Algorithm and hardware aspects of pre-coding in massive MIMO systems

Massive Multiple-Input Multiple-Output (MIMO) systems have been shown to improve both spectral and energy efficiency one or more orders of magnitude by efficiently exploiting the spatial domain. Low-cost RF chains can be employed to reduce the Base Station (BS) cost, however this may require additional baseband processing to handle induced distortions due to the hardware impairments. In this article the reduction of Peak-to-Average power Ratio (PAR) of the transmitted signals and IQ imbalance in the mixer are analyzed for the down-link. We analyze various pre-coding schemes and estimate the required processing energy per transmitted information bit. Simulation on gate-level show that the energy cost of performing pre-coding and tackling of hardware impairments are low, in the order of few pJ per bit.

[1]  Robert H. Halstead,et al.  Matrix Computations , 2011, Encyclopedia of Parallel Computing.

[2]  Emil Björnson,et al.  Massive MIMO Systems With Non-Ideal Hardware: Energy Efficiency, Estimation, and Capacity Limits , 2013, IEEE Transactions on Information Theory.

[3]  Erik G. Larsson,et al.  Constant-Envelope Multi-User Precoding for Frequency-Selective Massive MIMO Systems , 2013, IEEE Wireless Communications Letters.

[4]  Thomas Kailath,et al.  A Family of New Efficient Arrays for Matrix Multiplication , 1989, IEEE Trans. Computers.

[5]  Fredrik Rusek,et al.  Hardware efficient approximative matrix inversion for linear pre-coding in massive MIMO , 2014, 2014 IEEE International Symposium on Circuits and Systems (ISCAS).

[6]  Fredrik Tufvesson,et al.  Massive MIMO channels — Measurements and models , 2013, 2013 Asilomar Conference on Signals, Systems and Computers.

[7]  Fredrik Rusek,et al.  High throughput constant envelope pre-coder for massive MIMO systems , 2015, 2015 IEEE International Symposium on Circuits and Systems (ISCAS).

[8]  Fredrik Rusek,et al.  A low-complex peak-to-average power reduction scheme for OFDM based massive MIMO systems , 2014, 2014 6th International Symposium on Communications, Control and Signal Processing (ISCCSP).

[9]  Erik G. Larsson,et al.  Scaling Up MIMO: Opportunities and Challenges with Very Large Arrays , 2012, IEEE Signal Process. Mag..

[10]  Israel Koren.  Computer arithmetic algorithms , 1993 .

[11]  Chen-Yi Lee,et al.  An Indexed-Scaling Pipelined FFT Processor for OFDM-Based WPAN Applications , 2008, IEEE Transactions on Circuits and Systems II: Express Briefs.

[12]  Liesbet Van der Perre,et al.  Compensation of IQ imbalance in OFDM systems , 2003, IEEE International Conference on Communications, 2003. ICC '03..

[13]  Michail Matthaiou,et al.  Massive MIMO with IQ Imbalance: Performance analysis and compensation , 2015, 2015 IEEE International Conference on Communications (ICC).