Primary User Channel State Tracking Based on K-Nearest Neighbors Classifier and Kalman Filter
Ahmed Mohammed Mikaeil, Bin Guo, Xuemei Bai, Zhijun Wang
Ahmed Mohammed Mikaeil
Available Online November 2015.
- https://doi.org/10.2991/itms-15.2015.167How to use a DOI?
- Channel state tracking; Channel state prediction; K-nearest neighbor classifier; Kalman Filter; Spectrum sensing time slot
- Spectrum sensing use the current primary user (PU) received signal to detect PU channel state at the current time, however predicting PU channel state in the near future using previous detected channel state information can secure an efficient utilization of the unoccupied spectrum, also can help in preventing the harmful interference with the PU and solving the problem of the latency between spectrum sensing and data transmission which caused by the hardware implementation. In this paper, an algorithm Based on K-Nearest Neighbor (KNN) Classifier and Kalman Filter is proposed to detect and predict the future of the primary user channel state “the spectrum holes”. The new algorithm based on training KNN classifier over a set containing some frame time slots energy test statistics along with their corresponding decisions about the presence or absence of the primary user transmission, so as to predict the decisions for new unclassified time slot, then utilize the Kalman filter to track the PU the primary user channel state by predicting the next expected spectrum holes based on the previously detected hole. Experimental results show that the proposed approach is efficient and easy to implement for detecting and tracking “predicting” the PU channel state.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Ahmed Mohammed Mikaeil AU - Bin Guo AU - Xuemei Bai AU - Zhijun Wang PY - 2015/11 DA - 2015/11 TI - Primary User Channel State Tracking Based on K-Nearest Neighbors Classifier and Kalman Filter BT - 2015 International Conference on Industrial Technology and Management Science PB - Atlantis Press SN - 2352-538X UR - https://doi.org/10.2991/itms-15.2015.167 DO - https://doi.org/10.2991/itms-15.2015.167 ID - Mikaeil2015/11 ER -