Intel Corporation

Lsi Corporation

Avago Technologies General IP (singapore) Pte. Ltd.

Ramon Sanchez Perez

Kameran Azadet

Meng-Lin Yu

Joseph H Othmer

Albert Molina

Samer Lutfi Hijazi

Joseph Williams

Jian-Guo Chen

Chengzhou Li

Steven Pinault

Software Digital Front End (SoftDFE) signal processing techniques are provided. One or more digital front end (DFE) functions are performed on a signal in software by executing one or more specialized instructions on a processor to perform the one or more digital front end (DFE) functions on the signal, wherein the processor has an instruction set comprised of one or more of linear and non-linear instructions. A block of samples comprised of a plurality of data samples is optionally formed and the digital front end (DFE) functions are performed on the block of samples. The specialized instructions can include a vector convolution function, a complex exponential function, an xfunction, a vector compare instruction, a vector max( ) instruction, a vector multiplication instruction, a vector addition instruction, a vector sqrt( ) instruction, a vector 1/x instruction, and a user-defined non-linear instruction.

Software digital front end (SoftDFE) signal processing

A method and system for canonical channel estimation in the Long Term Evolution uplink where a multi-frequency signal is generated and then converted to frequency spectrum which is then convolved in the frequency domain with a truncated window function to obtain a time domain channel impulse response. The time domain channel impulse response can be then transformed to a frequency domain to produce a down sampled user channel response, which can be then linearly interpolated to provide a channel estimate for a plurality of subcarriers. Such an approach achieves channel estimation within Long Term Evolution at only canonical locations to reduce complexity without loss in channel entropy.

Long term evolution (LTE) uplink canonical channel estimation

Block-based crest factor reduction (CFR) techniques are provided. An exemplary block-based crest factor reduction method comprises obtaining a block of data samples comprised of a plurality of samples; applying the block of data to a crest factor reduction block; and providing a processed block of data from the crest factor reduction block. The block-based crest factor reduction method can optionally be iteratively performed a plurality of times for the block of data. The block of data samples can comprise an expanded block having at least one cursor block. For example, at least two pre-cursor blocks and one post-cursor block can be employed. The peaks can be cancelled, for example, only in the block of data samples and in a first of the pre-cursor blocks.

Block-based crest factor reduction (CFR)

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Galapagar, Spain

Ramon Sanchez Perez