Multi slot-throughput tradeoff in an improved energy detector based faded cognitive radio network

Bhowmick, Abhijit; Yadav, Kuldeep; Roy, Sanjay Dhar; Kundu, Sumit

doi:10.1007/s11276-017-1487-0

Cited by 4 publications

(13 citation statements)

References 19 publications

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“…Moreover, Yadav et al 20 have presented that for AP ¼ 5, the clusterbased network shows a performance improvement of $24.48%, over the non-clustered network. From the figure, it can be observed that the highest detection probability has been achieved for the multislot with dynamic clustering (8,4,3).…”

Section: Resultsmentioning

confidence: 93%

“…In the first iteration, eight CRs have reported to AP-1, five CRs to AP-2, and two CRs to AP-3. Therefore, the first cluster has been formed as (8,5,2). Likewise, in the second iteration, five CRs have reported to AP-1, six CRs to AP-2, and four CRs to AP-3, which results in the formation of a second cluster as (5,6,4).…”

Section: Resultsmentioning

confidence: 99%

“…The traditional spectrum sensing system, in which there is no slotting into sensing time, that is, M ¼ 1, the CR user detects the spectrum during the time t, and T À t ð Þtime is used for the transmission. Let us assume that the signal received at CR is given as 7,8…”

Section: Ss Without Slotting Of Sensing Timementioning

confidence: 99%

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Multi‐slot‐based dynamic clustering in cognitive radio network

Singh

Ranjan

2022

Int J Communication

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Summary The current work investigates the performance of multi‐slot‐based dynamic clustering in a cognitive radio network. For the spectrum sensing, every secondary user utilizes the multi‐slots of sensing time frame and combines the decisions of every multi‐slot, using the OR logic scheme, to get the sub‐local decision. The channel state information of the connections among the secondary users and access points present on the network's access points has been used to cluster the secondary users. To form the clusters, the secondary users with higher quality reporting channels for a specific access point have been chosen. Therefore, the clusters formed, in each iteration, with their respective access points, are dynamic in nature. Every secondary user in a cluster detects the availability of a primary user, makes a decision, and forwards it to its corresponding access point, via the reporting‐channel. Each access point employs the OR rule to merge the decisions of secondary users in the respective clusters, to make a local decision. Finally, the local decisions of all access points have been directed to fusion center, making the final decision about primary user's accessibility. The performance of multi‐slot‐based dynamic clustering has been studied relative to the different parameters, like detection probability, number of clusters, number of multi‐slots, secondary users, and access points. Further, the performance evaluation and comparisons of a multi‐slot‐based cognitive radio network with a single slot, cluster‐based cognitive radio network with non‐clustered, and multi‐slot‐based dynamic cluster with individual multi‐slots as well as dynamic clustering, have been done.

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Section: Resultsmentioning

confidence: 93%

Section: Resultsmentioning

confidence: 99%

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Multi‐slot‐based dynamic clustering in cognitive radio network

Singh

Ranjan

2022

Int J Communication

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“…In this subsection, we compare our proposed two-stage based CRN-CSS with other existing works for an improved energy detector [9], enhanced ED [13], energy with correlation detection [14], majority rule [19], ED with fusion rules [30], selective cluster [31], cluster-based CSS [35], and two-tier spectrum allocation method [38] based on significant metrics such as sensing efficiency, spectrum utilisation efficiency, energy efficiency, and probability of detection. Table 5 outlines the problems existed in previous works, which lead to performance degradation.…”

Section: Comparative Analysismentioning

confidence: 99%

“…For each group in CRN-CSS, a time division multiple access-based timeslot is assigned for spectrum sensing. Multiple mini-slots sensing time is assigned for spectrum sensing by using the improved energy detector method [9].…”

Section: Introductionmentioning

confidence: 99%

Neuro‐fuzzy based two‐stage spectrum allocation scheme to ensure spectrum efficiency in CRN–CSS assisted by spectrum agent

Raman

Venkatesan

2019

IET Circuits, Devices & Systems

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Cognitive radio network (CRN) is a promising technology to solve increasing spectrum scarcity problems in recent times. This study resolves the problem of cooperative spectrum sensing (CSS) and spectrum allocation in CRN-CSS in order to improve spectrum efficiency. A two-stage spectrum allocation scheme is initiated by grouping secondary users (SUs) with the knowledge of one-hop neighbor approach, which leads to minimised grouping time. These grouped SUs perform spectrum sensing in cooperative manner. Optimal channel assignment (OCA) process assigns channels to each SU in a group in a dynamic manner. Upon the assigned channel, SU performs spectrum sensing with the aid of the enhanced threshold energy detection (ETED) method. Finally, the available spectrum is allocated to SUs by a novel two-stage allocation approach. In this approach, SUs, which provide accurate spectrum sensing result, are given high priority for spectrum allocation. These SUs are detected through global decision making at the fusion centre (FC). A novel mining tree scheme with a spectrum agent is adopted for decision making at the FC. The neuro-fuzzy based spectrum allocation scheme in two-stage allocation supports effective spectrum allocation. An extensive experimental result exhibits promising improvements than in the previous approaches.

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Enhancing Throughput Over Varied Fading Channels for Cluster-Based Cooperative Spectrum Sensing in Cognitive Wireless Networks

Sharma

2023

Int J Wireless Inf Networks

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Multi slot-throughput tradeoff in an improved energy detector based faded cognitive radio network

Cited by 4 publications

References 19 publications

Multi‐slot‐based dynamic clustering in cognitive radio network

Multi‐slot‐based dynamic clustering in cognitive radio network

Neuro‐fuzzy based two‐stage spectrum allocation scheme to ensure spectrum efficiency in CRN–CSS assisted by spectrum agent

Enhancing Throughput Over Varied Fading Channels for Cluster-Based Cooperative Spectrum Sensing in Cognitive Wireless Networks

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