Modified Partial Euclidean Distance for iterative tree-search MIMO detection

Abstract

To meet the requirements of modern, high throughput communication systems, like the 3GPP Long Term Evolution, which aims to achieve a peak throughput of 100 Mbit/s in the downlink and 50 Mbit/s in the uplink, MIMO is a key technology. Therefore, efficient MIMO detection algorithms have become of major interest. Iterative tree-search detectors offer a good trade-off between the computational complexity and the BER performance. All these detectors assume a QR decomposed channel matrix to transform the Maximum Likelihood problem into a tree structure and to define a criterion to prune branches early. This criterion can be described by the Partial Euclidean Distance. In this paper we consider an iterative tree-search detector, namely a K-best detector, in combination with a specific QR-decomposition algorithm to formulate a Modified Partial Euclidean Distance and to avoid square roots and divisions, which normally appear due to the QR-decomposition. Hence, in opposite to an usual, separate algorithm optimization, a combined optimization is described.