Abstract: A system and a method are disclosed and include generating a first kernel based on a tap of a first transmission and a plurality of taps of a second transmission; calculating a first weighted coefficient set based on the first kernel; calculating a first self-interference transmission signal estimation based on the first weighted coefficient set; and obtaining a first cancellation signal by subtracting the first self-interference transmission signal estimation from a downlink received signal.

Abstract: A system and a method are disclosed for decoding in wireless communications. In some embodiments, a method includes receiving a reference signal; generating a channel estimate, based on the reference signal; determining a channel estimation error metric for the channel estimate; receiving a transmission; calculating a log likelihood ratio, based on the channel estimation error metric, for each of a plurality of bit positions of the transmission; and decoding the transmission based on the log likelihood ratio. The calculating of the log likelihood ratio may include calculating a corrected log likelihood ratio based at least on an uncorrected log likelihood ratio.

Abstract: A system includes: a first transmitter to transmit a first signal; a second transmitter to transmit a second signal; a receiver to receive a downlink signal, the downlink signal including a desired signal and an interference signal corresponding to the first and second signals; a processor; and memory including instructions that, when executed by the processor, cause the processor to: select an interference function based on center frequencies of the first and second transmitters and the receiver; calculate correlation values based on a first delay between the receiver and a reference transmitter, and a second delay between the first and second transmitters; select one or more filter taps based on the calculated correlation values; estimate the interference signal based on the selected one or more filter taps and the interference function; determine the desired signal from the downlink signal according to the estimated interference signal; and transmit the desired signal.

Abstract: A system and a method are disclosed for noise power estimation in wireless communications. In some embodiments, the method includes: receiving a reference signal; generating a first channel estimate, based on the reference signal; calculating a first noise power estimate; calculating a corrected noise power estimate by applying a multiplicative correction based on a linear minimum mean square error filter weight to the first noise power estimate; receiving a transmission; and decoding the transmission, based on the corrected noise power estimate.

Abstract: A system and a method are disclosed and include generating a first kernel based on a tap of a first transmission and a plurality of taps of a second transmission; calculating a first weighted coefficient set based on the first kernel; calculating a first self-interference transmission signal estimation based on the first weighted coefficient set; and obtaining a first cancellation signal by subtracting the first self-interference transmission signal estimation from a downlink received signal.

Abstract: A method and system are provided. The method includes receiving an input frame, performing high frequency noise reduction (HFNR) on the received input frame, performing low frequency noise reduction (LFNR) on the received input frame, and obtaining a hybrid denoised clean frame by fusing an output of the performed HFNR and an output of the performed LFNR.

Abstract: A method and system are provided. The method includes obtaining a difference map between a reference frame and a non-reference frame, estimating a noise variance of the obtained difference map, obtaining merging weights based on the estimated noise variance and the obtained different map, and merging the non-reference frame with the reference frame using the obtained merging weights.

Abstract: A system and a method are disclosed for determining early termination during an iterative detection and decoding (IDD) procedure. The method may include computing, during the IDD procedure, one or more log-likelihood ratios (LLRs) of one or more cyclic-redundancy checks (CRC), and determining that at least one of the LLRs predict a failure of a CRC check and, in response, terminating the IDD procedure.

Abstract: A system and a method are disclosed for normalizing Log-Likelihood Ratios for bits of a transport block. For a narrowband channel estimation (NBCE), a normalization factor is selected based on an estimated delay spread, a cyclic prefix, an estimated Doppler spread, a rank, a modulation and coding scheme (MCS) and a signal-to-noise ratio (SNR) for the transport block, and an input LLRin for the individual bits of the transport block are scaled to respectively form an output LLRout for the individual bits of the transport block using the normalization factor. For a wideband channel estimation, a normalization factor is selected based on the rank/MCS/SNR of the transport block, and the input LLRin for the individual bits of the transport block are scaled to respectively form the output LLRout for the individual bits of the transport block using the normalization factor.

