Energy detector based TOA estimation for MMW systems using machine learning

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• 作者：Xiaolin Liang ; Hao Zhang ; Tingting Lu ; T. Aaron Gulliver
• 关键词：Energy detector ; Neural network ; TOA estimation ; IEEE 802.15.3c
• 刊名：Telecommunication Systems
• 出版年：2017
• 出版时间：February 2017
• 年：2017
• 卷：64
• 期：2
• 页码：417-427
• 全文大小：
• 刊物类别：Business and Economics
• 刊物主题：IT in Business; Computer Communication Networks; Artificial Intelligence (incl. Robotics); Probability Theory and Stochastic Processes;
• 出版者：Springer US
• ISSN：1572-9451
• 卷排序：64

文摘

60 GHz millimeter wave signals can provide precise time and multipath resolution and so have great potential for accurate time of arrival (TOA) and range estimation. To improve TOA estimation, a new energy detector based threshold selection algorithm which employs a neural network is proposed. The minimum slope, kurtosis, and skewness of the received energy block values are used to determine the normalized thresholds for different signal-to-noise ratios (SNRs). The effects of the channel and integration period are evaluated. Performance results are presented which show that the proposed approach provides better precision and is more robust than other solutions over a wide range of SNRs for the CM1.1 and CM2.1 channel models in the IEEE 802.15.3c standard.