Future Directions in Cognitive Radio Networks Research

Cognitive Radio Networks (CRN) is used to allocate the unoccupied spectrum to the secondary users to meet their communication requirements. Here, the cognitive radios sense the spectrum in the operating environment and improve the network performance. Overall, it overcomes the problems of traffic load, node density, mobility, and signal propagation. This article holds the information of Current and Future Directions in Cognitive Radio Networks Research with exciting research ideas!!!

In recent days, wireless technologies have the properties of constrained cognitive, frequency band agility, adaptation, spectrum selection, and many more. In order to improve the spectrum efficiency and network volume, it uses optimal dynamic protocols. Most probably, much of the researches are done related to the functionalities of the network, link, and physical layers. For instance: routing, modulation, Mac, etc. Also, these layers contain some security issues like jamming, sensing falsification, eavesdropping, PUEA, etc. Further, we have also listed out some important functionalities of CRN as follows,

What are the major functions of CRN?

  • Reconfigurable Decision on Radio 
  • Spectrum Environment Sensing 
  • Construction of Smart Radio Environs
  • Spectrum Provisioning for SUs Access

Next, we can see some cognitive radio network characteristics. Since these features motivate the current scholars to move on to Future Directions in Cognitive Radio Networks Research. And, they are classified as distributed spectrum control, operational state languages, and operating environmental sensing. 

Cognitive Radio Characteristics

  • Distributed Spectrum Control: In CR, the spectrum is allocated dynamically based on the user requests so the frequency band changes its position constantly. As a result, CR networks need stable resource distribution in spite of heavy network traffic for dynamic users. In order to make this possible, several advanced techniques are used for efficient spectrum allocation.  
  • Operational State Languages: In CRN, this language is used to share the current status of the environment to the other network entities/devices. And, this status includes the details of available emitters in the network. So, it is known as the operational state language.
  • Operating Environ Sensing: In general, cognitive radio works effectively in multi-dimensional infrastructure. This environ is needs to be adaptive to any fast dynamic variation and traffic overhead on using both cooperative and non-cooperative emitters. Further, the environmental configuration changes are necessary to inform other devices.

Additionally, we have also given the major operations involved in the cognitive radio network. In specific, these operations are common to any kind of CRN research and development. Even the research ideas will depend on the improvisation of the following functions,

Cognitive Radio Functions

  • Spectrum Mobility: It is intended to work on the dynamic spectrum where the user frequently changes their frequency based on availability and service potentials. It let the user’s radio terminal move on a better spectrum for smooth transmission.
  • Spectrum Sensing: For improving the spectrum allocation, it first senses the environment to find the holes in the PU’s spectrum. After identifying the unused spectrum using PU detection approach the spectrum is efficiently allocated to the SUs. Generally, spectrum sensing is performed based on co-operative, interference, and transmitter detection techniques.
  • Spectrum Management: Here, while sensing the spectrum, we need to analyze the quality of service (QoS) of the spectrum bands. Based on this, we need to decide on selecting the optimal spectrum for the user requests. The spectrum which has high QoS and capability to satisfy the user needs is decided as the best spectrum among overall bands. On the whole, this spectrum management requires both spectrum inspection and detection processes.
  • Spectrum Sharing: It is the very important phase in spectrum allocation which basically works on spectrum scheduling policy an impartial nature. Also, it is a tricky task to attain the best spectrum provision for an open band. In the previous work, it depends on MAC protocol but now it is in need of advanced techniques.  

Cognitive Radio Architecture

As a matter of fact, the CR network is classified into primary and secondary networks. In the primary network, the primary users (PUs) are connected to the primary base stations (PBS) for data sharing where both PUs and PBSs are not considered as cognitive. Also, the PUs have licensed spectrum which has higher priority to use the specific spectrum. 

In the secondary network, the secondary users (SUs) are coordinated by the secondary base stations (SBSs) that work as a hub. Here, the SUs share the same base station for a particular range. And, if the single spectrum band is shared by multiple SBSs then the control is passed on to the broker of the central spectrum. In some cases, the SUs can interact with one another even without the SBSs. For instance: VANET and Internet of Things. Next, we can see the general cognitive radio system architecture. It is most probably classified as either centralized or decentralized form.

