Abstract: Embodiments are presented herein of apparatuses, systems, and methods for a wireless device to perform improved channel estimates for sensing applications such as ranging. The wireless device may determine noise characteristics, e.g., a spectrum of the variance of noise on a channel and may use the noise characteristics to estimate a response of an analog front end of the wireless device. The wireless device may correct a channel estimate based on the estimated response of the analog front end.

Abstract: Embodiments are presented herein of apparatuses, systems, and methods for a wireless device to perform improved channel estimates for sensing applications such as ranging. The wireless device may determine noise characteristics, e.g., a spectrum of the variance of noise on a channel and may use the noise characteristics to estimate a response of an analog front end of the wireless device. The wireless device may correct a channel estimate based on the estimated response of the analog front end.

Abstract: Embodiments are presented herein of apparatuses, systems, and methods for a wireless device to perform improved channel estimates for sensing applications such as ranging. The wireless device may determine noise characteristics, e.g., a spectrum of the variance of noise on a channel and may use the noise characteristics to estimate a response of an analog front end of the wireless device. The wireless device may correct a channel estimate based on the estimated response of the analog front end.

Abstract: Methods and systems for substantially simultaneously scan of two or more frequencies using one transceiver are discussed herein. For example, a listening device can include a controller configured to control its transceiver to alternate between two or more frequencies from two or more sets of frequencies. The controller is also configured to capture a portion of a preamble of a received signal to determine whether the received signal is intended for the listening device.

Abstract: Methods and systems for substantially simultaneously scan of two or more frequencies using one transceiver are discussed herein. For example, a listening device can include a controller configured to control its transceiver to alternate between two or more frequencies from two or more sets of frequencies. The controller is also configured to capture a portion of a preamble of a received signal to determine whether the received signal is intended for the listening device.

Abstract: This document describes techniques for decoding a convolutionally coded signal using a trellis decoder in a drift robust manner. A convolutionally coded and differentially modulated signal may be received. The signal may be decoded using a trellis. A noise prediction loop may be used to reduce noise characteristics of the signal. A frequency offset estimation loop may be used to reduce a frequency offset drift of the signal. The noise prediction loop and the frequency offset estimation loop may be applied at each branch of the trellis.

Abstract: This document describes techniques for decoding a convolutionally coded signal using a trellis decoder in a drift robust manner. A convolutionally coded and differentially modulated signal may be received. The signal may be decoded using a trellis. A noise prediction loop may be used to reduce noise characteristics of the signal. A frequency offset estimation loop may be used to reduce a frequency offset drift of the signal. The noise prediction loop and the frequency offset estimation loop may be applied at each branch of the trellis.

Abstract: This document describes techniques for performing non-coherent demodulation of a differentially modulated signal in a drift robust manner. A differentially modulated signal, including a plurality of symbols, may be received. Non-coherent demodulation of the differentially modulated signal may be performed. The non-coherent demodulation may include performing noise prediction using a high-pass filtered estimated error signal associated with the differentially modulated signal. In some embodiments, frequency offset estimation to reduce frequency offset of the differentially modulated signal may also be performed as part of the non-coherent demodulation.

Abstract: This document describes techniques for performing non-coherent demodulation of a differentially modulated signal in a drift robust manner. A differentially modulated signal, including a plurality of symbols, may be received. Non-coherent domodulation of the differentially modulated signal may be performed. The non-coherent demodulation may include performing noise prediction using a high-pass filtered estimated error signal associated with the differentially modulated signal. In some embodiments, frequency offset estimation to reduce frequency offset of the differentially modulated signal may also be performed as part of the non-coherent demodulation.

Abstract: A modified Viterbi algorithm, that integrates decision feedback equalization (DFE), is disclosed. The modified algorithm may be used especially for rate ½ coded transmissions through additive noise channels, where the additive noise is one-tap filtered noise. Each state in the presently-disclosed trellis holds an aggregate error and an aggregate weight of the winner path terminating at that state. Each branch of the trellis carries one or more aggregate errors, where each of the aggregate errors includes a contribution from the aggregate error of the branch's source state as well as a contribution from the difference between an expected symbol of the branch and a corresponding received symbol.

Abstract: A modified Viterbi algorithm, that integrates decision feedback equalization (DFE), is disclosed. The modified algorithm may be used especially for rate 1/2 coded transmissions through additive noise channels, where the additive noise is one-tap filtered noise. Each state in the presently-disclosed trellis holds an aggregate error and an aggregate weight of the winner path terminating at that state. Each branch of the trellis carries one or more aggregate errors, where each of the aggregate errors includes a contribution from the aggregate error of the branch's source state as well as a contribution from the difference between an expected symbol of the branch and a corresponding received symbol.