Abstract: A system and a method are disclosed for normalizing Log-Likelihood Ratios for bits of a transport block. For a narrowband channel estimation (NBCE), a normalization factor is selected based on an estimated delay spread, a cyclic prefix, an estimated Doppler spread, a rank, a modulation and coding scheme (MCS) and a signal-to-noise ratio (SNR) for the transport block, and an input LLRin for the individual bits of the transport block are scaled to respectively form an output LLRout for the individual bits of the transport block using the normalization factor. For a wideband channel estimation, a normalization factor is selected based on the rank/MCS/SNR of the transport block, and the input LLRin for the individual bits of the transport block are scaled to respectively form the output LLRout for the individual bits of the transport block using the normalization factor.

Abstract: A method and system are provided. The method includes determining a difference map between a reference frame and a non-reference frame, determining a local variance of the reference frame, determining a detail power map based on a difference between the determined local variance and the determined difference map, and determining a detail grade map based on the determined detail power map.

Abstract: An apparatus and method for successive multi-frame image denoising are herein disclosed. The apparatus includes a first subtractor including a first input to receive a frame of the image, a second input to receive a reference frame, and an output; an absolute value function block including an input connected to the output of the first subtractor and an output; a second subtractor including a first input connected to the output of the absolute value function block, a second input for receiving a first predetermined value, and an output; and a maximum value divider function block including an input connected to the output of the second subtractor and an output for outputting filter weights.

Abstract: A method and system are provided. The method includes receiving an input frame, performing high frequency noise reduction (HFNR) on the received input frame, performing low frequency noise reduction (LFNR) on the received input frame, and obtaining a hybrid denoised clean frame by fusing an output of the performed HFNR and an output of the performed LFNR.

Abstract: A method and system are provided. The method includes obtaining a difference map between a reference frame and a non-reference frame, estimating a noise variance of the obtained difference map, obtaining merging weights based on the estimated noise variance and the obtained different map, and merging the non-reference frame with the reference frame using the obtained merging weights.

Abstract: A method and system are provided. The method includes determining a difference map between a reference frame and a non-reference frame, determining a local variance of the reference frame, determining a detail power map based on a difference between the determined local variance and the determined difference map, and determining a detail grade map based on the determined detail power map.

Abstract: An apparatus and method for successive multi-frame image denoising are herein disclosed. The apparatus includes a first subtractor including a first input to receive a frame of the image, a second input to receive a reference frame, and an output; an absolute value function block including an input connected to the output of the first subtractor and an output; a second subtractor including a first input connected to the output of the absolute value function block, a second input for receiving a first predetermined value, and an output; and a maximum value divider function block including an input connected to the output of the second subtractor and an output for outputting filter weights.

Abstract: An apparatus and method for successive multi-frame image denoising are herein disclosed. According to one embodiment, an apparatus includes a Wiener filter configured to filter a frame of an image; and a frame delayer configured to feedback the filtered frame to the Wiener filter, wherein the Wiener filter is further configured to filter a subsequent frame of the image based on the filtered frame.

Abstract: A method and an apparatus. The method includes receiving a signal including a serving signal and an interference signal; applying a Gaussian approximation (GA) on the serving signal and the interference signal; determining likelihood metrics for hypotheses on a rank of the interference signal, a precoding matrix index, and power on the GA-applied interference signal based on a covariance matrix for each of the hypotheses; and selecting one of the hypotheses that maximizes one of the likelihood metrics.

Abstract: An apparatus and method for successive multi-frame image denoising are herein disclosed. According to one embodiment, an apparatus includes a Wiener filter configured to filter a frame of an image; and a frame delayer configured to feedback the filtered frame to the Wiener filter, wherein the Wiener filter is further configured to filter a subsequent frame of the image based on the filtered frame.

Abstract: A method and an apparatus. The method includes receiving a signal including a serving signal and an interference signal; applying a Gaussian approximation (GA) on the serving signal and the interference signal; determining likelihood metrics for hypotheses on a rank of the interference signal, a precoding matrix index, and power on the GA-applied interference signal based on a covariance matrix for each of the hypotheses; and selecting one of the hypotheses that maximizes one of the likelihood metrics.