Abstract: A circuit may be configured to adaptively combine two or more waveforms into a single waveform. The circuit can generate weighting factors based on received error signals, and can apply the weighting factors to the two or waveforms to be combined. In some examples, a circuit can be configured to receive input signals, receive error signals, generating a weighting coefficient based on at least some of the error signals, and determine an output signal based on the weighting coefficient and the input signals.

Abstract: Technologies are described herein for adapting channel parameters of a read channel in a transformed space. A set of channel parameter values associated with one or more components of the read channel is received. A general transformation is performed on the channel parameter values, and insignificant terms in the transformed space are filtered out. Next, an inverse transformation is performed on the filtered terms in the transformed space to calculate a new set of channel parameter values for the component(s).

Abstract: Technologies are described herein for adapting channel parameters of a read channel in a transformed space. A set of channel parameter values associated with one or more components of the read channel is received. A general transformation is performed on the channel parameter values, and insignificant terms in the transformed space are filtered out. Next, an inverse transformation is performed on the filtered terms in the transformed space to calculate a new set of channel parameter values for the component(s).

Abstract: A circuit may be configured to adaptively combine two or more waveforms into a single waveform. The circuit can generate weighting factors based on received error signals, and can apply the weighting factors to the two or waveforms to be combined.

Abstract: Technologies are described herein for implementing a universal modulation coding mechanism for a data channel. A user data sequence comprising a first number of bits is received. The user data sequence is encoded into a codeword sequence comprising a second number of bits utilizing a modulation encoder. The modulation encoder may encode the user data sequence based on a set of Markov state transition probabilities. The modulation encoder may implement a data de-compressor algorithm from a lossless data compression scheme. The codeword sequence is output to the data channel. The codeword sequence is subsequently received from the data channel, and the user data sequence is decoded from the codeword sequence utilizing a modulation decoder. The modulation decoder may decode the user data sequence based on the same set of Markov states transition probabilities. The modulation de-coder may implement the data compressor algorithm from the lossless data compression scheme.

Abstract: Training is performed to characterize one or more communication channels between a first communication unit (CU) and one or more additional CUs. Channel characteristic(s) are determined by using first training signals received by the first CU from one of the additional CUs. Second training signals, defined at least in part by the channel characteristic(s), are determined. The channel characteristic(s) may comprise a unitary factor and power levels for subchannels. The second training signals are transmitted from the first CU to the one additional CU, which not only determines characteristics of the channel but also usually determines scheduling information. Each CU independently determines communication rates on subchannels. Typically, the two communication rates will be in agreement. The one additional CU sends modified training signals so that the first CU lowers the communication rate on subchannels.