Analyzing Cognitive Radio Network Operation With the Mechanism of Deciding Handoff and Process of Handoff Employing Varied Distribution Models (5G)

2021 ◽  
Vol 13 (4) ◽  
pp. 37-64
Author(s):  
Sumathi D. ◽  
Manivannan S. S.
Keyword(s):  
Decision Making ◽  
Cognitive Radio ◽  
Vital Role ◽  
Distribution Models ◽  
Spectrum Handoff ◽  
Spectrum Mobility ◽  
Network Operation ◽  
The One ◽  
Adjacent Channels ◽  
Multiple Attributes Decision Making

Cognitive radio networks (CRNs) deal with sensing, decision making, sharing, and mobility. Among these aspects, spectrum mobility assumes a vital role for spectrum handoff to occur. When the spectrum handoff process is analyzed, there are call drops, spectrum handoff, and interferences with the adjacent channels. For minimizing the handoff of the spectrum's probability as well as the call drops, which involves three processes, which are followed in sequential order, results demonstrate a better QoS. In the following three processes, the first process starts with the Markov model that identifies a channel's states and selects the channel which is apt. The second process, multiple attributes decision making (MADM), methods choose one of the best possible channels among all the channels based on various metrics in the proposed hybrid method. In the third process, the spectrum handoff is analyzed through different distribution models to indicate which model is the desirable one for a handoff process such as the one with stationary users or the other with non-stationary users.

scholarly journals A Spectrum Handoff Scheme for Optimal Network Selection in NEMO Based Cognitive Radio Vehicular Networks

2017 ◽  
Vol 2017 ◽  
pp. 1-16 ◽  
Author(s):  
Krishan Kumar ◽  
Arun Prakash ◽  
Rajeev Tripathi
Keyword(s):  
Cognitive Radio ◽  
Vehicular Networks ◽  
Mobile Network ◽  
Network Selection ◽  
Spectrum Handoff ◽  
Radio Access ◽  
Optimal Network ◽  
Multiple Attributes ◽  
Grey Relational ◽  
Multiple Attributes Decision Making

When a mobile network changes its point of attachments in Cognitive Radio (CR) vehicular networks, the Mobile Router (MR) requires spectrum handoff. Network Mobility (NEMO) in CR vehicular networks is concerned with the management of this movement. In future NEMO based CR vehicular networks deployment, multiple radio access networks may coexist in the overlapping areas having different characteristics in terms of multiple attributes. The CR vehicular node may have the capability to make call for two or more types of nonsafety services such as voice, video, and best effort simultaneously. Hence, it becomes difficult for MR to select optimal network for the spectrum handoff. This can be done by performing spectrum handoff using Multiple Attributes Decision Making (MADM) methods which is the objective of the paper. The MADM methods such as grey relational analysis and cost based methods are used. The application of MADM methods provides wider and optimum choice among the available networks with quality of service. Numerical results reveal that the proposed scheme is effective for spectrum handoff decision for optimal network selection with reduced complexity in NEMO based CR vehicular networks.

Spectrum Handoff Mechanism in the Framework of Mobility Management in Cognitive Radio Networks

2019 ◽  
Vol 10 (4) ◽  
pp. 19-35 ◽  
Author(s):  
Semba Yawada ◽  
Mesmin J Mbyamm Kiki ◽  
Mai Trung Dong
Keyword(s):  
Cognitive Radio ◽  
Cognitive Radio Networks ◽  
Wireless Systems ◽  
Radio Spectrum ◽  
Radio Networks ◽  
Innovative Technology ◽  
Management Approach ◽  
Spectrum Handoff ◽  
Opportunistic Access ◽  
Spectrum Mobility

Cognitive radio appears as an innovative technology in the field of access to wireless systems, aimed at significantly improving the use of the radio spectrum by allowing an opportunistic access manner. This article deals with some of the important characteristics of the spectrum mobility in cognitive radio networks (CRNs).The new management approach to the mobility and the connection are designed to reduce the latency and loss of information during spectrum handoff. A list of channel safeguards are maintained in this effect, but the maintenance and updates are a challenge. In this article, the authors describe the reasons and mechanisms for spectrum handoff. Algorithms have been developed to illustrate this handoff mechanism and make the comparison between the different methods of spectrum handoff. The simulation results obtained confirm that the proposed method and the algorithms developed presents a better performance.

Rail-CR: Cognitive Radio for Enhanced Railway Communication

2010 ◽  
Author(s):  
Ashwin Amanna ◽  
Manik Gadhiok ◽  
Matthew J. Price ◽  
Jeffrey H. Reed ◽  
W. Pam Siriwongpairat ◽  
...  
Keyword(s):  
Decision Making ◽  
Cognitive Radio ◽  
Communication Systems ◽  
Situational Awareness ◽  
Cognitive Radios ◽  
Research Area ◽  
Vital Role ◽  
Wireless Communication Systems ◽  
Cognitive Engine ◽  
Train Control

Robust, reliable, and interoperable wireless communications play a vital role in the success of railroad operations. This paper describes an effort towards developing a railroad-specific “cognitive radio” (Rail-CR) that can meet the needs of future wireless communication systems for railways by making positive train control (PTC) communication more interoperable, robust, reliable, and spectrally efficient, and less costly to deploy and maintain. Cognitive radios are a cutting edge research area that combines artificial intelligence (AI) with Software Defined Radios (SDRs) with the goal of improving upon existing radio performance. SDRs are radios in which capabilities are flexible due to realizing some functionality in software as opposed to a purely hardware platform. By utilizing situational awareness from the radio in the form of observable parameters, often known as ‘meters’, a cognitive engine (CE) utilizes software-based decision-making algorithms to determine if a change in the radio parameters, commonly referred to as ‘knobs’, is required based on sets of predefined goals. Additionally, learning algorithms dovetail with the decision making to enable the system to track and utilize past decisions and observations.

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