Pricing for past channel state information in multi-channel cognitive radio networks

Kang, Sunjung; Joo, Changhee; Lee, Joohyun

doi:10.1109/wiopt.2016.7492942

Cited by 6 publications

(11 citation statements)

References 13 publications

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“…• During the transmission slot, each user i accesses channel k if x i (t) = k and gets reward X i,k (t). Then it updatesμ i,k (t) andτ i,k (t) according to (3) and (4).…”

Section: Uniform Sampling Algorithmmentioning

confidence: 99%

Low-Complexity Learning for Dynamic Spectrum Access in Multi-User Multi-Channel Networks

Kang

Joo

2018

IEEE INFOCOM 2018 - IEEE Conference on Computer Communications

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“…• During the transmission slot, each user i accesses channel k if x i (t) = k and gets reward X i,k (t). Then it updatesμ i,k (t) andτ i,k (t) according to (3) and (4).…”

Section: Uniform Sampling Algorithmmentioning

confidence: 99%

Low-Complexity Learning for Dynamic Spectrum Access in Multi-User Multi-Channel Networks

Kang

Joo

2018

IEEE INFOCOM 2018 - IEEE Conference on Computer Communications

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“…The fundamental idea underlying the medium access control protocol (MAC protocol) is to use the CSI by interrupting the detection of the carrier, whereby the interruption time is considered a reduced function of the state of the channel [32,33]. Only the sensor presenting the best transmission channel state is guaranteed by this system [34,35]. The concept of a system of MUD must take into account two key issues, namely, the delay and fairness.…”

Section: Multiuser Diversitymentioning

confidence: 99%

Performance Analysis of New Spectrum Sensing Scheme Using Multiantennas with Multiuser Diversity in Cognitive Radio Networks

Yawada

Dong

2018

Wireless Communications and Mobile Computing

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The spectrum detection (SD) is a critical step in cognitive radio (CR) to identify the available spectrum and avoid interference and harm to primary users (PUs). Nonetheless, the practical detection process is often compromised with problems of receptors uncertainty, shadows, and multipath fading. To overcome these problems, a cooperative SD scheme is proposed by exploiting the system multiantenna (MUA) with the multiuser diversity (MUD). The SD gives unlicensed secondary users (SUs) the opportunity to use the licensed spectral band until the PU makes its appearance. By allowing the cooperation between the SUs of the same spectral band, the time of the detection is reduced and increases the general dexterity, which is the strong point of cooperative SD. In this paper, we proposed an SD scheme using a new MUA design. The scheme proposed is a clear difference compared to other systems because in this design weight of quantification is multiplied by each receiving antenna. The scheme has the ability to detect low-level signals to facilitate sharing between the SU and PU system. The numerical results are made to prove the performance of the proposed system compared to the conventional system. The results obtained confirm the efficiency of the proposed scheme.

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“…The method of achieving the access of idle spectrum using clustering algorithm is an urgent problem that must be solved by the spectrum sharing technology of CR networks. 8 After analyzing the spectrum characteristics of distributed cognitive networks, a clustering algorithm was proposed. By maximizing the intra-cluster throughput and maintaining the stability of the cluster structure, the algorithm could realize the effective operation of cluster structure and show robustness to the behavior of PUs.…”

Section: Introductionmentioning

confidence: 99%

“…The clustering algorithm can reduce the competition for channels among the nodes, improve channel utilization, and thus stabilize the network. The method of achieving the access of idle spectrum using clustering algorithm is an urgent problem that must be solved by the spectrum sharing technology of CR networks 8 …”

Section: Introductionmentioning

confidence: 99%

Combination weighted clustering algorithms in cognitive radio networks

Wang

et al. 2019

Concurrency and Computation

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The topology of the distributed cognitive radio network is volatile as influenced by the behavior of primary users, and this condition leads to the large communication overhead and low utilization of spectrum resources. A combination weighted clustering algorithm is proposed in the study to reduce the communication overhead of the distributed cognitive network and maintain the stability of the network structure. First, a clustering algorithm considering the available channel, geographic location, and experienced data (used for collecting the behavior of secondary users (SUs) in the network and the evaluation on it) of SUs is put forward through analyzing the characteristics of the idle channels in cognitive network. Three factors, namely, average channel capacity, stability, and channel quality, are converted into quantifiable values. The cluster head is determined on the basis of the three factors. Then, the cluster members and gateway nodes are determined using the weighting formula and the location information of the cluster head. Results show that the proposed clustering algorithm can generate 15% more clusters than other algorithms and reduce 40% of network communication overhead when the transmission distance between cognitive users and the channel number change. Thus, the stability of the cluster structure is maintained and the communication overhead is decreased. This study provides references for the construction of the stable distributed cognitive network.

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Pricing for past channel state information in multi-channel cognitive radio networks

Cited by 6 publications

References 13 publications

Low-Complexity Learning for Dynamic Spectrum Access in Multi-User Multi-Channel Networks

Low-Complexity Learning for Dynamic Spectrum Access in Multi-User Multi-Channel Networks

Performance Analysis of New Spectrum Sensing Scheme Using Multiantennas with Multiuser Diversity in Cognitive Radio Networks

Combination weighted clustering algorithms in cognitive radio networks

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