VLSI Architecture for Matrix Inversion using Modified Gram-Schmidt based QR Decomposition
VLSID '07 Proceedings of the 20th International Conference on VLSI Design held jointly with 6th International Conference: Embedded Systems
EURASIP Journal on Applied Signal Processing
Design and implementation of numerical linear algebra algorithms on fixed point DSPs
EURASIP Journal on Advances in Signal Processing
Architectural optimization of decomposition algorithms for wireless communication systems
WCNC'09 Proceedings of the 2009 IEEE conference on Wireless Communications & Networking Conference
Efficient FPGA implementation of MIMO decoder for mobile WiMAX system
ICC'09 Proceedings of the 2009 IEEE international conference on Communications
Implementation comparisons of the QR decomposition for MIMO detection
SBCCI '10 Proceedings of the 23rd symposium on Integrated circuits and system design
Efficient and portable SDR waveform development: the nucleus concept
MILCOM'09 Proceedings of the 28th IEEE conference on Military communications
Fundamentals of LTE
A MIMO decoder accelerator for next generation wireless communications
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
The complex Householder transform
IEEE Transactions on Signal Processing
The use of CORDIC in software defined radios: a tutorial
IEEE Communications Magazine
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Matrix decomposition of the channel matrix in the form of QR decomposition (QRD) is needed for advanced multiple input and multiple output (MIMO) demapping algorithms like sphere decoder. Due to the computation-intensive nature of the QRD, its implementation has to be highly efficient. Flexibility in several forms, e.g. support for different algorithms, reusability of wireless implementations, portability, etc. is highly sought in wireless devices. The contradictory nature of flexibility and efficiency requires tradeoffs to be made between them in system development. In this paper, we have analyzed such tradeoffs by implementing two minimum mean squared error-sorted QRD algorithms. The algorithms have been implemented in four different methods with varying degree of reusability and in five different forms of portability. The performance of the implementations is evaluated by using the real-time constraints from the LTE standard. For all the implementations, modular equations for accurately estimating the execution time are derived.