author = {M. Hummert and D. W\"{u}bben and A. Dekorsy},
  year = {2020},
  month = {Feb},
  title = {DeEQ: Deep Equalization for Large MIMO Systems},
  volume = {24},
  URL = {https://cgi.tu-harburg.de/~c00wsa20/index.php},
  address={Hamburg, Germany},
  abstract={Multiple Input Multiple Output (MIMO) and massive
MIMO (mMIMO) are key-enabling technologies for 4G and
5G communications systems. mMIMO uses a high number of
antennas, where the number of antennas at the base station
exceeds in general the number of antennas in the mobiles. For uncorrelated
channels, linear equalizers already achieve promising
performance in the uplink due to the channel hardening effect.
In contrast, we will focus on large symmetrical MIMO systems in
this paper, where many antennas are employed at the transmitter
and the receiver side resulting in a more challenging task for
the receiver. Traditionally, receiver algorithms have been derived
based on models for the communications system. Recently, machine
learning approaches have been proposed where the design is
data driven. In order to overcome the drawbacks of model-based
and pure data driven approaches, hybrid approaches combining
the benefits of both worlds have emerged. In this paper, we
present the novel hybrid approach entitled Deep Equalization
(DeEQ) based on model knowledge and a neural network like
structure. As demonstrated by simulation results, this novel
approach achieves very good performance with the advantage
of only a very low error floor.},
  booktitle={24th International ITG Workshop on Smart Antennas (WSA 2020)}