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Deep-learning based Cooperative Spectrum Prediction for Cognitive Networks

机译:基于深度学习的认知网络的合作频谱预测

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The concept of dynamic spectrum access under the basic principles of Cognitive Radio (CR) networks is considered to alleviate the current inefficient use of radio spectrum. Relying on the presumed knowledge of the radio environment, CR opportunistically allows secondary users to access licensed spectrum bands when not in use by their respective owners (primary users), in a non-interference manner. As identifying and exploiting the available spectrums is time and energy consuming, different spectrum prediction methodologies are used in order to provide meaningful data about current and future spectrum usage in specific time and location. However, local spectrum prediction done from a single SU`s perspective can be unreliable and prone to error under harsh channel conditions. Where applicable, cooperative spectrum prediction in multi-user environment has the potential to overcome the limitations of local spectrum prediction accuracy. In this paper, cooperative spectrum prediction with neural network predictors is investigated. Considering heterogeneous CR network, soft cooperative fusion is implemented to exploit the spatial dependency of spectrum measurement data and provide occupancy predictions for an SU in near proximity range without it conducting spectrum sensing.
机译:在认知无线电(CR)网络的基本原理下动态频谱访问的概念被认为是减轻无线电频谱的电流低效使用。依靠被广播环境的假定知识,CR机会主动允许辅助用户在不被其各自的所有者(主要用户)使用时访问许可的频谱频带,以非干扰方式。作为识别和利用可用频谱是时间和能量消耗,使用不同的频谱预测方法,以便在特定时间和位置提供有关当前和未来频谱使用的有意义的数据。然而,从单个SU`S的角度完成的局部频谱预测可能是不可靠的并且在恶劣的信道条件下容易出现错误。如果适用的,在多用户环境中的协同频谱预测有可能克服本地频谱预测精度的局限性。本文研究了与神经网络预测器的协作频谱预测。考虑异构CR网络,实施软协作融合以利用频谱测量数据的空间依赖性,并在没有传导光谱传感的情况下提供近邻范围内的SU的占用预测。

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