Abstract: A system and method for a LTE network device is provided. The system includes a first signal processing center configured to obtain a plurality of metrics from an LTE network device. The first signal processing center includes a plurality of processors configured to calculate interference based on the plurality of metrics. The signal processing system further includes a second signal processing center configured to receive the calculated interference from the first signal processing center.

Abstract: A method, and components for performing such method, is provided for synchronizing multiple user signals in a multi-user communication system. An interference matrix is generated based on time delay and frequency offset information for the active users accessing an OFDMA uplink receiver. User signals are received from the active users and are segmented into blocks, and the interference matrix is applied to each of the blocks. The received user signal is OFDM demodulated and un-used sub-carriers are discarded. Typically, the method includes also applying a factorization matrix formed by factoring a correction matrix created from the interference matrix and an inverse matrix formed based on the factoring results to the user signal blocks, e.g., the correction step includes multiplying each of the blocks from the user signal by each of these three matrices. The corrected user blocks are then concatenated to form a corrected vector signal.

Abstract: A blind carrier frequency offset estimator is based on a single-OFDM-symbol training sequence in multi-user OFDMA uplink. Through multiple access interference modeling and analysis, a virtual user is employed that occupies the all null sub-carriers. By minimizing the energy leakage on the virtual user in term of tentative frequency offsets, the estimator can approach the real frequency offset. The estimator performs only on frequency-domain, simplifies interference calculations, and lowers the rank of the matrix. An iterative computation method is used to approach the real frequency offset.

Abstract: A method for decoding tail-biting convolutional codes. The method includes initializing a correction depth, selecting a first starting state from a set of encoding states, and initializing a metric value for the selected starting state as zero and the other states as infinity. The input bit stream is read and a Search Depth Viterbi algorithm (SDVA) is performed to determine path metrics and identify a minimum-metric path. The ending state for the minimum-metric path is determined and the output for this ending state is identified as “previous output.” A second starting state is set to the ending state of the minimum-metric path, and symbols equal to the correction depth from the previous output are read. The SDVA is performed on the second set of read symbols to generate a corrected output. A decoded output is generated by replacing symbols at the beginning of the previous output with the corrected output.

Abstract: A method for decoding tail-biting convolutional codes. The method includes initializing a correction depth, selecting a first starting state from a set of encoding states, and initializing a metric value for the selected starting state as zero and the other states as infinity. The input bit stream is read and a Search Depth Viterbi algorithm (SDVA) is performed to determine path metrics and identify a minimum-metric path. The ending state for the minimum-metric path is determined and the output for this ending state is identified as “previous output.” A second starting state is set to the ending state of the minimum-metric path, and symbols equal to the correction depth from the previous output are read. The SDVA is performed on the second set of read symbols to generate a corrected output. A decoded output is generated by replacing symbols at the beginning of the previous output with the corrected output.

Abstract: A method for decoding tail-biting convolutional codes. The method includes initializing a correction depth, selecting a first starting state from a set of encoding states, and initializing a metric value for the selected starting state as zero and the other states as infinity. The input bit stream is read and a Search Depth Viterbi algorithm (SDVA) is performed to determine path metrics and identify a minimum-metric path. The ending state for the minimum-metric path is determined and the output for this ending state is identified as “previous output.” A second starting state is set to the ending state of the minimum-metric path, and symbols equal to the correction depth from the previous output are read. The SDVA is performed on the second set of read symbols to generate a corrected output. A decoded output is generated by replacing symbols at the beginning of the previous output with the corrected output.

Abstract: A method for decoding tail-biting convolutional codes. The method includes initializing a correction depth, selecting a first starting state from a set of encoding states, and initializing a metric value for the selected starting state as zero and the other states as infinity. The input bit stream is read and a Search Depth Viterbi algorithm (SDVA) is performed to determine path metrics and identify a minimum-metric path. The ending state for the minimum-metric path is determined and the output for this ending state is identified as “previous output.” A second starting state is set to the ending state of the minimum-metric path, and symbols equal to the correction depth from the previous output are read. The SDVA is performed on the second set of read symbols to generate a corrected output. A decoded output is generated by replacing symbols at the beginning of the previous output with the corrected output.

Abstract: A blind carrier frequency offset estimator is based on a single-OFDM-symbol training sequence in multi-user OFDMA uplink. Through multiple access interference modeling and analysis, a virtual user is employed that occupies the all null sub-carriers. By minimizing the energy leakage on the virtual user in term of tentative frequency offsets, the estimator can approach the real frequency offset. The estimator performs only on frequency-domain, simplifies interference calculations, and lowers the rank of the matrix. An iterative computation method is used to approach the real frequency offset.

Abstract: A blind carrier frequency offset estimator is based on a single-OFDM-symbol training sequence in multi-user OFDMA uplink. Through multiple access interference modeling and analysis, a virtual user is employed that occupies the all null sub-carriers. By minimizing the energy leakage on the virtual user in term of tentative frequency offsets, the estimator can approach the real frequency offset. The estimator performs only on frequency-domain, simplifies interference calculations, and lowers the rank of the matrix. An iterative computation method is used to approach the real frequency offset.

Abstract: A blind carrier frequency offset estimator is based on a single-OFDM-symbol training sequence in multi-user OFDMA uplink. Through multiple access interference modeling and analysis, a virtual user is employed that occupies the all null sub-carriers. By minimizing the energy leakage on the virtual user in term of tentative frequency offsets, the estimator can approach the real frequency offset. The estimator performs only on frequency-domain, simplifies interference calculations, and lowers the rank of the matrix. An iterative computation method is used to approach the real frequency offset.

Abstract: A method for decoding tail-biting convolutional codes. The method includes initializing a correction depth, selecting a first starting state from a set of encoding states, and initializing a metric value for the selected starting state as zero and the other states as infinity. The input bit stream is read and a Search Depth Viterbi algorithm (SDVA) is performed to determine path metrics and identify a minimum-metric path. The ending state for the minimum-metric path is determined and the output for this ending state is identified as “previous output.” A second starting state is set to the ending state of the minimum-metric path, and symbols equal to the correction depth from the previous output are read. The SDVA is performed on the second set of read symbols to generate a corrected output. A decoded output is generated by replacing symbols at the beginning of the previous output with the corrected output.

Abstract: A method, and components for performing such method, is provided for synchronizing multiple user signals in a multi-user communication system. An interference matrix is generated based on time delay and frequency offset information for the active users accessing an OFDMA uplink receiver. User signals are received from the active users and are segmented into blocks, and the interference matrix is applied to each of the blocks. The received user signal is OFDM demodulated and un-used sub-carriers are discarded. Typically, the method includes also applying a factorization matrix formed by factoring a correction matrix created from the interference matrix and an inverse matrix formed based on the factoring results to the user signal blocks, e.g., the correction step includes multiplying each of the blocks from the user signal by each of these three matrices. The corrected user blocks are then concatenated to form a corrected vector signal.