Minimizing The Number of Channel Switches of Mobile Users in Cognitive Radio Ad-Hoc Networks
Minimizing The Number of Channel Switches of Mobile Users in Cognitive Radio Ad-Hoc Networks
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DOI：
10.3390/jsan9020023
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发表时间：
2020-05
期刊：
影响因子：
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通讯作者：
Rajorshi Biswas;Jie Wu
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文献类型：
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作者：
Rajorshi Biswas;Jie Wu
认知无线电（CR）技术可以在主用户（PU）不使用无线频谱的情况下，利用空闲的频谱，由移动的用户组成分布式多跳网络。不同位置的信道质量不同。当用户从一个地方移动到另一个地方时，需要切换信道以保持不同应用所需的服务质量（QoS）。信道的QoS取决于使用量。用户可以在其移动期间选择满足QoS要求的信道。在本文中，我们研究了用户的移动模式，预测他们的下一个位置和概率移动到那里的基础上，它的历史。我们从每个用户的位置历史记录中提取移动模式，并将最近的轨迹与模式进行匹配，以找到未来的位置。我们使用Wi-Fi接入点位置数据和自由空间路径损耗公式构建频谱数据库。我们提出了一种基于机器学习的机制来预测频谱数据库中某些缺失位置的频谱状态。我们制定了一个问题，以选择当前的通道，以尽量减少在一定数量的用户的下一个动作的通道切换的总数。我们进行了广泛的模拟结合真实的和合成数据集，以支持我们的模型。
Cognitive radio (CR) technology is envisioned to use wireless spectrum opportunistically when the primary user (PU) is not using it. In cognitive radio ad-hoc networks (CRAHNs), the mobile users form a distributed multi-hop network using the unused spectrum. The qualities of the channels are different in different locations. When a user moves from one place to another, it needs to switch the channel to maintain the quality-of-service (QoS) required by different applications. The QoS of a channel depends on the amount of usage. A user can select the channels that meet the QoS requirement during its movement. In this paper, we study the mobility patterns of users, predict their next locations and probabilities to move there based on its history. We extract the mobility patterns from each user’s location history and match the recent trajectory with the patterns to find future locations. We construct a spectrum database using Wi-Fi access point location data and the free space path loss formula. We propose a machine learning-based mechanism to predict spectrum status of some missing locations in the spectrum database. We formulate a problem to select the current channel in order to minimize the total number of channel switches during a certain number of next moves of a user. We conduct an extensive simulation combining real and synthetic datasets to support our model.