Joint Sensing Period and Transmission Time Optimization for Energy-Constrained Cognitive Radios

Xu, You; Sun, Yin; Li, Yunzhou; Zhao, Yifei; Zou, Hongxing

doi:10.1155/2010/818964

Cited by 14 publications

(14 citation statements)

References 11 publications

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“…This sub-problem is very similar to our previous work [11], in which r 0,i and r 1,i are continuous variables. In [11], we have proved and obtained the relationship between r 0,i (r 1,i ) and T, which can be illustrated in Figure 3. 1) r 0,i : when period T is small, r 0,i = 1, which means SU can access all the "OFF slots" and its interference to PU i will not exceed threshold C i . When T > T i c , the optimal r 0,i will decrease.…”

Section: Sa Strategymentioning

confidence: 57%

“…If sensing result is "ON", the expected time overlap j 1 (m) is Therefore, similar to [11], we can obtain the following lemma.…”

Section: Sa Strategymentioning

confidence: 88%

“…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%

“…In our previous study [11], we investigate the simplest single-channel continuous-time model and proposed two access policies under interference constraint and energy consumption constraint. Finally, the optimal sensing period and transmission time are derived.…”

Section: Introductionmentioning

confidence: 99%

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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.

show abstract

Section: Sa Strategymentioning

confidence: 57%

“…If sensing result is "ON", the expected time overlap j 1 (m) is Therefore, similar to [11], we can obtain the following lemma.…”

Section: Sa Strategymentioning

confidence: 88%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Selective sensing and transmission for multi-channel cognitive radio networks

Zhao

et al. 2011

J Wireless Com Network

Self Cite

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show abstract

“…Let z k ∈ {0, 1} denote the real state of the PU within the sensing duration of any specific frame k at the SU. On the above assumptions, it is clear that there is a high probability that z k remains unchanged within a whole frame and that z k is closely related to the past state z k−1 with respect to the preceding frame k − 1 for k > 1 [18,20,26].…”

Section: System Modelmentioning

confidence: 99%

An inter-frame dynamic double-threshold energy detection for spectrum sensing in cognitive radios

et al. 2017

J Wireless Com Network

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This paper proposes an inter-frame dynamic double threshold (IF-DDT) spectrum sensing algorithm in order to improve the sensing performance based on energy detection (ED) in cognitive radios (CRs). Based on both the activity model of the primary user (PU) and the sensing mechanism of the secondary user (SU), the proposed algorithm exploits the relationship between two adjacent sensing frames and designs dynamic double thresholds for each sensing frame to enhance spectrum sensing performance when the received energy cannot give a reliable local decision. The detection probability and false alarm probability of the proposed sensing scheme are analyzed, and an algorithm for searching the optimal dynamic double thresholds is derived with very low complexity according to the Neyman-Pearson (NP) test criterion. Theoretical analysis and simulation results show that the proposed algorithm outperforms the ED algorithm.

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A prediction and scheduling framework in centralized cognitive radio network for energy efficient non‐real time communication

Manna

Misra

2018

Int J Communication

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Advent of Internet of Things led to an exponential rise in battery-operated sensors transmitting small non-real time (NRT) data regularly. To this end, this work proposes a framework for centralized cognitive radio network (CRN) that facilitates better spectrum utilization and low-cost opportunistic NRT data transfer with high energy efficiency. The novelty of this framework is to incorporate Hidden Markov Model-based prediction within the traditional cognitive radio sensing-transmission cycle. To minimize the prediction time, we design a Hardware-based Hidden Markov Model engine (H2M2) to be used by the cognitive base station (CBS). CBS exploits the H2M2 engine over high primary user (PU) activity channels to minimize the collisions between PUs and NRT secondary users, thereby reducing the SU energy consumption. However, this is at the cost of reduced throughput. Taking this into account, we propose an Intersensing-Prediction Time Optimization algorithm that identifies the predictable PU activity channels and maximizes the throughput within a PU interference threshold. Furthermore, to minimize the total battery consumption of all the SUs within CRN, a Battery Consumption Minimizing Scheduler is designed at the CBS that efficiently allocates the predictable PU channels to the NRT SUs. By exploiting the unutilized high PU activity channels, the proposed Centralized Scheduling, Sensing and Prediction (CSSP) framework improves the spectral efficiency of the CRN. Exhaustive performance studies show that CSSP outperforms traditional nonpredictive sensing techniques in terms of energy efficiency and interference management. Finally, through a proof of concept, we validate the ability of CSSP framework in enabling NRT communication.

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Joint Sensing Period and Transmission Time Optimization for Energy-Constrained Cognitive Radios

Cited by 14 publications

References 11 publications

Selective sensing and transmission for multi-channel cognitive radio networks

Selective sensing and transmission for multi-channel cognitive radio networks

An inter-frame dynamic double-threshold energy detection for spectrum sensing in cognitive radios

A prediction and scheduling framework in centralized cognitive radio network for energy efficient non‐real time communication

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