Probability-Based Spectrum Sensing and Data Transmission Scheduling for Cognitive Radio Sensors

In a cognitive radio (CR) sensor network, more than one node may sense the same channel simultaneously. If the channel is available, those nodes may start to transmit data in this channel concurrently due to no coordination between them. This may lead to collision and data transmission failure. Thus...

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Bibliographic Details
Published in:IEEE sensors journal 2019-08, Vol.19 (16), p.7129-7140
Main Author: Li, Xiukui
Format: Article
Language:English
Subjects:
Channels
Cognitive radio
Data communication
Data transmission
Delays
Detection
Energy consumption
Energy transmission
False alarms
Networks
Nodes
Optimization
Probability
Scheduling
sensor communications
Sensors
Throughput
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Summary:In a cognitive radio (CR) sensor network, more than one node may sense the same channel simultaneously. If the channel is available, those nodes may start to transmit data in this channel concurrently due to no coordination between them. This may lead to collision and data transmission failure. Thus, energy consumed in channel sensing and data transmission will be wasted. Hence, to achieve the maximum throughput with limited energy, in this paper, we propose a probability-based spectrum sensing scheduling method to enable nodes to determine the optimal sensing time. Each node senses the channels with a probability which is determined by the numbers of channels and nodes. Also, a channel is sensed with a probability which is determined by the number of available channels. This is different from a conventional sensing scheduling with which a node senses channels per request without considering the number of channels and competitive counterparts. First, the probabilities of different cases of node sensing channels are investigated. Then the average throughput achievable, energy consumed, and packet delay are derived. Incorporating the probabilities of the channel being available, detection and false alarm, an optimization problem is formulated to find the optimal sensing time that maximizes the throughput-to-energy ratio.
ISSN:1530-437X
1558-1748
DOI:10.1109/JSEN.2019.2912429