Abstract:
Software implementations are provided for performing IQ imbalance correction and/or RF equalization. An input signal, x, is processed in software by executing a vector convolution instruction to apply the input signal, x, to a first complex FIR filter that performs one or more of RF equalization and IQ imbalance correction; and executing a vector convolution instruction to apply a conjugate x* of the input signal, x, to a second complex FIR filter that performs the one or more of RF equalization and IQ imbalance correction, wherein the second complex FIR filter is in parallel with the first complex FIR filter. The first and second complex FIR filters have complex coefficients and the input signal comprises a complex signal.
Abstract:
A vector processor is provided having an instruction set with a vector convolution function. The disclosed vector processor performs a convolution function between an input signal and a filter impulse response by obtaining a vector comprised of at least N1+N2-1 input samples; obtaining N2 time shifted versions of the vector (including a zero shifted version), wherein each time shifted version comprises N1 samples; and performing a weighted sum of the time shifted versions of the vector by a vector of N1 coefficients; and producing an output vector comprising one output value for each of the weighted sums. The vector processor performs the method, for example, in response to one or more vector convolution software instructions having a vector input. The vector can comprise a plurality of real or complex input samples and the filter impulse response can be expressed using a plurality of coefficients that are real or complex.
Abstract:
In one embodiment, a programmable vector processor performs preamble detection in a wireless communication network. Implementation of preamble detection in the vector processor is made possible by a set of vector instructions that include (i) a circular load instruction for loading vectors of received data, (ii) a correlation instruction for correlating the vectors of received data with vectors of the scrambling code to concurrently generate a plurality of complex correlations, (iii) a partial-transpose instruction for arranging vectors of the complex correlations for use by a Fast Hadamard Transform (FHT) processor, and (iv) an FHT instruction for performing FHT processing on a vector of complex correlations. Implementing preamble detection in the vector processor allows more of the received data to be processed concurrently. As a result, preamble detectors of the disclosure may detect preambles using fewer clock cycles than that of comparable preamble detectors implemented using hardware accelerators.
Abstract:
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 xk function, 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.
Abstract:
A processor is provided having an instruction set with a sliding window non-linear convolution function. A processor obtains a software instruction that performs a non-linear convolution function for a plurality of input delayed signal samples. In response to the software instruction for the non-linear convolution function, the processor generates a weighted sum of two or more of the input delayed signal samples, wherein the weighted sum comprises a plurality of variable coefficients defined as a sum of one or more non-linear functions of a magnitude of the input delayed signal samples; and repeats the generating step for at least one time-shifted version of the input delayed signal samples to compute a plurality of consecutive outputs. The software instruction for the non-linear convolution function is optionally part of an instruction set of the processor. The non-linear convolution function can model a non-linear system with memory, such as a power amplifier model and/or a digital pre-distortion function.
Abstract:
Systems, methods, and apparatuses relating to a user defined formatting instruction to configure multicast Benes network circuitry are described. In one embodiment, a processor includes a decoder to decode a single instruction into a decoded single instruction, the single instruction having fields that identify packed input data, packed control data, and a packed data destination; and an execution unit to execute the decoded single instruction to: send the packed control data to respective control inputs of a circuit that comprises an inverse butterfly circuit coupled in series to a butterfly circuit, wherein the inverse butterfly circuit comprises a first plurality of stages of multicast switches and the butterfly circuit comprises a second plurality of stages of multicast switches, read, once from storage separate from the circuit, each element of the packed input data as respective inputs of the circuit, route the packed input data through the circuit according to the packed control data, and store resultant packed data from the circuit into the packed data destination.
Abstract:
A digital processor is provided having an instruction set with a complex exponential function. The digital processor evaluates a complex exponential function for an input value, x, by obtaining a complex exponential software instruction having the input value, x, as an input; and in response to the complex exponential software instruction: invoking at least one complex exponential functional unit that implements complex exponential software instructions to apply the complex exponential function to the input value, x; and generating an output corresponding to the complex exponential of the input value, x. A complex exponential function for an input value, x, can be evaluated by wrapping the input value to maintain a given range; computing a coarse approximation angle using a look-up table; scaling the coarse approximation angle to obtain an angle from 0 to θ; and computing a fine corrective value using a polynomial approximation.