A new architecture for minimum mean square error sorted QR decomposition for MIMO wireless communication systems

Abstract

Multiantenna telecommunication systems represent channels with multiple inputs and multiple outputs (MIMO) by matrices. QR decomposition (QRD) of the channel matrix is a crucial part of MIMO detection algorithms, such as successive interference cancellation or sphere detection. Modern standards like Long Term Evoliton (LTE) require the processing of millions of matrices per second, in order to compensate channel changes that occur due to the mobility of the detector and Doppler spread. We introduce a new architecture for minimum mean square error (MMSE) sorted QR decomposition based on Givens rotations. The architecture is derived from classical systolic array approach but includes modifications to allow sorting and MMSE preprocessing. It balances throughput against area and fulfills the real-time requirements of 1.763 µs and 0.881 µs derived from the LTE MIMO standard when synthesized on ALTERA Stratix III and Stratix V family FPGAs. Moreover, it can trade speed for area and is suitable for tighter time constraints.

Reliability analysis of MIMO channel preprocessing by fault injection

“…Figure 2 depicts the QRD circuit architecture. The hardware implementation details can be found in [17] The line of systolic array, used for rotation processing, contains two types of elements: the boundary cell, which computes the new diagonal element and Sine and Cosine values, and the internal cell, which updates the matrix memory according to Eq. 12.…”