Learning
    • Prioritized Experience Replay
    • Deep Q-Networks
  • Unsupervised Learning
    • Density Estimation
      • Deep Boltzmann Machine (DBM)
      • Gaussian Mixture Model
      • Generalized Denoising Auto-Encoders
      • Kernel Density Estimation (KDE)
    • Dimensionality Reduction
      • Auto-Associative Neural Network
      • Local Linear Embedding (LLE)
      • Stacked Auto-Encoders (SAE)
    • Clustering
      • K-Means Clustering
      • Deep Belief Network (DBN)
      • Conventional Neural Network (ConvNet)
  • Supervised Learning
    • Classification
      • Deep Belief Network (DBN)
      • Neural Network (NN)
      • Naïve Bayesian Classifier
      • Binary Decision Tree (BDT)
      • Conventional Neural Network (CNN)
      • Recurrent Neural Network (RNN) 
    • Regression

On the whole, we discussed all the emerging ideas, techniques, and implementation topics for the successful accomplishment of projects on cognitive radio networks. If you want to know important or current aspects / trend in CRN, you can contact us.