Efficient proactive channel switching in cognitive radio networks

Shil, Sukhen; Chauhan, Prakash; Deka, Sanjib Kumar; Sarma, Nityananda

doi:10.1109/infocomtech.2017.8340607

Cited by 5 publications

(4 citation statements)

References 7 publications

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“…This list is defined in advance so that users can switch to an idle channel to avoid having an interference effect on incumbent users. As for the channel selection method for generating such a backup channel list, occupancy probability and state transition probability based channel selection methods have been studied based on history data representing spectrum statistics [ 7 , 8 , 9 ].…”

Section: Learning Enginementioning

confidence: 99%

“…Therefore, it should be dynamically optimized according to time varying parameters such as channel state information. To this end, in previous studies [ 7 , 8 , 9 ], the usage patterns of incumbent users were learned using the occupancy probability and state transition probability based on history data representing spectrum statistics to infer idle channels in a dynamic spectrum environment. In addition, in [ 10 , 11 , 12 , 13 ], in order to enable continuous communication without interference to incumbent users, a reactive handoff method based on spectrum sensing and a proactive handoff method using a backup channel list are defined.…”

Section: Introductionmentioning

confidence: 99%

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Intelligent Dynamic Spectrum Resource Management Based on Sensing Data in Space-Time and Frequency Domain

Yun

Lee

2021

Sensors

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Edge computing offers a promising paradigm for implementing the industrial Internet of things (IIoT) by offloading intensive computing tasks from resource constrained machine type devices to powerful edge servers. However, efficient spectrum resource management is required to meet the quality of service requirements of various applications, taking into account the limited spectrum resources, batteries, and the characteristics of available spectrum fluctuations. Therefore, this study proposes intelligent dynamic spectrum resource management consisting of learning engines that select optimal backup channels based on history data, reasoning engines that infer idle channels based on backup channel lists, and transmission parameter optimization engines based genetic algorithm using interference analysis in time, space and frequency domains. The performance of the proposed intelligent dynamic spectrum resource management was evaluated in terms of the spectrum efficiency, number of spectrum handoff, latency, energy consumption, and link maintenance probability according to the backup channel selection technique and the number of IoT devices and the use of transmission parameters optimized for each traffic environment. The results demonstrate that the proposed method is superior to existing spectrum resource management functions.

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Section: Learning Enginementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Intelligent Dynamic Spectrum Resource Management Based on Sensing Data in Space-Time and Frequency Domain

Yun

Lee

2021

Sensors

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“…The authors of [16,17,18,19] considered the impact of prediction accuracy; however, benefits of prediction is limited to improve the spectrum sensing time, ignoring the spectrum handoff management process. The authors of [20] proposed a proactive channel switching handoff mechanism to minimize the number of handoff. A list of candidate target channel based on probability of idleness is maintained and sensed during handoff instead of sensing all channels.…”

Section: Related Workmentioning

confidence: 99%

“…Imperfect channel prediction mechanism has been considered for improving only the spectrum sensing phase in a cognitive cycle [16,17,18,19]. In [20,21], prediction accuracy for improving the spectrum sensing delay as a part of spectrum handoff has been considered; however, this is applicable only in a centralized CRN. The work in this paper differs from the existing work in that the imperfect channel prediction method is being considered for analyzing the spectrum handoff in a multi-user cognitive radio ad hoc network (CRAHN).…”

Section: Introductionmentioning

confidence: 99%

Spectrum Handoff based on Imperfect Channel State Prediction Probabilities with Collision Reduction in Cognitive Radio Ad Hoc Networks

Shakeel

Hussain

Iqbal

et al. 2019

Sensors

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The spectrum handoff is highly critical as well as challenging in a cognitive radio ad hoc network (CRAHN) due to lack of coordination among secondary users (SUs), which leads to collisions among the SUs and consequently affects the performance of the network in terms of spectrum utilization and throughput. The target channel selection mechanism as part of handoff process can play an enormously significant role in minimizing the collisions among the SUs and improving the performance of a cognitive radio network (CRN). In this paper, an enhanced target channel selection scheme based on imperfect channel state prediction is proposed for the spectrum handoff among the SUs in a CRAHN. The proposed scheme includes an improved frame structure that increases coordination among the SUs in the ad hoc environment and helps in organizing the SUs according to the shortest job first principle during channel access. Unlike the existing prediction-based spectrum handoff techniques, the proposed scheme takes into account the accuracy of channel state prediction; the SUs affected due to false prediction are compensated by allowing them to contend for channel access within the same transmission cycle and thus enabling them to achieve higher throughput. The proposed scheme has been compared with the contemporary spectrum handoff schemes and the results have demonstrated substantial improvement in throughput and extended data delivery time by virtue of the reduced number of collisions.

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A Survey on Proactive and Reactive Channel Switching Techniques in Cognitive Radios

Srivastava

Prakash

Tripathi

2020

Lecture Notes in Electrical Engineering

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Efficient proactive channel switching in cognitive radio networks

Cited by 5 publications

References 7 publications

Intelligent Dynamic Spectrum Resource Management Based on Sensing Data in Space-Time and Frequency Domain

Intelligent Dynamic Spectrum Resource Management Based on Sensing Data in Space-Time and Frequency Domain

Spectrum Handoff based on Imperfect Channel State Prediction Probabilities with Collision Reduction in Cognitive Radio Ad Hoc Networks

A Survey on Proactive and Reactive Channel Switching Techniques in Cognitive Radios

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