A method includes moving one or more transmit facility or transmit facilities attached to a first component with regard to at least two receive facilities attached at a fixed position to the second component; and at least one of registering a respective error if an error condition exists for the respective receive facility or modifying an operation of an apparatus comprising the first and a second component if the error condition exists for the respective receive facility, the error condition for the respective receive facility depends on location information relating to at least one of the position of the first component with regard to the second component or orientation of the first component with regard to the second component, and/or at least one of a measure for a receive quality of the signals or data packets received from the transmit facility or from at least one of the transmit facilities.

Aspects of the invention include a driver arranged at a stand-alone receiver that is configured to receive a binary sequence from a pseudorandom binary sequence (PRBS) generator arranged at the receiver. The driver is configured to adjust the signal characteristics of the binary sequence to simulate channel loss at the receiver. The driver is further configured to output the adjusted binary sequence to a downstream data path of the receiver to enable the receiver to perform a self-test.

A method for reconstructing uncorrectable forward error correction (FEC) data includes generating and transcoding a known bit sequence and transmitting a FEC encoded codeword that includes a payload containing the transcoded known bit sequence through a component under test. The method further includes receiving the FEC encoded codeword transmitted via the component under test and determining that the encoded contents of the FEC encoded codeword contains a number of symbol errors that exceeds a predefined threshold. The method also includes utilizing stored scramble seed bits corresponding to an immediately preceding FEC encoded codeword and the transcoded known bit sequence to generate a reconstructed codeword.

A method for reconstructing uncorrectable forward error correction (FEC) data includes generating and transcoding a known bit sequence and transmitting a FEC encoded codeword that includes a payload containing the transcoded known bit sequence through a component under test. The method further includes receiving the FEC encoded codeword transmitted via the component under test and determining that the encoded contents of the FEC encoded codeword contains a number of symbol errors that exceeds a predefined threshold. The method also includes utilizing stored scramble seed bits corresponding to an immediately preceding FEC encoded codeword and the transcoded known bit sequence to generate a reconstructed codeword.

One example discloses an apparatus for near-field magnetic induction (NFMI) based robustness, including: a first wireless device including a first wireless signal interface and a first NFMI signal interface; wherein the first wireless signal interface is configured to receive a data set from a third wireless device; wherein the first NFMI signal interface is configured to communicate with a second wireless device through a second NFMI signal interface; and wherein the first wireless device is configured to detect an error in the data set received from the third wireless device and in response to detecting the error configure the first NFMI signal interface to receive the data set from the second wireless device through the second NFMI signal interface.

Apparatus, systems and methods for error detection in transmissions on a multi-wire interface are disclosed. A method for correcting transmission errors in multi-wire transition-encoded interface may include determining whether a symbol error is present in the sequence of symbols based on a value of an error detection code (EDC) in the received plurality of bits, generating one or more permutations of the sequence of symbols, where each permutation includes one symbol that is different from corresponding symbols in the sequence of symbols and different from corresponding symbols in other permutations. A permutation in the one or more permutations may be identified as including a corrected sequence of symbols when it produces a decoded EDC value that matches an expected EDC value. The expected EDC value may correspond to a predefined value for EDCs transmitted over the multi-wire interface to enable detection of up to two symbol errors at the receiver.

A system and method for acquiring a frequency hopped spread spectrum (FHSS) signal with no prior knowledge about the FHSS signal. In example implementations, an RF signal is received at a receiver. The RF signal is converted into a stream of digital signal levels. Energy detections are identified in the stream of digital signal as possible hops of a FHSS signal. A feature set is blindly acquired for defining an FHSS signal from the energy detections. At least one waveform classification is generated based on the feature set. Energy detections are re-acquired from the RF signal based on the waveform classification.

A signal analysis apparatus for analyzing an input signal is described. The input signal includes a symbol sequence. The symbol sequence includes data information and redundant data information. The signal analysis apparatus includes one or more circuits composed of a decoder module, an error correction module, and a processing module. The decoder module is configured to decode the input signal, thereby obtaining a decoded input signal. The error correction module is configured to identify at least one error in the decoded input signal. The processing module is configured to generate a data set. The data set includes information on the at least one identified error. The data set further includes information on at least one of a portion of the input signal being associated with the error and a portion of the decoded input signal being associated with the error. Further, a signal analysis device and a signal analysis method are described.

Apparatus and associated methods relate to predicting various transient output waveforms at a receiver's output after an initial neural network model is trained by a receiver's transient input waveform and a corresponding transient output waveform. In an illustrative example, the machine learning model may include an adaptive-ordered auto-regressive moving average external input based on neural networks (NNARMAX) model designed to mimic the performance of a continuous time linear equalization (CTLE) mode of the receiver. A Pearson Correlation Coefficient (PCC) score may be determined to select numbers of previous inputs and previous outputs to be used in the neural network model. In other examples, corresponding bathtub characterizations and eye diagrams may be extracted from the predicted transient output waveforms. Providing a machine learning model may, for example, advantageously predict various data patterns without knowing features or parameters of the receiver or related channels.

To include an FCS code calculation unit to calculate a first code that is an FCS code of a received frame, an FCS check unit to compare the first code with a second code that is an FCS code stored in the received frame, and to output a first comparison result indicating a match or no match between two codes, an inverse FCS check unit to compare the first code with a third code obtained by inverting each bit of the second code, and to output a second comparison result indicating a match or no match between two codes, an error determination unit to determine an error state of the received frame, and an FCS update unit to update, if the second comparison result indicates no match, the second code with a fourth code obtained by inverting each bit of the first code.