On cyclostationary analysis of WiFi signals for direction estimation
Changlai Du, Huacheng Zeng, Wenjing Lou, Y. Thomas Hou
Abstract
Changlai Du, Huacheng Zeng, Wenjing Lou, Y. Thomas Hou
Abstract
Cyclostationary analysis is a powerful tool to study the Signal-Selective Direction Estimation (SSDE) problem as different types of wireless signals have different cyclostationary patterns. Generally speaking, each type of wireless signal has unique cyclic frequencies with the frequency-selective property, which distinguishes itself from other types of signals. The cyclostationary property of a signal may be induced by its modulation method, its carrier frequency, and/or its frame structure. In this paper, we study the cyclostationary property of IEEE 802.11 (WiFi) signals induced by their underlying OFDM frame structure, which includes pilots, cyclic prefix (CP), and preambles. We first analyze the pilot-induced, CP-induced, and preamble-induced cyclostationary properties of WiFi signals, respectively. We then derive their spectral correlation function (SCF) and investigate their applicability to solving the SSDE problem. Simulation results show that the pilot-induced cyclostationary property of WiFi signals is a promising feature that can be used to solve the SSDE problem.
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Cyclostationary analysis is a powerful tool to study the Signal-Selective Direction Estimation (SSDE) problem as different types of wireless signals have different cyclostationary patterns. Generally speaking, each type of wireless signal has unique cyclic frequencies with the frequency-selective property, which distinguishes itself from other types of signals. The cyclostationary property of a signal may be induced by its modulation method, its carrier frequency, and/or its frame structure. In this paper, we study the cyclostationary property of IEEE 802.11 (WiFi) signals induced by their underlying OFDM frame structure, which includes pilots, cyclic prefix (CP), and preambles. We first analyze the pilot-induced, CP-induced, and preamble-induced cyclostationary properties of WiFi signals, respectively. We then derive their spectral correlation function (SCF) and investigate their applicability to solving the SSDE problem. Simulation results show that the pilot-induced cyclostationary property of WiFi signals is a promising feature that can be used to solve the SSDE problem.
Key concepts: Cyclostationary process, Cyclic prefix, Computer science, SIGNAL (programming language), Property (philosophy), Frame (networking), Orthogonal frequency-division multiplexing, Wireless