Predictive Channel Selection for over-the-Air Video Transmission Using Software-Defined Radio Platforms
This paper demonstrates a predictive channel selection method by implementing it in software-defined radio (SDR) platforms and measuring the performance using over-the-air video transmissions. The method uses both long term and short term history informat
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stract. This paper demonstrates a predictive channel selection method by implementing it in software-defined radio (SDR) platforms and measuring the performance using over-the-air video transmissions. The method uses both long term and short term history information in selecting the best channel for data transmission. Controlled interference is generated in the used channels and the proposed method is compared to reference methods. The achieved results show that the predictive method is a practical one, able to increase the throughput and reduce number of collisions and channel switches by using history information intelligently. Keywords: Cognitive radio
Spectrum databases Dynamic spectrum access
1 Introduction Cognitive radio (CR) techniques have been studied intensively for over a decade, focusing mainly on dynamic spectrum access oriented operation. Numerous techniques have been developed and analyzed, including spectrum sensing, power and frequency allocations, beacon signaling, and spectrum databases. Only a subset of the proposed techniques have been implemented and tested in real systems to see their practicality. This paper focuses on channel selection problem in a changing radio environment and demonstration of the proposed method in a practical system. Importance of history information and knowledge on primary traffic patterns in channel selection was shortly discussed already in [1]. Later, the problem has been studied intensively and prediction methods for both stochastic and deterministic traffic have been developed [2–8]. For example, a deterministic long-term component can be seen in several bands such as cellular mobile communication systems due to daily rhythm of the users [3]. Traffic pattern estimation method for exponential traffic has been proposed in [4]. A more general method able to classify traffic patterns and select the prediction method based on this information is proposed in [5]. Switching delay has been included in the channel selection to decide whether to switch a channel or not based on channel prediction and switching overhead in [6]. The method is developed further in [7] where an adaptive sensing policy is developed to detect the primary user appearance as fast as possible. Sequential channel sensing policy is studied also in [8]. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2016 D. Noguet et al. (Eds.): CROWNCOM 2016, LNICST 172, pp. 569–579, 2016. DOI: 10.1007/978-3-319-40352-6_47
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The sensing procedure and channel selection can be made faster by reducing the number of channels to sense in the first place. Both short term and long term information can be used to guide the process. A channel selection method that was described in [9, 10] uses long term information on the use of primary channels to select the most promising ones to be sensed and exploited by cognitive radios at the requesting time. These channels are investigated in more detail over short term to find the best channels for data transmission.
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