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Joint Estimation of Carrier and Sampling Frequency Offsets Using OFDM WLAN Preamble

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Abstract

In this paper, the problem of joint estimation of carrier and sampling frequency offsets is considered, for IEEE 802.11ac-based OFDM wireless LAN systems, based on the guard interval and two long symbols (GI2L) in the preamble of a packet. The measurement model of the GI2L is developed, in the presence of both carrier and sampling frequency offsets. One method based on the GI2L is proposed and compared with some existing relevant methods based on long symbol/data symbols. An improved performance, measured by estimation error and bit error rate, is achieved by this method, at a similar computational load.

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Notes

  1. This rectangular shaping function was used in [1, 15], but slightly different from that suggested in Figures 22–29 of [21].

  2. This channel model can also include the effect of transmit and receive filters.

  3. \(\psi\) under this assumption is called fractional CFO. This assumption is valid if the frequency accuracies of oscillators in transmitter and receiver satisfy the IEEE 802.11ac recommendation. Otherwise, one has to estimate the integer part of CFO from the demodulated measurement as in [16], or from the MUSIC spectrum as in [17] using data symbols and/or long symbols. One subcarrier spacing is equal to 20 MHz/64 = 312.5 kHz.

  4. A stationary channel is a channel which is invariant across various simulation runs.

  5. Among 52 phases, the averaged number of phases in that range is 51.994 at \(\hbox {SNR} = 40\,\hbox {dB}\), 51.95 at \(\hbox {SNR} = 30\,\hbox {dB}\), 51.514 at \(\hbox {SNR} = 20\,\hbox {dB}\) and 47.652 at \(\hbox {SNR} = 10\,\hbox {dB}\), over 3000 simulation runs.

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Correspondence to Qi Cheng.

Appendix: Robust Channel Estimation Using GI2L

Appendix: Robust Channel Estimation Using GI2L

Define

$$\begin{aligned} {\mathbf{v}}_{k}&= {} \left[ z_{0}^{ -\,16 k (1+\zeta )}, \ldots , z_{0}^{ - k(1+\zeta )}, \right. \\&\left. 1,z_{0}^{ k (1+\zeta )},\ldots , z_{0}^{ (2N-1) k (1+\zeta )} \right] ^{T} \\ {\mathbf{V}}_{\zeta }&= {} \left[ {\mathbf{v}}_{-26}, \ldots , {\mathbf{v}}_{-1}, {\mathbf{v}}_{1}, \ldots , {\mathbf{v}}_{26} \right] \\ {\mathbf{E}}_{\psi ,\zeta }&= {} { diag}\left[ z_{0}^{ -\,16(1+\zeta )\psi }, \ldots , z_{0}^{ -(1+\zeta )\psi },\right. \\&\left. 1,z_{0}^{ (1+\zeta )\psi },\ldots , z_{0}^{ (2N-1)(1+\zeta )\psi } \right] \\ {\mathbf{S}}&= {} { diag}\left[ s(-26),\ldots ,s(-1),s(1),\ldots ,s(26)\right] \\&{\mathbf{A}}_{\psi ,\zeta } = {\mathbf{E}}_{\psi ,\zeta } {\mathbf{V}}_{\zeta } {\mathbf{S}} \\ {{\mathcal {H}}}&= {} \left[ H_{-26},\ldots ,H_{-1},H_{1},\ldots ,H_{26}\right] ^{T} \\ \hat{{\mathbf{y}}}&= {} \left[ {\hat{y}}(-\,16),\ldots ,{\hat{y}}(-1),{\hat{y}}(0), {\hat{y}}(1), \ldots ,{\hat{y}}(2N-1)\right] ^{T} \\ {\varvec{\xi }}&= {} \left[ \xi (-\,16),\ldots ,\xi (-1),\xi (0), \xi (1), \ldots ,\xi (2N-1)\right] ^{T}. \end{aligned}$$

Then the noisy GI2L samples in (10) can be written in a matrix-vector form as \(\hat{\mathbf{y}} = {\mathbf{A}}_{\psi ,\zeta } {{\mathcal {H}}} + {\varvec{\xi }}.\) For the noisy GI2L and given a pair of estimates \({\hat{\psi }}={\hat{\psi }}_{gl}+{\hat{\epsilon }},{\hat{\zeta }}\), where \({\hat{\epsilon }},{\hat{\zeta }}\) are given by a fine-tuning method, the channel response vector \({{\mathcal {H}}}\) can be estimated as

$$\begin{aligned} {\hat{\mathcal {H}}} = \left( {\mathbf{A}}_{{\hat{\psi }},{\hat{\zeta }}}^{H} {\mathbf{A}}_{{\hat{\psi }},{\hat{\zeta }}}\right) ^{-1} {\mathbf{A}}_{{\hat{\psi }},{\hat{\zeta }}}^{H} \hat{{\mathbf{y}}}. \end{aligned}$$
(156)

Expressions (11) and (12) of [3] can also be used to calculate an estimate of \(H_{k_{1}}\). The resulting estimates are found to be much less accurate than that in (156) and thus are not used in this paper.

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Cheng, Q. Joint Estimation of Carrier and Sampling Frequency Offsets Using OFDM WLAN Preamble. Wireless Pers Commun 98, 2121–2161 (2018). https://doi.org/10.1007/s11277-017-4967-8

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