A low complexity antenna selection algorithm for energy efficiency in massive mimo systems
2015 IEEE International Conference on Data Science and Data …, 2015•ieeexplore.ieee.org
Massive multi-input multi-output (MIMO) system adopting large amount of antennas is a
promising technique to improve spectral efficiency and energy efficiency for next generation
wireless communication systems. However, the power consumptions of the radio frequency
(RF) chains can be huge, which poses great challenges. In this paper, we consider the
problem of transmit RF chain configuration with the goal to maximize energy efficiency under
total power constraint in massive MIMO systems. The RF chain configuration can be decided …
promising technique to improve spectral efficiency and energy efficiency for next generation
wireless communication systems. However, the power consumptions of the radio frequency
(RF) chains can be huge, which poses great challenges. In this paper, we consider the
problem of transmit RF chain configuration with the goal to maximize energy efficiency under
total power constraint in massive MIMO systems. The RF chain configuration can be decided …
Massive multi-input multi-output (MIMO) system adopting large amount of antennas is a promising technique to improve spectral efficiency and energy efficiency for next generation wireless communication systems. However, the power consumptions of the radio frequency (RF) chains can be huge, which poses great challenges. In this paper, we consider the problem of transmit RF chain configuration with the goal to maximize energy efficiency under total power constraint in massive MIMO systems. The RF chain configuration can be decided by applying antenna selection methods. Considering the trade-off between performance and complexity, we propose the norm-and-correlation-based selection algorithm with low complexity for energy efficiency maximization. Our selection metric considers the effect of the norm of each channel column and correlation between columns while attaining low computational complexity. Simulation results validate the advantages of the proposed algorithm.
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