Adaptive Retry Time Based MAC Layer Spectrum Sensing for Cognitive Radio 
 Networks

Varalakshm, S.; Shanmugave, S.

doi:10.3923/ajsr.2014.581.590

Cited by 3 publications

(2 citation statements)

References 2 publications

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“…Hence, cognitive radio method plays a vital role in serving nodes to create networks based on their capability of vibrant access. Multi-channel Medium Access Control (MAC) protocol permit varied users to transmit via diverse pathways concurrently without interrupting others [7]. The delay and throughput is enhanced in simultaneous transmission mode by enabling varied users to transmit the data.…”

Section: Introductionmentioning

confidence: 99%

Cross Layer QoS Aware Scheduling based on Loss-Based Proportional Fairness with Multihop CRN

Saravanan¹,

Tamilselvan²,

Rajendran³

2022

Computer Systems Science and Engineering

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As huge users are involved, there is a difficulty in spectrum allocation and scheduling in Cognitive Radio Networks (CRNs). Collision increases when there is no allocation of spectrum and these results in huge drop rate and network performance degradation. To solve these problems and allocate appropriate spectrum, a novel method is introduced termed as Quality of Service (QoS) Improvement Proper Scheduling (QIPS). The major contribution of the work is to design a new cross layer QoS Aware Scheduling based on Loss-based Proportional Fairness with Multihop (QoSAS-LBPFM). In Medium Access Control (MAC) multi-channel network environment mobile nodes practice concurrent broadcast between several channels. Acquiring the advantage of introduced cross layer design, the real-time channel conditions offered by Cognitive Radio (CR) function allows adaptive sub channel choice for every broadcast. To optimize the resources of network, the LBPFM adaptively plans the radio resources for allocating to diverse services without lessening the quality of service. Results obtained from simulation proved that QoSAS-LBPFM provides enhanced QoS guaranteed performance against other existing QIPS algorithm.

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Section: Introductionmentioning

confidence: 99%

Cross Layer QoS Aware Scheduling based on Loss-Based Proportional Fairness with Multihop CRN

Saravanan¹,

Tamilselvan²,

Rajendran³

2022

Computer Systems Science and Engineering

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“…That is, sensing at physical layer and MAC layer. Sensing a spectrum at the physical layer makes adaptation of modulation schemes and parameters for detecting and measuring the signals transmitted by PUs from different channel while MAC layer makes a decision as when SUs have to sense which channels [2]. MAC layer is responsible for searching of channels, transmissions, and scheduling time slots for sensing.…”

Section: Introductionmentioning

confidence: 99%

Channel Selection Algorithm Optimized for Improved Performance in Cognitive Radio Networks

Tlouyamma

Velempini

2021

Wireless Pers Commun

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A major concern in the recent past was the traditional static spectrum allocation which gave rise to spectrum underutilization and scarcity in wireless networks. In an attempt to solve this challenge, cognitive radios technology was proposed. It allows a spectrum to be accessed dynamically by Cognitive radio users or secondary users (SU). Dynamic access can efficiently be achieved by making necessary adjustment to some Medium access control (MAC) layer functionalities such as sensing and channel allocation. MAC protocols play a central role in scheduling sensing periods and channel allocation which ensure that the interference is reduced to a tolerable level. In order to improve the accuracy of sensing algorithm, necessary adjustments should be made at MAC layer. Sensing delays and errors are major challenges in the design of a more accurate spectrum sensing algorithm. This study focuses on designing a channel selection algorithm to efficiently utilize the spectrum. Channels are ordered and grouped to allow faster discovery of channel access opportunities. The ordering is based on descending order of channel’s idling probabilities. Grouping of channels ensured that channels are sensed simultaneously. These two techniques greatly reduce delays and maximized throughput of SU. Hence, Extended Generalized Predictive Channel Selection Algorithm, a proposed scheme has significantly performed better than its counterpart (Generalized Predictive Channel Selection Algorithm). Matlab simulation tool was used to simulate and plot the results of the proposed channel selection algorithm.

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Delay minimization in spectrum sensing in cognitive radio networks

2017

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Adaptive Retry Time Based MAC Layer Spectrum Sensing for Cognitive Radio Networks

Cited by 3 publications

References 2 publications

Cross Layer QoS Aware Scheduling based on Loss-Based Proportional Fairness with Multihop CRN

Cross Layer QoS Aware Scheduling based on Loss-Based Proportional Fairness with Multihop CRN

Channel Selection Algorithm Optimized for Improved Performance in Cognitive Radio Networks

Delay minimization in spectrum sensing in cognitive radio networks

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