Joint sensing period optimization and transmission time allocation for cognitive radio networks

Xu, You; Li, Yunzhou; Zou, Hongxing; Yang, Xiaojun

doi:10.1109/wcsp.2009.5371713

Cited by 15 publications

(8 citation statements)

References 9 publications

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“…For the PU activities in these channels, we employ the slotted exponential ON/OFF model. Although this model suffers from ignoring the diverse characteristics of existing primary networks , it is widely accepted as the most useful model and used in the literature because the research on developing better traffic models for primary behavior still lacks feasibility in practical situations. In the exponential ON/OFF model, each channel has two possible states: ON and OFF.…”

Section: System Model and Basic Conceptsmentioning

confidence: 99%

An efficient and adaptive channel handover procedure for cognitive radio networks

Kahraman

Buzluca

2013

Wirel. Commun. Mob. Comput.

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In designing cognitive radio systems, one of the most critical issues is handling the channel handover process (CHP). The CHP consists of spectrum sensing, spectrum decision, negotiation on the common control channel, and adjustment of frequency and modulation settings, and such, it can be a time-consuming process. Consequently, initiating the CHP after each detected user activity (UA) can decrease the aggregate spectrum utilization. To alleviate this problem, we introduce a novel handover strategy to find the optimal trade-off between the durations of the CHP and UAs. With the use this model, secondary users (SUs) track only local information on their current data channel to make the decision to initiate the CHP or to wait for the termination of the ongoing UA. The system adapts to the dynamic conditions of the data channels and reduces the frequency of handovers to increase throughput and decrease access delay. We give analytical utilization bounds for SUs and also compare the performance of our model to those of other channel handover strategies by using extensive simulations. Our results for channels with heterogeneous loads and dynamic environments show that this model can clearly decrease the frequency of handover and consequently increase the aggregate SU utilization.

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Section: System Model and Basic Conceptsmentioning

confidence: 99%

An efficient and adaptive channel handover procedure for cognitive radio networks

Kahraman

Buzluca

2013

Wirel. Commun. Mob. Comput.

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“…Particularly, the interference between PU and SU is modeled by the average temporal overlap, namely, interference time divided by total time, which is also adopted in some related publications [7,10]. Mathematically, the interference I i between SU and PU i is…”

Section: Problem Formulationmentioning

confidence: 99%

“…Thus, suitable sensing period should also be considered. In [9][10][11], the optimal sensing period is derived for the simplest single-channel model. In [12], a theoretical framework is proposed for jointly optimizing sensing and transmission time for each channel.…”

Section: Introductionmentioning

confidence: 99%

Selective sensing and transmission for multi-channel cognitive radio networks

Zhao

et al. 2011

J Wireless Com Network

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In this article, we consider a continuous time Markov chain (CTMC) modeled multi-channel CR network, where there are multiple independent primary users and one slotted secondary user (SU) who can access multiple channels simultaneously. To maximize SU's temporal channel utilization while limiting its interference to PUs, a selective sensing and selective access (SS-SA) strategy is proposed. With SS strategy, each channel is sensed almost periodically with different periods according to parameter T c , which reflects the maximal period that each channel should be probed. The effect of sensing period is also considered. When the sensing period is suitable, the SA strategy can be regarded as greedy access strategy. Numerical simulations illustrate that T c is a valid measurement to indicate how often each channel should be sensed, and with SS-SA strategy, SU can effectively utilize the channels and consume less energy and time for sensing than adopting reference strategies.

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“…Several optimal sensing time algorithms were presented when the frame duration is constant [3] [4] [5] [6]. The schemes on the joint optimization of sensing time and transmission time have been proposed [7] [8]. The maximal ratio between the transmission time and the entire frame duration was researched [7].…”

Section: Introductionmentioning

confidence: 99%

“…The maximal ratio between the transmission time and the entire frame duration was researched [7]. But, it could not ensure the maximized throughput and the constraint of spectrum sensing quality wasn't considered [8]. All the papers above except [6] [7] assumed that the PU is either active or inactive for the whole secondary frame period.…”

Section: Introductionmentioning

confidence: 99%

Joint design of spectrum sensing and data transmission for cognitive radio networks

Zhao

Gao

Xiao

et al. 2013

2013 6th International Conference on Biomedical Engineering and Informatics

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In cognitive radio networks, it is quite significant to integrate the design of spectrum sensing and data transmission together in the asynchronous scenario where the primary network is non-slotted. Most existing works just focused on either the optimization of spectrum sensing or the maximal throughput subject to the probability of detection or some interference level constraints. In this paper, we propose to jointly optimize the durations for spectrum sensing and data transmission by maximizing the throughput while keeping the probabilities of interference to a primary user (PU) and false alarm under certain thresholds. The optimal sensing and transmission durations are obtained by the differential evolution algorithm. Numerical results show that the optimal time durations for sensing and transmission vary under different thresholds for interference probability to the PU and different primary activity distributions.

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Joint sensing period optimization and transmission time allocation for cognitive radio networks

Cited by 15 publications

References 9 publications

An efficient and adaptive channel handover procedure for cognitive radio networks

An efficient and adaptive channel handover procedure for cognitive radio networks

Selective sensing and transmission for multi-channel cognitive radio networks

Joint design of spectrum sensing and data transmission for cognitive radio networks

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