A scalable MIMO near-maximum likelyhood (ML) detector

Here is a novel MIMO near-maximum likelihood (ML) detection technique that is scalable in performance and power. The detector is implemented on a SDR platform and on a TI DSP.

By Eduardo Lopez-Estraviz and Liesbet Van der Perre, IMEC
mobilehandsetdesignline.com (November 30, 2008)

IMEC has developed a multiple input multiple output (MIMO) near-maximum likelihood (ML) detection technique that is scalable in terms of performance and power consumption. The scalable MIMO functionality has been designed to meet the high spectral efficiency requirements in next-generation wireless standards such as 3GPP LTE. The scalable MIMO detector will be implemented on a software defined radio (SDR) platform that is based on parallel architectures. This implementation approach supports different communication standards and meets today's demand for high spectral efficiency, low power consumption, low cost and reduced time to market.

Introduction
In order to address high spectrum costs, emerging standards such as 3GPP LTE provide high spectral efficiency. These emerging standards achieve high spectral efficiency through a combination of orthogonal frequency division multiplexing (OFDM) and spatial multiplexing (SM). To fully exploit these techniques, LTE terminals require high-complexity and power-consuming detection techniques such as multiple input multiple output (MIMO) maximum likelihood (ML) detection. Furthermore, wireless user terminals require support for multiple standards in order to operate in different parts of the world. This flexibility comes at the penalty of increasing design complexity and silicon area, since more functionality has to be implemented in the baseband platform. These requirements add up to a major challenge, because LTE terminals must be developed at low cost, short time to market and low power consumption in order to be competitive.

IMEC provides a solution for the majority of these challenges with an SDR implementation of a scalable near-ML detector. This solution adaptively and efficiently scales the ML search space. Firstly, the detection technique is scalable in terms of performance and power consumption. This scalability tailors the power consumption to the required high spectral efficiency given certain channel propagation conditions. Secondly, the scalable LTE MIMO functionality has been designed to be implemented on an SDR platform based on parallel architectures. This approach supports reduced time to market and power consumption, and it supports different communication standards. Thirdly, in combination with a flexible and scalable forward error correction (FEC) architecture, it allows a cross-layer controller to tune the detector and the FEC performance/complexity to obtain minimal power consumption given a required data throughput.

In this article, the novel MIMO near-ML detection technique is presented and its scalability in both performance and power consumption is evaluated. Next, the co-optimization of the near-ML detector algorithm and implementation for parallel architectures is discussed. Finally, the benefits of these approaches are summarized in view of the challenges that today's wireless terminals impose.

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