Abstract: Apparatuses, systems, and methods are disclosed for stochastic linear detection. A digital signal processor determines information about a plurality of transmitted signals based on a plurality of received signals. An initialization module determines an estimator matrix and a noise shaping matrix based on channel state information that relates the transmitted signals to the received signals. A sample generation module stochastically generates a plurality of signal estimates so that each signal estimate is a sum of a fixed component and a random component. The fixed component may be based on applying the estimator matrix to a vector of the received signals, and the random component may be based on applying the noise shaping matrix to generated noise. An output module sends soft information to an error-correcting code (ECC) decoder for decoding bits carried by the transmitted signals. The soft information may be based on the plurality of signal estimates.

Abstract: Apparatuses, systems, and methods are disclosed for stochastic linear detection. A digital signal processor determines information about a plurality of transmitted signals based on a plurality of received signals. An initialization module determines an estimator matrix and a noise shaping matrix based on channel state information that relates the transmitted signals to the received signals. A sample generation module stochastically generates a plurality of signal estimates so that each signal estimate is a sum of a fixed component and a random component. The fixed component may be based on applying the estimator matrix to a vector of the received signals, and the random component may be based on applying the noise shaping matrix to generated noise. An output module sends soft information to an error-correcting code (ECC) decoder for decoding bits carried by the transmitted signals. The soft information may be based on the plurality of signal estimates.

Abstract: Apparatuses, systems, and methods are disclosed for stochastic interference cancellation. A digital signal processor decodes a set of transmitted signals based on a set of received signals. A projection module updates a subset of a set of estimated signals based on error information and on a corresponding subset of mapping information. Error information is based on the received signals and the set of estimated signals. Mapping information indicates how the received signals are affected by the transmitted signals. A stochastic update module adjusts the set of estimated signals based on generated noise. An interference cancellation module updates the error information based on the adjusted estimated signals.

Abstract: A technology is disclosed and described for processing a radio signal using an excited Markov Chain Monte Carlo (MCMC) Gibbs sampler. An example method (200) may include calculating a distance (210) of a bit sequence from an estimated correct bit sequence, where the bit sequence may be extracted from a received radio signal received by a Multiple-Input and Multiple-Output (MIMO) detector. An excitation factor may be modified (220) based in part on the distance of the bit sequence from the estimated correct bit sequence, where the where the excitation factor may be used in part to calculate a probability of state transition of the bit sequence. A pseudo-convergence of the probability of state transition of the bit sequence may be detected (230) and a pseudo-convergence mitigation technique may be executed (240) that causes the probability of state transition of the bit sequence to increase.