Abstract:
Systems and methodologies are described that provide low-complexity soft-output detection for MIMO communication systems. Looping can be performed over a set of constellation points per spatial stream to obtain distance metrics for each of a series of transmitted streams, for which values for the other transmitted streams can be estimated using a MIMO channel matrix and a sub-optimal MIMO algorithm. Examples of MIMO algorithms that can be utilized include Per-Stream List Detection (PSLD), Lattice-Reduced Detection (LRD), and a Guided-M Algorithm. Performance can be further improved by pre-processing the MIMO channel matrix and/or by utilizing techniques for Enhanced Metric Usage (EMU).
Abstract:
An apparatus for wireless communications is disclosed herein that is configured to have a plurality of transceivers arranged to process a plurality of spatial streams, wherein each of the plurality of transceivers is configured to operate asynchronously and simultaneously with other transceivers of the plurality of transceivers.
Abstract:
Certain embodiments of the present disclosure provide techniques for approximate computation of l 2 norms as a part of the maximum likelihood (ML) detection: tri-maxmin, maxsum and sortsum algorithms. The proposed approximation schemes show better accuracy than conventional approximation schemes - the abssum and maxmin algorithms, while the computational complexity is very similar. The error rate performance of the ML detection that utilizes proposed norm-approximation techniques are very close to the reference ML detection with exact calculation of l 2 norms, while the computational complexity is significantly smaller.
Abstract:
A "post-squaring" detection algorithm, and related devices, that may reduce the complexity of maximum likelihood detection (MLD) schemes while preserving their performance is provided. Rather than search for optimum metrics (such as minimum distance metrics) based on squared norm values, a search may be based on un-squared norm metrics, and the squaring may be postponed, for example, until subsequent log-likelihood ratio (LLR) computation. For certain embodiments, approximations of un-squared norm values may significantly reduce computation complexity.