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The nonlinearities of power amplifiers in massive MIMO arrays introduce unwanted spectral regrowth, which is typically avoided via digital predistortion at each amplifier. However, as the number of base station antennas scales up, so does the computational burden of per-antenna linearization. This work introduces a neural-network virtual digital predistortion (vDPD) scheme that operates before the linear precoder for OFDM-based massive MU-MIMO systems. By applying predistortion before the precoder, complexity scales primarily with the number of users. We can achieve comparable linearization along the user beams by training our neural network based on the memory polynomial, predistortion-per-antenna approach. We verify our algorithm through an exhaustive simulator that includes high-order amplifier nonlinearities, memory effects, and variance across the amplifier models.
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Reduced Dimension Minimum BER PSK Precoding for Constrained Transmit Signals in Massive MIMO
Recently a number of nonlinear precoding algorithms have been developed for designing a downlink transmit signal that is constrained by some nonlinearity, such as one-bit quantization, power-amplifier saturation or constant modulus. These methods use iterative search algorithms to directly design the signal that is transmitted from each antenna. Since the dimension of the search space equals the number of antennas, the computational complexity of these approaches can be high for massive MIMO scenarios. Thus, in this paper we pose the problem in a smaller dimensional space by constraining the signal prior to the nonlinearity to be the output of a linear precoder. The search is then over the vector of predistorted symbols at the input to the linear precoder, which is typically much smaller than the number of antennas. We focus on algorithms that minimize the bit error rate at the receivers, and show that performance can be obtained that is similar to algorithms that operate directly in the antenna domain.
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- PAR ID:
- 10092571
- Date Published:
- Journal Name:
- 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
- Page Range / eLocation ID:
- 3584 to 3588
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/ICASSP.2018.8461642