CR Architectural Approaches

  • Distributed Technique – In this, there is no central power to control spectrum distribution and access instead it is managed by the cognitive radio users
  • Centralized Technique – In this, there will be an entity that has central authority to take a decision on spectrum distribution and access (For instance: base station)

Further, we have also given some importance-performance metrics that are used to evaluate the overall cognitive radio system efficiency. These metrics are common in all CRN systems but still may vary based on the project requirements and their handling concepts. 

Future Directions in Cognitive Radio Networks Research Implementation

CRN Performance Regulating Parameters 

  • Bandwidth
  • Probability of False Alarm
  • Modulation
  • Backoff Period (s)
  • DTV threshold [dBm / MHz] (for sensing)
  • Detection Probability
  • Adjacent Channel Leakage Ratio (ACLR)
  • Wideband Sensing Frequency (1 per minute)
  • Transmit Power Control (TPC)
  • Maximum Transmission Power adjacent to DTV [dBm]

As a matter of fact, CRN is a promising technology to overcome the heavy radio traffic issue by efficiently allocating spectrum to the users. Also, it is sure to meet their communication requirements and scale up the wireless system for the next Quarter-century. In addition, we have given the Future Directions in Cognitive Radio Networks Research through the following CRN research gaps followed by future CRN supportive technologies.

Future Research Gaps in CRN 

  • Assessment of the CR system in real-world deployment is still a challenging task to perform. So, it demands CR testbeds to evaluate the system at different scales and development phases. 
  • The emerging CRN is expected to address the research holes and not commendably resolve technical issues. And some of them are given as follows,  
    • CR system and protocol modeling
    • Multipoint cooperative interaction
    • Sensing of spectrum band
    • Adoption of incentive models
    • Dynamic radio resource distribution and access
    • The emergent behavior of the system
    • CRN privacy and security
    • An adaptive algorithm for CR system

Future Technologies in CRN

  • Big Data Servers and Grid Analytics
  • VANET 
  • Software-Defined Cognitive Networks 
  • Fog assisted Cloud Computing 
  • Mobile Edge Computing (MEC)

For your ease, our resource team has precisely pointed out the Future Directions in Cognitive Radio Networks Research in the following. These areas are listed by our expert’s suggestion after the in-depth study of current research.

Future Research Directions in CRN

  • Cognitive V2X and V2V Applications and Services
  • Innovation of Spectrum Sharing Models in Deployments
  • CR Standard Regulatory Policies and initiatives
  • 5G based Cognitive Radio Networks
  • Radio Resource and Network Slicing Techniques
  • Innovations of CRN in Future Wireless Technologies
  • Virtualized Cognitive Radio Mechanisms
  • Artificial intelligence for Spectrum Sensing
  • Coexistence of Heterogeneous Network in Unlicensed Bands
  • Deep Machine Learning Mechanism in Big Data Mining
  • Cognitive Radio based UAV Communications 
  • Virtualized Spectrum Resource Management
  • Context-Aware Spectrum Sharing in Future Networks
  • Sustainable Spectrum Sharing in Digital Inclusion
  • 5G based RAN Network Slicing
  • Spectrum Efficacy Evaluation in End-to-End Wireless Networks
  • Spectrum Sharing in IoT Applications
  • Advanced Adaptive Spectrum Access System for CBRS
  • Optimization of Spectrum Efficiency at Different Layers
  • 6G based Advanced Signal Processing
  • Radio Resource Management (RMM) in Future Technologies
  • Energy-Aware Spectrum Consumption in High-Frequency Bands 
  • Privacy Enforcement in Spectrum Sharing 
  • Backhauling and Fornthauling Challenges in Future Networks
  • Advancements in Cognitive Radio Technologies for Spectrum Sensing
  • Employment of Cognitive Radio in Satellite Communications
  • Cognitive Spectrum Accessing and Sharing
    • Licensed/Authorized Shared Access (LSA/ASA)
    • Dynamic Spectrum Access (DSA)
    • enhanced Licensed Assisted Access (eLAA)

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