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 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: 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: 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.

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, jointly, a maximum likelihood (ML) solution of rank, traffic to pilot ratio (TPR), and precoding matrix index on the GA-applied serving signal and the GA-applied interference signal. The apparatus includes an antenna for receiving a signal including a serving signal and an interference signal; a processor configured to apply a GA on the serving signal and the interference signal, and determine, jointly, an ML solution of rank, TPR, and precoding matrix index on the GA-applied serving signal and the GA-applied interference signal.

Abstract: Methods and apparatus for soft MIMO detection of high order QAM with initial candidate reduction are described. A method includes receiving a plurality of signals including Q-order quadrature amplitude modulation (QAM) symbols; determining, based on linear minimum mean square error (MMSE) estimation using detection of I and Q signs around a target layer, a reduced candidate set including C potential candidates, where C is less than Q; calculating Euclidean distances (EDs) based on the reduced candidate set; and generating log-likelihood ratio (LLR) information based on the calculated EDs.

Abstract: Methods and apparatus for soft MIMO detection of high order QAM with initial candidate reduction are described. A method includes receiving a plurality of signals including Q-order quadrature amplitude modulation (QAM) symbols; determining, based on linear minimum mean square error (MMSE) estimation using detection of I and Q signs around a target layer, a reduced candidate set including C potential candidates, where C is less than Q; calculating Euclidean distances (EDs) based on the reduced candidate set; and generating log-likelihood ratio (LLR) information based on the calculated EDs.

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, jointly, a maximum likelihood (ML) solution of rank, traffic to pilot ratio (TPR), and precoding matrix index on the GA-applied serving signal and the GA-applied interference signal. The apparatus includes an antenna for receiving a signal including a serving signal and an interference signal; a processor configured to apply a GA on the serving signal and the interference signal, and determine, jointly, an ML solution of rank, TPR, and precoding matrix index on the GA-applied serving signal and the GA-applied interference signal.

Abstract: Methods and apparatus for soft MIMO detection of high order QAM with initial candidate reduction are described. A method includes receiving a plurality of signals including Q-order QAM symbols; determining a reduced candidate set including C potential candidates, where C is less than Q; calculating Euclidean distances (EDs) based on the reduced candidate set; and generating LLR information based on the calculated EDs.

Abstract: A method is provided. The method includes receiving a signal, wherein the signal includes a serving signal and an interference signal; removing the serving signal from the received signal to provide a residual signal; equalizing the residual signal based on linear estimation; determining a sample sum of the equalized signal; determining a plurality of eigenvalues from the sample sum; and estimating a transmission rank of the interference signal using hypothesis testing based on the plurality of eigenvalues.

Abstract: Methods and apparatus for soft MIMO detection of high order QAM with initial candidate reduction are described. A method includes receiving a plurality of signals including Q-order QAM symbols; determining a reduced candidate set including C potential candidates, where C is less than Q; calculating Euclidean distances (EDs) based on the reduced candidate set; and generating LLR information based on the calculated EDs.